This paper describes a new robust regular polygon detector. The regular polygon transform is posed as a mixture of regular polygons in a five dimensional space. Given the edge structure of an image, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a mixture of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. The remaining dimensions may be efficiently recovered subsequently using maximum likelihood at the locations of the most likely polygons in the subspace. This leads to an efficient algorithm. Also the a posteriori formulation facilitates inclusion of additional a priori information leading to real-time application to road sign detection. The use of gradient information also reduces noise compared to existing approaches such as the generalised Hough transform. Results are presented for images with noise to show stability. The detector is also applied to two separate applications: real-time road sign detection for on-line driver assistance; and feature detection, recovering stable features in rectilinear environments. 1

Moving objects tracking is an important problem in many applications such as video-surveillance. Monitoring systems can be improved using vision-based techniques able to extract and classify objects in the scene. However, problems arise due to unexpected shadows because shadow detection is critical for accurate objects detection in video stream, since shadow points are often misclassified as object points causing errors in localization, segmentation, measurements, tracking and classification of moving objects. The paper presents a new approach for removing shadows from moving objects, starting from a frame-difference method using a gray-level textured adaptive background. The shadow detection scheme uses photometric properties and the notion of shadow as semi-transparent region which retains a reduced-contrast representation of the underlying surface pattern and texture. We analyze the problem of representing texture information in terms of redundant systems of functions for texture identification. The method for discriminating shadows from moving objects is based on a Pursuit scheme using an over-complete dictionary. The basic idea is to use the simple but powerful Matching Pursuit algorithm (MP) for representing texture as linear combination of elements of a big set of functions. Particularly, MP selects the best little set of atoms of 2D Gabor dictionary for features selection representative of properties of the texture in the image. Experimental results validate the algorithm’s performance.

Abstract. Traffic sign recognition is a difficult task if we aim at detecting and recognizing signs in images captured from unfavorable environments. Complex background, weather, shadow, and other lighting-related problems may make it difficult to detect and recognize signs in the rural as well as the urban areas. We employ discrete cosine transform and singular value decomposition for extracting features that defy external disturbances, and compare different designs of detection and classification systems for the task. Experimental results show that our pilot systems offer satisfactory performance when tested with very challenging data. 1

Abstract---Traffic sign recognition usually consists of two parts: detection and classification. In this paper we describe the classification stage using ring partitioned method. In this method, first the RGB image is converted into gray scale image using color thresholding and histogram specification technique. This gray scale image, called as specified gray scale image is invariant to the illumination changes. Then the image is classified using ring partitioned method. The image is divided by several concentric areas like rings. In every ring the histogram is used as an image descriptor. The matching process is done by computing the histogram distances for all rings of the images by introducing the weights for every ring. The method doesn’t need a lot of samples of sign images for training process, alternatively only the standard sign images are used as the reference images. The experimental results show the effectiveness of the method in the matching of occluded, rotated, and illumination problems of traffic sign images. I.

Abstract—The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. Index Terms—Dissociated dipoles, ensemble of dichotomizers, Error-Correcting Output Code (ECOC), evolutionary boosting, traffic sign recognition. I.

To improve traffic safety, an automatic traffic sign detection system would be important to assist the driver. In this paper, an approach for detecting Norwegian speed limit signs is proposed. It consists of three major steps: Color-based filtering, locating sign(s) in an image and detection of numbers on the sign. About 91 % correct recognition is achieved for a selection of 198 images. Ongoing work is focused on video input analysis.

In this paper an approach for detecting Norwegian speed limit signs is proposed. It consists of three major steps: Color-based filtering, locating sign(s) in an image and detection of numbers on the sign. Very good results are obtained for the two first steps and for the third one work is still in progress.

Considering the problem of automatic traffic signs recognition in natural scene image (mainly including three kinds of traffic signs: yellow warning signs, red prohibition signs and blue mandatory signs), a new method for traffic signs recognition based on central projection transformation is proposed in this paper. In this method, self-adaptive image segmentation is firstly used to extract binary inner images of detected traffic signs after they are detected from natural scene images. Secondly, one-dimensional feature vectors of inner images are computed by central projection transformation. Lastly, these vectors are input to the trained probabilistic neural networks (PNN) for exact classification, the output of PNN is final recognition result. The new method is applied to 221 natural scene images taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a recognition rate of over 98%. Especially, the problem of confirming optimal projection number in central projection transformation is solved by the information entropy in this paper. Moreover, the proposed recognition method is compared with other recognition methods based on three kinds of invariant moments. Results of contrastive experiments also show that the method proposed in this paper is effective and reliable. 1.