In this paper, we present an automatic vision-based traffic sign recognition system, which can detect and classify traffic signs at long distance under different lighting conditions. To realize this purpose, the traffic sign recognition is developed in an originally proposed dual-focal active camera system. In this system, a telephoto camera is equipped as an assistant of a wide angle camera. The telephoto camera can capture a high accuracy image for an object of interest in the view field of the wide angle camera. The image from the telephoto camera provides enough information for recognition when the accuracy of traffic sign is low from the wide angle camera. In the proposed system, the traffic sign detection and classification are processed separately for different images from the wide angle camera and telephoto camera. Besides, in order to detect traffic sign from complex background in different lighting conditions, we propose a type of color transformation which is invariant to light changing. This color transformation is conducted to highlight the pattern of traffic signs by reducing the complexity of background. Based on the color transformation, a multi-resolution detector with cascade mode is trained and used to locate traffic signs at low resolution in the image from the wide angle camera. After detection, the system actively captures a high accuracy image of each detected traffic sign by controlling the direction and exposure time of the telephoto camera based on the information from the wide angle camera. Moreover, in classification, a hierarchical classifier is constructed and used to recognize the detected traffic signs in the high accuracy image from the telephoto camera. Finally, based on the proposed system, a set of experiments in the domain of traffic sign recognition is presented. The experimental results demonstrate that the proposed system can effectively recognize traffic signs at low resolution in different lighting conditions.

A new recoloring method to improve visibility of indiscriminable colors for protanopes or deuteranopes is proposed. In the proposed method, yellow-blue components of a color image perceived by protanopes/deuteranopes are adequately modified. Moreover, the gamut mapping is considered to obtain proper output color values in this method.

Usually, paintings are more appealing than photographic images. This is because paintings can incorporate styles based on the artist's subjective view of motif. This style can be distinguished by looking at elements such as motif, color, shape deformation and brush texture. In our work, we focus on the effect of "color" element and devise a method for transforming the color of an input photograph according to a reference painting. To do this, we consider basic color category concepts in the color transformation process. We assume that color transformations from one basic color category to another may cause peculiar feelings. Therefore, we restrict each color transformation within the same basic color category. For this, our algorithm first categorizes each pixel color of a photograph into one of eleven basic color categories. Next, for every pixel color of the photograph, the algorithm finds its corresponding color in the same category of a reference painting. Finally, the algorithm substitutes the pixel color with its corresponding color. In this way, we achieve large but natural color transformations of an image.

This paper proposes a unified coding algorithm for the lossless and near-lossless compression of still color images. The proposed unified color image coding scheme can control the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image while the level of distortion on the RGB plane is suppressed to within a preset magnitude. In order to control the PSNR, the distortion level is adaptively changed at each pixel. An adaptive quantizer to control the distortion is designed on the basis of psychovisual criteria. Finally, experiments on Super High Definition (SHD) images show the effectiveness of the proposed algorithm.

This paper describes a unified coding algorithm for lossless and near-lossless color image compression that exploits the correlations between RGB signals. A reversible color transform that removes the correlations between RGB signals while avoiding any finite word length limitation is proposed for the lossless case. The resulting algorithm gives higher performance than the lossless JPEG without the color transform. Next, the lossless algorithm is extended to a unified coding algorithm of lossless and near-lossless compression schemes that can control the level of the reconstruction error on the RGB plane from 0 to p, where p is a certain small non-negative integer. The effectiveness of this algorithm was demonstrated experimentally.

This paper proposes an object oriented face region detection and tracking method using range color information. Range segmentation of the objects are obtained from the complicated background using disparity histogram (DH). The facial regions among the range segmented objects are detected using skin-color transform technique that provides a facial region enhanced gray-level image. Computationally efficient matching pixel count (MPC) disparity measure is introduced to enhance the matching accuracy by removing the effect of the unexpected noise in the boundary region. Redundancy operations inherent in the area-based matching operation are removed to enhance the processing speed. For the skin-color transformation, the generalized facial color distribution (GFCD) is modeled by 2D Gaussian function in a normalized color space. Disparity difference histogram (DDH) concept from two consecutive frames is introduced to estimate the range information effectively. Detailed geometrical analysis provides exact variation of range information of moving object. The experimental results show that the proposed algorithm works well in various environments, at a rate of 1 frame per second with 512 480 resolution in general purpose workstation.

In this paper, a new traffic sign detection algorithm and a symbol recognition algorithm are proposed. For a traffic sign detection, a dominant color transform is introduced, which serves as a tool of highlighting a dominant primary color, while discarding the other two primary colors. For a symbol recognition, the curvilinear shape distribution on a circle centered on the centroid of the symbol, called a circular pattern vector, is used as a spatial feature of the symbol. The circular pattern vector is invariant to scaling, translation, and rotation. As simulation results, the effectiveness of traffic sign detection and recognition algorithms are confirmed.