Hasan Fleyeh

Color detection and segmentation for road and traffic signs

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

Shadow And Highlight Invariant Colour Segmentation Algorithm For Traffic Signs

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

Invariant Road Sign Recognition with Fuzzy ARTMAP and Zernike Moments

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 36times36 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.

Support vector machines for traffic signs recognition

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.

Traffic sign recognition by fuzzy sets

A novel fuzzy approach developed to recognize traffic signs is presented in this paper. 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.

Segmentation of low quality fingerprint images

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.

Traffic sign classification using invariant features and Support Vector Machines

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.

A Robust Model for Traffic Signs Recognition Based on Support Vector Machines

Road and traffic sign recognition has been of great interest for many years. This paper presents an approach to recognize Swedish road and traffic signs by using support vector machines. We focus on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compare and analyze the performances of the SVM recognition model using different feature representations and different kernels and SVM types through recognizing 350 traffic sign shapes and 250 speed limit signs. Experiments have shown excellent results, which have achieved 100% accuracy on sign shapes classification and 99% accuracy on speed limit signs classification.

Traffic sign detection based on AdaBoost color segmentation and SVM classification

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.

Detection and classification of speed limit traffic signs

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.