Sheng-Jyh Wang

Information Preserving Color Transformation for Protanopia and Deuteranopia

In this letter, we proposed a new recoloring method for people with protanopic and deuteranopic color deficiencies. We present a color transformation that aims to preserve the color information in the original images while maintaining the recolored images as natural as possible. Two error functions are introduced and combined together to form an objective function using the Lagrange multiplier with a user-specified parameter lambda. This objective function is then minimized to obtain the optimal settings. Experimental results show that the proposed method can yield more comprehensible images for color-deficient viewers while maintaining the naturalness of the recolored images for standard viewers.

A Hierarchical Bayesian Generation Framework for Vacant Parking Space Detection

In this paper, from the viewpoint of scene under standing, a three-layer Bayesian hierarchical framework (BHF) is proposed for robust vacant parking space detection. In practice, the challenges of vacant parking space inference come from dramatic luminance variations, shadow effect, perspective distortion, and the inter-occlusion among vehicles. By using a hidden labeling layer between an observation layer and a scene layer, the BHF provides a systematic generative structure to model these variations. In the proposed BHF, the problem of luminance variations is treated as a color classification problem and is tack led via a classification process from the observation layer to the labeling layer, while the occlusion pattern, perspective distortion, and shadow effect are well modeled by the relationships between the scene layer and the labeling layer. With the BHF scheme, the detection of vacant parking spaces and the labeling of scene status are regarded as a unified Bayesian optimization problem subject to a shadow generation model, an occlusion generation model, and an object classification model. The system accuracy was evaluated by using outdoor parking lot videos captured from morning to evening. Experimental results showed that the proposed framework can systematically determine the vacant space number, efficiently label ground and car regions, precisely locate the shadowed regions, and effectively tackle the problem of luminance variations.

Contrast-based color image segmentation

In this letter, we propose a color image segmentation algorithm based on contrast information. Given a color image, we use contrast information, instead of the commonly used derivative information, to detect edges. To fit for humans visual perception, the CIE L/sup */a/sup */b/sup */ color space is used and the /spl Delta/E/sub ab/ color difference is adopted as the measure of color contrast. A subjective experiment is made to demonstrate the weak correlation between the perceived color contrast and the levels of (L/sup */,a/sup */,b/sup */). This experiment implies the feasibility of using a single-threshold scheme to suppress perceptually faint boundaries. A complete segmentation scheme is proposed and the simulation results demonstrate the superiority of this approach in providing reasonable and reliable color image segmentation.

Image contrast enhancement based on intensity-pair distribution

Current contrast enhancement algorithms sometimes come with undesired drawbacks, like the loss of tiny details, enhancement of image noise, occasional over-enhancement, and unnatural look of the processed images. In this paper, we propose a new approach for contrast enhancement based on the use of a so-called intensity-pair distribution. This distribution possesses both local information and global information of the image content. By analyzing the content of intensity-pair distribution, a set of expansion forces are generated for contrast enhancement while another set of anti-expansion forces are generated to suppress image noise. To avoid over-enhancement and preserve the natural look of the processed images, a magnitude mapping function is also proposed. Experimental results show that the proposed algorithm does provide a flexible and reliable way for contrast enhancement.

The use of visible color difference in the quantitative evaluation of color image segmentation

In this paper, we propose the use of visible color difference in a new quantitative evaluation scheme for color image segmentation. With visible color difference, two measurements, named intra-region visual error and inter-region visual error, are defined to evaluate the quality of segmentation results. To fit human visual perception, segmentation results with an excessive amount of intra-region error are regarded as under-segmentation, while segmentation results with too many inter-region errors are regarded as over-segmentation. Based on these two measurements, a complete scheme for the evaluation of color image segmentation is proposed. The simulation results demonstrate that this new scheme may provide a reliable and efficient way to automatically select the parameter settings for a given segmentation algorithm and to compare the performance between various segmentation algorithms.

An Efficient Approach for the Calibration of Multiple PTZ Cameras

In this paper, we propose an efficient approach to infer the relative positioning and orientation among multiple pan-tilt-zoom (PTZ) cameras. In this approach, the tilt angle and altitude of each PTZ camera are estimated first based on the observation of some simple objects lying on a horizontal plane. With the estimated tilt angles and altitudes, the calibration of multiple cameras can be easily accomplished by comparing the back-projected world coordinates of some common vectors in 3-D space. This calibration method does not require particular system setup or specific calibration patterns. The sensitivity analysis with respect to parameter fluctuations and measurement errors is also discussed. Experiment results over real images have demonstrated the efficiency and feasibility of this approach. Note to Practitioners-This paper was motivated by the problem that pan-tilt-zoom (PTZ) cameras may change their poses from time to time to achieve better monitoring results. Whenever there is a change, we need to recalibrate the extrinsic parameters again. In this paper, we demonstrate a new and efficient approach to calibrate multiple PTZ cameras. The concept of our approach originated from the observation that people could usually make a rough estimate about the tilt angle of the camera simply based on some clues revealed in the captured images. Based on our approach, we can simply use some A4 papers on a table to calibrate multiple PTZ cameras. In our approach, there is no need to calculate the commonly used homography matrix. For real cases, once a set of PTZ cameras is settled, we can simply place a few simple patterns on a horizontal plane. These patterns can be A4 papers, books, boxes, etc.; and the horizontal plane can be a tabletop or the ground plane. The whole procedure does not need specially designed calibration patterns and requires only a light computational load. In the near future, we will work on the extension of the proposed approach so that we will be able to perform dynamic calibration when the PTZ cameras are under movement

A Bayesian hierarchical detection framework for parking space detection

In this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter- occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.

Image recolorization for the colorblind

In this paper, we propose a new re-coloring algorithm to enhance the accessibility for the color vision deficient (or colorblind). Compared to people with normal color vision, people with color vision deficiency (CVD) have difficulty in distinguishing between certain combinations of colors. This may hinder visual communication owing to the increasing use of colors in recent years. To address this problem, we re-color the image to preserve visual detail when perceived by people with CVD. We first extract the representing colors in an image. Then we find the optimal mapping to maintain the contrast between each pair of these representing colors. The proposed algorithm is image content dependent and completely automatic. Experimental results on natural images are illustrated to demonstrate the effectiveness of the proposed re-coloring algorithm.

Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework

In this paper, we propose a vacant parking space detection system that operates day and night. In the daytime, the major challenges of the system include dramatic lighting variations, shadow effect, inter-object occlusion, and perspective distortion. In the nighttime, the major challenges include insufficient illumination and complicated lighting conditions. To overcome these problems, we propose a plane-based method which adopts a structural 3-D parking lot model consisting of plentiful planar surfaces. The plane-based 3-D scene model plays a key part in handling inter-object occlusion and perspective distortion. On the other hand, to alleviate the interference of unpredictable lighting changes and shadows, we propose a plane-based classification process. Moreover, by introducing a Bayesian hierarchical framework to integrate the 3-D model with the plane-based classification process, we systematically infer the parking status. Last, to overcome the insufficient illumination in the nighttime, we also introduce a preprocessing step to enhance image quality. The experimental results show that the proposed framework can achieve robust detection of vacant parking spaces in both daytime and nighttime.

Efficient Vision-Based Calibration for Visual Surveillance Systems with Multiple PTZ Cameras

Surveillance systems with multiple cameras have become increasingly important. The space relationship between cameras offers useful information for surveillance applications, such as object tracking or 3- D positioning. In this paper, we propose a visionbased approach to infer the relative positioning and orientation among multiple PTZ cameras. The relationship between the tilt angle of a PTZ camera and the 3D-to-2D coordinate transformation is built first. Then, the tilt angles and the altitudes of PTZ cameras are estimated based on the observation of some simple objects lying on a horizontal plane, with known intrinsic parameters of these PTZ cameras. With the estimated tilt angles and altitudes, the calibration of multiple cameras can be accomplished by comparing the back-projected world coordinates of a common vector in the 3-D space. The whole procedure does not need a huge amount of data and the computational load is light. Experimental results over real images have demonstrated the efficiency and feasibility of this approach.