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  • 1.
    Aghanavesi, Somayeh
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Memedi, Mevludin
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests2019Konferansepaper (Fagfellevurdert)
  • 2.
    Biswas, Rubel
    et al.
    BRAC Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh..
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Mostakim, Moin
    BRAC Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh..
    Detection and classification of speed limit traffic signs2014Inngår i: 2014 World Congree on Computer Applications and Information Systems (WCCAIS), 2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel traffic sign recognition system which can aid in the development of Intelligent Speed Adaptation. This system is based on extracting the speed limit sign from the traffic scene by Circular Hough Transform (CHT) with the aid of colour and non-colour information of the traffic sign. The digits of the speed limit sign are then extracted and classified using SVM classifier which is trained for this purpose. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 270 images which were collected in different light conditions. To check the robustness of this system, it was tested against 210 images which contain 213 speed limit traffic sign and 288 Non-Speed limit signs. It was found that the accuracy of recognition was 98% which indicates clearly the high robustness targeted by this system.

  • 3. Davami, Erfan
    et al.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Classification with NormalBoost2011Inngår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, nr 2, s. 187-208Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. Since NormalBoost assumes the dataset to be continuous, it is also noise resistant because it only deals with the means and standard deviations of each dimension. Experiments conducted to evaluate its performance shows that NormalBoost performs almost the same as AdaBoost in the classification rate. However, NormalBoost performs 189 times faster than AdaBoost and employs a very little amount of memory when a dataset of 2 million samples with 50 dimensions is invoked.

  • 4.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    A novel fuzzy approach for shape determination of traffic signs2005Inngår i: Second Indian International Conference on Arificial Intelligence, Pune, India, 2005Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, a novel fuzzy approach is developed to determine the shape of traffic signs. More than 1600 images of traffic signs were collected in different light conditions by a digital camera mounted in a car and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV colour space. The fuzzy rules are used to extract the colours of the road signs. Objects in each segmented image are labelled and tested for the presence of probable sign. All small objects under certain threshold are discarded, and the remaining objects are tested by a fuzzy shape recognizer which invokes another set of fuzzy rules. Four shape measures are used to decide the shape of the sign; rectangularity, triangularity, ellipticity, and the new shape measure octagonality.

  • 5.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Color detection and segmentation for road and traffic signs2004Inngår i: IEEE conference on Cybernetics and Intelligent Systems, Singapore, 2004Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

  • 6.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Road and Traffic Sign Color Detection and Segmentation - A Fuzzy Approach2005Inngår i: Machine Vision Applications (MVA2005), Tsukuba Science City, 2005Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new algorithm for color detection and segmentation of road signs based on fuzzy sets. The images were taken by a digital camera mounted in a car. The RGB image was converted into HSV color space, and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space. The fuzzy rules are used to extract the colors of the road signs. The method was tested on outdoor images in different light conditions, and it was tested on color images from different European countries and it showed high robustness. This project is part of the research conducted by Dalarna University-Sweden and Napier University Edinburgh-Scotland in the field of the Intelligent Transport Systems (ITS).

  • 7.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Segmentation and enhancement of low quality fingerprint images2016Inngår i: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part II, China - Shanghai: Springer, 2016, Vol. 10042, s. 371-384Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new approach to segment low quality finger-print images which are collected by low quality fingerprint scanners. Images collected using such readers are easy to collect but difficult to segment. The proposed approach focuses on automatically segment and enhance these fingerprint images to reduce the detection of false minutiae and hence improve the recognition rate. There are four major contributions of this paper. Firstly, segmentation of fingerprint images is achieved via morphological filters to find the largest object in the image which is the foreground of the fingerprint. Secondly, specially designed adaptive thresholding algorithm to deal with fingerprint images. The algorithm tries to fit a curve between the gray levels of the pixels of each row or column in the fingerprint image. The curve represents the binarization threshold of each pixel in the corresponding row or column. Thirdly, noise reduction and ridge enhancement is achieved by invoking a rotational invariant anisotropic diffusion filter. Finally, an adaptive thinning algorithm which is immune against spurs is invoked to generate the recognition ready fingerprint image. Segmentation of 100 images from databases FVC2002 and FVC2004 was performed and the experiments showed that 96 % of images under test are correctly segmented.

  • 8.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Shadow and highlight invariant colour segmentation algorithm for traffic signs2006Inngår i: 2006 IEEE Conference on cybernetics and intelligent systems, vol.1 and 2, 2006Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Shadows and highlights represent a challenge to the computer vision researchers due to a variance in the brightness on the surfaces of the objects under consideration. This paper presents a new colour detection and segmentation algorithm for road signs in which the effect of shadows and highlights are neglected to get better colour segmentation results. Images are taken by a digital camera mounted in a car. The RGB images are converted into HSV colour space and the shadow-highlight invariant method is applied to extract the colours of the road signs under shadow and highlight conditions. The method is tested on hundreds of outdoor images under such light conditions, and it shows high robustness; more than 95% of correct segmentation is achieved.

  • 9.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic and Road Sign Recognition2008Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm. Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%. Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

  • 10.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic Sign detection and recognition2017Inngår i: Computer Vision and Imaging in Intelligent Transportation Systems, John Wiley & Sons, 2017, 1, s. 343-374Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    This chapter presents an overview of traffic sign detection and recognition. It describes the characteristics of traffic signs and the requirements and difficulties when dealing with traffic sign detection and recognition in outdoor images. The chapter also covers the different techniques invoked to segment traffic signs from the different traffic scenes and the techniques employed for the recognition and classification of traffic signs. It points many problems regarding the stability of the received colour information, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. It also proposes an adaptive colour segmentation model based on Neural Networks. The chapter demonstrates the way to classify segmented traffic signs by employing one of widely used classifiers, AdaBoost , based on a set of features, in this case HOG descriptors, which was developed for pedestrian recognition but found the way for many applications in different fields. The chapter ends by showing examples where traffic sign recognition is applicable in vehicle industry

  • 11.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic sign recognition by fuzzy sets2008Inngår i: Intelligent Vehicles Symposium, 2008 IEEE, Eindhoven, 2008Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, a novel fuzzy approach is developed to recognize traffic signs. More than 3400 images of traffic signs were collected in different light conditions by a digital camera mounted in a car and used for developing and testing this approach. Every RGB image was converted into HSV color space and segmented by using a set of fuzzy rules depending on the hue and saturation values of each pixel. Objects in each segmented image are labeled and tested for the presence of probable sign. Objects passed this test are recognized by a fuzzy shape recognizer which invokes another set of fuzzy rules. These fuzzy rules are based on four invariant shape measures which are invoked to decide the shape of the sign; rectangularity, triangularity, ellipticity, and the new shape measure octagonality. The method is tested in different environmental conditions and it shows high robustness.

  • 12.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic Sign Recognition: visions and systems2010Bok (Annet vitenskapelig)
  • 13.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic sign recognition without color information2015Inngår i: Colour and Visual Computing Symposium (CVCS), 2015 / [ed] Pedersen, M; Thomas, JB, IEEE conference proceedings, 2015, s. 1-6Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same. This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients HOG descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate. The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs which represent a challenge to the proposed approach.

  • 14.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic sign recognition without color information2015Rapport (Annet vitenskapelig)
    Abstract [en]

    Color represents an important attribute in the field of traffic sign recognition. However, when the color of the traffic sign fades or the traffic scene is collected in gray as in the case of Infrared imaging, then color based recognition systems fail. Other problems related to color are simply that different countries use different colors. Even within the European Union, colors of traffic signs are not the same.

    This paper aims to present a new approach to detect traffic signs without color attributes. It is based a two-stage sliding window which detects traffic signs in the multi-scale image. Histogram of Oriented Gradients (HOG) descriptors are computed as a quality function which are evaluated by two SVM classifier; the coarse and the fine detectors. 

    Different objects detected by the coarse detectors are clustered and a fine search is conducted in the areas where traffic signs are more probable to exist. 

    Experiments conducted to detect traffic signs under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 98% and very low false positive rate.  The proposed approach was tested on the Yield traffic signs because it has a simple triangular shape which can be found in many places other than the traffic signs and represent a challenge to the proposed approach.

  • 15.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic Signs Color Detection and Segmentation in Poor Light Conditions2005Inngår i: Machine Vision Applications (MVA2005), Tsukuba Science City, 2005Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new algorithm for color detection and segmentation of road signs in poor light conditions. The images were taken by a digital camera mounted in a car. The RGB channels of the digital images were enhanced separately by histogram equalization, and then a color constancy algorithm was applied to extract the true colors of the sign. The resultant image was then converted into HSV color space, and segmented to extract the colors of the road signs. The method was tested on outdoor images in different poor light conditions such as fog and snow, and they show high robustness. This project is part of the research taking place at Dalarna University - Sweden in the field of the Intelligent Transport Systems (ITS).

  • 16.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Bhuiyan, Nizam
    Biswas, Rubel
    Prohibitory traffic signs detection using LVQ and windowed Hough transform2011Inngår i: IICAI-11 (5 th Indian International Conference on Artificial Intelligence), Tumkur, India, 2011Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Prohibitory traffic signs represent an important group of traffic signs which are used to prohibit certain types of manoeuvres or some types of traffic. Speed limits signs belong to this group and speed is the main cause of many deadly accidents. Detecting this group in good time may be helpful to avoid many fatal accidents. This paper presents a new approach to detecting prohibitory traffic signs which is based on colour segmentation using LVQ and windowed Hough Transform. Experiments conducted to check the robustness of this approach indicated that 98.5% of the traffic signs invoked for this test were successfully detected. This test was carried out using images collected under a wide range of environmental conditions.

  • 17.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Bin Mumtaz, Al Hasanat
    Adaptive Shadow and Highlight Invariant Colour Segmentation for Traffic Sign Recognition Based on Kohonen SOM2011Inngår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, nr 1, s. 15-31Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper describes an intelligent algorithm for traffic sign recognition which converges quickly, is accurate in its segmentation and adaptive in its behaviour. The proposed approach can segment images of traffic signs in different lighting and environmental conditions and in different countries. It is based on using Kohonen's Self-Organizing Maps (SOM) as a clustering tool and it is developed for Intelligent Vehicle applications. The current approach does not need any prior training. Instead, a slight portion, which is about 1% of the image under investigation, is used for training. This is a key issue to ensure fast convergence and high adaptability. The current approach was tested by using 442 images which were collected under different environmental conditions and from different countries. The proposed approach shows promising results; good improvement of 73% is observed in faded traffic sign images compared with 53.3% using the traditional algorithm. The adaptability of the system is evident from the segmentation of the traffic sign images from various countries where the result is 96% for the nine countries included in the test.

  • 18.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Biswas, Rubel
    Bhuiyan, Nizam
    An adaptive approach to detect warning traffic signs using som and windowed hough transform2011Inngår i: IASTED, Krete, 2011, s. 195-202Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Warning traffic signs represent an important group of traffic signs which indicate danger for road users. Detecting this group in good time may be helpful to avoid many fatal accidents. This paper presents a new approach to detecting warning traffic signs which is based on color segmentation using Self Organizing Maps and windowed Hough Transform. The proposed system is a standalone and adaptive which means that it works without any kind of training. This is due to the fact that color segmentation using SOM employs 1% of the image under investigation for the training and Hough transform is invoked to detect the shape of this group of traffic signs. Experiments conducted to check the robustness of this approach indicated that 95.6% of the traffic signs invoked for this test were successfully detected. This test was carried out under a wide range of environmental conditions and in different European countries.

  • 19.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Biswas, Rubel
    Davami, Erfan
    Traffic sign detection based on AdaBoost color segmentation and SVM classification2013Inngår i: Eurocon 2013: IEEE Conference Publications / [ed] IEEE, 2013, s. 2005-2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper aims to present a new approach to detect traffic signs which is based on color segmentation using AdaBoost binary classifier and circular Hough Transform.The Adaboost classifier was trained to segment traffic signs images according to the desired color. A voting mechanism was invoked to establish a property curve for each of the candidates. SVM classifier was trained to classify the property curves of each object into their corresponding classes.

    Experiments conducted on Adaboost color segmentation under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 95%. The proposed system was tested on two different groups of traffic signs; the warning and the prohibitory signs. In the case of warning signs, a recognition rate of 98.4% was achieved while it was 97% for prohibitory traffic signs. This test was carried out under a wide range of environmental conditions.

  • 20.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Classification with NormalBoost- Case Study Traffic Sign Classification2012Inngår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, nr 1, s. 25-43Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    NormalBoost is a new boosting algorithm which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. The purpose of this paper is to present NormalBoost as a framework which establishes a platform to solve classification problems. The approach was tested with a dataset which was extracted automatically from real-world traffic sign images. The dataset contains both images of traffic sign borders and speed limit pictograms. This framework involves the computation of Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which were classified was 6500 binary images. A -fold validation was invoked to check the validity of the classification which resulted in a classification rate of 98.4% and 98.9% being achieved for these two databases. This framework is distinguished by its invariance to in-plane rotation of the images under consideration. Experiments show that the classification rate remains almost constant when traffic sign images with different angles of rotations were tested.

  • 21.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Eigen Based Traffic Sign Recognition Which Aids In Achieving Intelligent Speed Adaptation2011Inngår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, nr 2, s. 129-145Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Speed is one of the major factors by which the traffic safety is affected. If the speed limit traffic signs on the road are recognised and displayed to a driver, this will be a motivation to keep the vehicle's speed within the permitted range. The purpose of this paper is to investigate Eigen-based traffic sign recognition which can aid in the development of Intelligent Speed Adaptation. This system is based on invoking the PCA technique to detect the unknown speed limit traffic sign and computes its best effective Eigen vectors. The traffic sign is then recognized and classified by using the shortest Euclidean distance to the different speed limit traffic sign classes. The system was trained using 24 037 images which were collected in different light conditions. To check the robustness of this system, it was tested against 1429 images and it was found that the accuracy of recognition was 97.5% which indicates clearly the high robustness targeted by this system.

  • 22.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Eigen-based traffic sign recognition2011Inngår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 5, nr 3, s. 190-196Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper’s purpose is to introduce Eigen-based traffic sign recognition. This technique is based on invoking the PCA algorithm to choose the most effective components of traffic sign images to classify an unknown traffic sign. A set of weights are computed from the most effective Eigen vectors of the traffic sign. By using the Euclidean distance, unknown traffic sign images are then classified. The approach was tested on two different databases of traffic sign’s borders and speed limit pictograms which were extracted automatically from real-world images. A classification rate of 96.8% and 97.9% was achieved for these two databases. To check the robustness of this approach, non-traffic sign objects and occluded signs were invoked. A performance of 71% was achieved when occluded signs are used. When signs were rotated 10 degrees around their centre, the performance became 89% when traffic signs’ outer shapes were used and for rotated speed limit pictograms the result was 80%.

  • 23.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    University of Central Florida.
    Multiclass Adaboost Based on an Ensemble of Binary Adaboosts2013Inngår i: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 3, nr 2, s. 57-70Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.

  • 24.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Segmentation of fingerprint images based on bi-level processing using fuzzy rules2012Inngår i: Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American, 2012, s. 1-6Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest object in the image. On the local level, fingerprint's foreground and its border image are treated using different fuzzy rules which the two images are segmented. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages; preprocessing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.

  • 25.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Road and traffic sign detection and recognition2005Inngår i: 10th EWGT Meeting and 16th Mini-EURO Conference, Poznan, Poland, 2005Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents an overview of the road and traffic sign detection and recognition. It describes the characteristics of the road signs, the requirements and difficulties behind road signs detection and recognition, how to deal with outdoor images, and the different techniques used in the image segmentation based on the colour analysis, shape analysis. It shows also the techniques used for the recognition and classification of the road signs. Although image processing plays a central role in the road signs recognition, especially in colour analysis, but the paper points to many problems regarding the stability of the received information of colours, variations of these colours with respect to the daylight conditions, and absence of a colour model that can led to a good solution. This means that there is a lot of work to be done in the field, and a lot of improvement can be achieved. Neural networks were widely used in the detection and the recognition of the road signs. The majority of the authors used neural networks as a recognizer, and as classifier. Some other techniques such as template matching or classical classifiers were also used. New techniques should be involved to increase the robustness, and to get faster systems for real-time applications.

  • 26.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    SVM based traffic sign classification using legender moments2007Inngår i: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007, 2007, s. 957-968Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel approach to recognize traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36×36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition. Copyright © 2007 IICAI.

  • 27.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    SVM Based Traffic Sign Classification Using Legendre Moments2007Inngår i: Third Indian International Conference on Artificial Intelligence, Pune, India, 2007Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel approach to recognise traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are colour segmented and all objects which represent signs are extracted and normalised to 36x36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition.

  • 28.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Traffic sign classification using invariant features and support vector machines2008Inngår i: Intelligent Vehicles Symposium, 2008 IEEE, 2008, Vol. 1-3, s. 530-535Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a novel approach to recognize traffic signs using invariant features and support vector machines (SVM). Images of traffic signs are collected by a digital camera mounted in a vehicle. They are color segmented and all objects which represent signs are extracted and normalized to 36 x 36 pixels images. Invariant features of sign rims and speed-limit sign interiors of 350 and 250 images are computed and the SVM classifier is trained with these features. Two stages of SVM are trained; the first stage determines the shape of sign rim and the second determines the pictogram of the sign. Training and testing of both SVM classifiers are done using still images. The best performance achieved is 98% for sign rims and 93% for speed limit signs.

  • 29.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Aenugula, Dinesh
    Baddam, Sruthi
    Invariant road sign recognition with fuzzy ARTMAP and zernike moments2007Inngår i: 2007 IEEE Intelligent Vehicles Symposium, vols 1-3, 2007, Vol. 1-3, s. 1-6Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, a novel approach to recognize road signs is developed. Images of road signs are collected by a digital camera mounted in a vehicle. They are segmented using colour information and all objects which represent signs are extracted, normalized to 36x36 pixels, and used to train a Fuzzy ARTMAP neural network by calculating Zernike moments for these objects as features. Sign borders and pictograms are investigated in this study. Zernike moments of sign borders and speed-limit signs of 210 and 150 images are calculated as features. A fuzzy ARTMAP is trained directly with features, or by using PCA for dimension reduction, or by using LDA algorithm as dimension reduction and data separation algorithm. Two Fuzzy ARTMAP Neural Networks are trained. The first NN determines the class of the sign from the shape of its border and the second one determines the sign itself from its pictogram. Training and testing of both NNs is done offline by using still images. In the online mode, the system loads the Fuzzy ARTMAP Neural Networks, and performs recognition process. An accuracy of about 99% is achieved in sign border recognition and 96% for Speed-Limit recognition.

  • 30.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Gilani, Syed
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Road sign detection and recognition using fuzzy artmap: a case study Swedish speed-limit signs2006Inngår i: The 10th IASTED International Conference on Artificial Intelligence and Soft Computing, Palma de Mallorca, Spain, 2006Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, a novel approach is developed using Fuzzy ARTMAP Neural Networks to recognize and classify Swedish road and traffic signs. The Swedish Speed-Limit signs are selected as a case study, but the system can be applied to other signs. A new color detection and segmentation algorithm is presented in which the effects of shadows and highlights are eliminated. Images are taken by a digital camera mounted in a car. Segmented images are created by converting RGB images into HSV color space and applying the shadow-highlight invariant method. The method is tested on hundreds of outdoor images under shadow and highlight conditions, and it shows high robustness; in 95% of cases of correct segmentation is achieved. Classification is carried out by two stages of Fuzzy ARTMAP which are trained by 210 and 150 images, respectively. The first stage determines the border of the sign and the second stage determines the pictogram. Training and testing of both stages are made offline, using still images. In online mode, the system loads the Fuzzy ARTMAP and performs recognition process. An accuracy of 96.7% is achieved in Speed-Limit recognition and more than 90% as whole accuracy.

  • 31.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Segmentation of low quality fingerprint images2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a new algorithm to segment fingerprint images. The algorithm uses four features, the global mean, the local mean, variance and coherence of the image to achieve the fingerprint segmentation. Based on these features, a rule based system is built to segment the image. The proposed algorithm is implemented in three stages; pre-processing, segmentation, and post-processing. Gaussian filter and histogram equalization are applied in the pre-processing stage. Segmentation is applied using the local features. Finally, fill the gaps algorithm and a modified version of Otsu thresholding are invoked in the post-processing stage. In order to evaluate the performance of this method, experiments are performed on FVC2000 DB1. Segmentation of 100 images is performed and compared with manual examinations of human experts. It shows that the proposed algorithm achieves a correct segmentation of 82% of images under test.

  • 32.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Segmentation of fingerprint images based on bi-level combination of global and local processing2012Inngår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, nr 2, s. 97-120Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint imageswhich are collected by low quality fingerprint readers. Images collected using such readersare easy to collect but difficult to segment. The proposed approach is based on combiningglobal and local processing to achieve segmentation of fingerprint images. On the globallevel, the fingerprint is located and extracted from the rest of the image by using a globalthresholding followed by dilation and edge detection of the largest object in the image.On the local level, fingerprint’s foreground and its border image are treated using differentfuzzy rules. These rules are based on the mean and variance of the block under consideration.The approach is implemented in three stages: pre-processing, segmentation, andpost-processing.Segmentation of 100 images was performed and compared with manual examinationsby human experts. The experiments showed that 96% of images under test are correctlysegmented. The results from the quality of segmentation test revealed that the averageerror in block segmentation was 2.84% and the false positive and false negatives wereapproximately 1.4%. This indicates the high robustness of the proposed approach.

  • 33.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Khan, Taha
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Pattern matching approach towards real-time traffic sign recognition2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper addresses the problem of traffic sign recognition in real-time conditions. The algorithm presented in this paper is based on detecting traffic signs in life images and videos using pattern matching of the unknown sign’s shape with standard shapes of the traffic signs. The pattern matching algorithm works with shape vertices rather than the whole image. This reduces the computation time which is a crucial factor to fit real-time demands. The algorithm is translation and scaling invariant. It shows high robustness as it is tested with 500 images and several videos and a recognition rate of 97% is achieved.

  • 34.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Mohammed, Iman
    Night time vehicle detection2012Inngår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, nr 2, s. 143-165Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Night driving is one of the major factors which affects traffic safety. Althoughdetecting oncoming vehicles at night time is a challenging task, it may improve trafficsafety. If the oncoming vehicle is recognised in good time, this will motivate drivers tokeep their eyes on the road. The purpose of this paper is to present an approach to detectvehicles at night based on the employment of a single onboard camera. This system isbased on detecting vehicle headlights by recognising their shapes via an SVM classifierwhich was trained for this purpose. A pairing algorithm was designed to pair vehicleheadlights to ensure that the two headlights belong to the same vehicle. A multi-objecttracking algorithm was invoked to track the vehicle throughout the time the vehicle isin the scene. The system was trained with 503 single objects and tested using 144 587single objects which were extracted from 1410 frames collected from 15 videos and 27moving vehicles. It was found that the accuracy of recognition was 97.9% and the vehiclerecognition rate was 96.3% which indicates clearly the high robustness attained by thissystem.

  • 35.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Roch, Janina
    TU Kaiserslautern, Kaiserslautern, Germany.
    Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs2013Rapport (Annet vitenskapelig)
    Abstract [en]

    The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.

      HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

  • 36.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Shi, Min
    Heifeng, Wu
    A Robust Model for Traffic Signs Recognition Based on Support Vector Machines2008Inngår i: CISP'2008, Hainan, China, 2008Konferansepaper (Fagfellevurdert)
  • 37.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Shi, Min
    Wu, Haifeng
    Support vector machines for traffic signs recognition2008Inngår i: IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence)., Hong Kong, 2008, s. 3820-3827Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing the data to the SVM for training and test. We compared and analyzed the performances of the SVM recognition model using different feature representations and different kernels and SVM types. Our experimental data sets consisted of 350 traffic sign shapes and 250 speed limit signs. Experimental results have shown excellent results, which have achieved 100% accuracy on sign shapes classification and 99% accuracy on speed limit signs classification. The performance of SVM model highly depends on the choice of model parameters. Two search algorithms, grid search and simulated annealing search have been implemented to improve the performances of our classification model. The SVM model were also shown to be more effective than Fuzzy ARTMAP model.

  • 38.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Extracting Body Landmarks from Videos for Parkinson Gait Analysis2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients’ gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body’s center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object’s movement plots show high level of accuracy in the calculation of the body landmarks.

  • 39.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Hansson, Karl
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Feature selection and bleach time modelling of paper pulp using tree based learners2016Inngår i: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I / [ed] Wojciech CellaryMohamed F. MokbelJianmin WangHua WangRui ZhouYanchun Zhang, China - Shanghai: Springer, 2016, Vol. 10042, s. 385-396Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Paper manufacturing is energy demanding and improvedmodelling of the pulp bleach process is the main non-invasive means ofreducing energy costs. In this paper, time it takes to bleach paper pulpto desired brightness is examined. The model currently used is analysedand benchmarked against two machine learning models (Random Forestand TreeBoost). Results suggests that the current model can be super-seded by the machine learning models and it does not use the optimalcompact subset of features. Despite the differences between the machinelearning models, a feature ranking correlation has been observed for thenew models. One novel, yet unused, feature that both machine learningmodels found to be important is the concentration of bleach agent.

  • 40.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Zhao, Ping
    A contour-based separation of vertically attached traffic signs2008Inngår i: 34th Annual Conference of the IEEE Industrial Electronics Society, vols 1-5, proceedings, 2008, Vol. 1-5, s. 1747-1752Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a contour-based approach to separate vertically attached traffic signs. The algorithm is based on using binary images which are generated by any color segmentation algorithm to represent objects which could be candidate traffic signs. Since all traffic signs are similar about their vertical axis, an improved cross-correlation algorithm is invoked to determine this similarity and filters traffic sign candidates. Shape decomposition is used to smooth the contour of the candidate object iteratively in order to reduce white noise. Flipping point detection algorithm which locates black noise along the smoothed contour and the curve prediction algorithm are invoked to determine the final cut points. A separation accuracy of 94% is achieved by the algorithm. In this experiment more that 70000 images of different traffic sign combinations are invoked to achieve this result. The algorithm is tested on one-sign images, two-sign images, and three-sign images which are combined together for the purpose of testing this algorithm.

  • 41.
    Hansson, Karl
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Machine Learning Algorithms in Heavy Process Manufacturing2016Inngår i: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, nr 1, s. 1-13Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

  • 42.
    Jayaram, M. A.
    et al.
    Siddaganga Instistute of Technology.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Soft computing in biometrics: a pragmatic appraisal2013Inngår i: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 3, nr 3, s. 105-112Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The ever increasing spurt in digital crimes such as image manipulation, image tampering, signature forgery, image forgery, illegal transaction, etc. have hard pressed the demand to combat these forms of criminal activities. In this direction, biometrics - the computer-based validation of a persons' identity is becoming more and more essential particularly for high security systems. The essence of biometrics is the measurement of person’s physiological or behavioral characteristics, it enables authentication of a person’s identity. Biometric-based authentication is also becoming increasingly important in computer-based applications because the amount of sensitive data stored in such systems is growing. The new demands of biometric systems are robustness, high recognition rates, capability to handle imprecision, uncertainties of non-statistical kind and magnanimous flexibility. It is exactly here that, the role of soft computing techniques comes to play. The main aim of this write-up is to present a pragmatic view on applications of soft computing techniques in biometrics and to analyze its impact. It is found that soft computing has already made inroads in terms of individual methods or in combination. Applications of varieties of neural networks top the list followed by fuzzy logic and evolutionary algorithms. In a nutshell, the soft computing paradigms are used for biometric tasks such as feature extraction, dimensionality reduction, pattern identification, pattern mapping and the like.

  • 43.
    Jayaram, M.A.
    et al.
    Siddaganga Institute of Technology.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Convex Hulls in Image Processing: A Scoping Review2016Inngår i: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, nr 2, s. 48-58Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The demands of image processing related systems are robustness, high recognition rates, capability to handle incomplete digital information, and magnanimous flexibility in capturing shape of an object in an image. It is exactly here that, the role of convex hulls comes to play. The objective of this paper is twofold. First, we summarize the state of the art in computational convex hull development for researchers interested in using convex hull image processing to build their intuition, or generate nontrivial models. Secondly, we present several applications involving convex hulls in image processing related tasks. By this, we have striven to show researchers the rich and varied set of applications they can contribute to. This paper also makes a humble effort to enthuse prospective researchers in this area. We hope that the resulting awareness will result in new advances for specific image recognition applications.

  • 44.
    Jayaram, M.A.
    et al.
    Siddaganga Institute of Technology, Tumakuru, Karnataka, India.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Whither Edge Computing? – A Futuristic Review2018Inngår i: International Journal of Applied Research on Information Technology and Computing, ISSN 0975-8070, Vol. 9, nr 2, s. 180-188Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It is a well-known fact that the current day Internet is increasingly becoming laden with content that is bandwidth demanding due to ever-increasing number of things getting attached on a day-in and day-out basis. Hand-in-hand, mobile networks and data networks are converging into cloud computing bandwagon. Edge computing as a promising feature has already made inroads to face future requirements and to address exponential demands from cloud. This feature is all about inserting computing power and storage in the vicinity of the network edge. It is asserted that this scheme of operation brings down the data transport time, quick response times and increased availability. Edge computing brings bandwidthintensive content and latency-sensitive applications closer to the user or data source. In this paper, we explain the drivers of edge computing and have delved on various types of edge computing currently available and going to throng in near future. This paper is intended to draw a comprehensive picture of what is happening in edge currently and what would happen in the near foreseeable future.

  • 45.
    Li, Yujiao
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning2018Inngår i: 2018 International Conference on Computer and Applications, ICCA 2018, 2018, s. 4-11, artikkel-id 8460277Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexiconbased approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets’ sentiment polarity which has been rarely conducted.  

  • 46.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Parking Occupancy Detection Using Thermal Camera2019Inngår i: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, 2019, s. 483-490Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Parking a vehicle is a daunting task during peak hours. The search for a parking space leads to congestion and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. This paper aims to identify parking occupancy in an open parking lot which consists of free parking spaces using a thermal camera. A thermal camera is capable of detecting vehicles in any weather and light conditions based on emitted heat and it can also be installed in public places with less restrictions. However, a thermal camera is expensive compared to a colour camera. A thermal camera can detect vehicles based on the emitted heat without any illumination. Vehicles appear bright or dark based on heat emitted by the vehicles. In order to identify vehicles, pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented in this paper. The detection rates of the detectors reduced with diminishing of heat in the vehicles. Modified Faster RCNN deep learning network produced better detection results compared to other detectors. However, the detection rates can further be improved with larger and diverse training dataset.

  • 47.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Smart parking sensors, technologies and applications for open parking lots: a review2018Inngår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, nr 8, s. 735-741Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

  • 48.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Smart Parking Tools Suitability for Open Parking Lots: A Review2018Inngår i: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, s. 600-609Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

  • 49.
    Paidi, Vijay
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Nyberg, Roger G.
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera2019Inngår i: Artikkel i tidsskrift (Annet vitenskapelig)
    Abstract [en]

    Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

    Fulltekst tilgjengelig fra 2019-10-31 21:26
  • 50.
    Zhang, Fan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    A review on electricity price forecasting using neural network based models2018Rapport (Annet (populærvitenskap, debatt, mm))
12 1 - 50 of 52
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