In this paper, we present anapproach to colorimage understandingthat accountsforcolorvariationsdue to highlights and shading. We demonstrate that the reflected light from every point on a dielectric object. such as plastic, can be described asa linearcombination of the object color and the highlight color. The colors of all light rays reflected from one object then form a planar cluster in the color space.The shapeof this cluster is determined by the object and highlight colors and by the object shape and illumination geometry. We present a method that exploits the difference between object color and highlight color to separate the color of every pixel into a matte component and a highlight component.This generates two intrinsic images, one showing the scene without highlights, and the other one showing only the highlights. The intrinsic images may be a useful tool for a variety of algorithms in computer vision. such as stereo vision, motion analysis, shape from shading,and shapefrom highlights. Ourmethod combines the analysis of matte and highlight reflection with a sensor model that accounts for camera limitations. This enables us to successfully run our algorithm on real images taken in a laboratory setting. We show and discuss the results.

Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research.

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Current methods for image segmentation are confused by artifacts such as highlights, because they are not based on any physical model of these phenomena. In this paper, we present an approach to color image understanding that accounts for color variations due to highlights and shading. Based on the physics of reflection by dielectric materials, such as plastic, we show that the color of every pixel from an object can be described as a linear combination of the object color and the highlight color. According to this model, all color pixels from one object form a planar cluster in the color space whose shape is determined by the object and highlight colors and by the object shape and illumination geometry. We present a method which exploits the color difference between object color and highlight color, as exhibited in the cluster shape, to separate the color of every pixel into a matte component and a highlight component. This generates two intrinsic images, one showing the scene without highlights, and the other one showing only the highlights. The intrinsic images may be a useful tool for a variety of algorithms in computer vision that cannot detect or analyze highlights, such as stereo vision, motion analysis, shape from shading, and shape from highlights. We have applied this method to real images in a laboratory environment, and we show these results and discuss some of the pragmatic issues endemic to precision color imaging.

For natural language dialog systems, going beyond domain independence means the attempt to create a core system that can serve as a basis for interfaces to various application classes that differ not only with respect to the domain of discourse but also with respect to dialog type, user type, intended system behavior, and background system. In the design and implementation of HAM-ANS, which is presently operational as an interface to an expert system, a vision system and a data base system, we have shown that going beyond domain independence is possible. HAM-ANS is a large natural language dialog system with both considerable depth and breadth, which accepts typed input in colloquial German and produces typed German responses quickly enough to make it practical for realtime interaction. One goal of this paper is to report on the lessons learned during the realization of the system HAM-ANS. This paper introduces the overall structure of the system's processing units and knowledge sources. In addition we describe some of the innovative features concerning the strategy of semantic interpretation. I.

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Abstract. The integration of vision and natural language processing increasingly attracts attention in different areas of AI research. Up to now, however, there have only been a few attempts at connecting vision systems with natural language access systems. Within the SFB 314, special collaborative program on AI and knowledge-based systems, the automatic natural language description of real world image sequencesconstitutes a major research goal, which has been pursued during the last ten years. The aim of our approach is to obtain an incremental evaluation and simultaneous description of the perceived time-varying scenes. In this contribution we will report on new results of our joint efforts at combining the natural language access system VITRA with a vision system. We have investigated the problem of describing the movements of articulated bodies in image sequences within an integrated natural language and computer vision system. The paper will focus on our model-based approach for the recognition of pedestrians and on the further evaluation and language production in VITRA. 1

Abstract—In this paper, a continuous valued measure for local color symmetry is introduced. The new algorithm is an extension of the successful gray value-based symmetry map proposed by Reisfeld et al. The use of color facilitates the detection of focus points (FPs) on objects that are difficult to detect using gray-value contrast only. The detection of FPs is aimed at guiding the attention of an object recognition system; therefore, FPs have to fulfill three major requirements: stability, distinctiveness, and usability. The proposed algorithm is evaluated for these criteria and compared with the gray value-based symmetry measure and two other methods from the literature. Stability is tested against noise, object rotation, and variations of lighting. As a measure for the distinctiveness of FPs, the principal components of FP-centered windows are compared with those of windows at randomly chosen points on a large database of natural images. Finally, usability is evaluated in the context of an object recognition task. Index Terms—Focus-of-attention, color vision, symmetry, saliency maps, object recognition. æ 1

The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibration

In this paper, we present an approach to color image understanding that can be used to segment and analyze surfaces with color variations due to highlights and shading. We begin with a theory that relates the reflected light from dielectric materials, such as plastic, to fundamental physical reflection processes, and describes the color of the reflected light as a linear combination of the color of the light due to surface reflection (highlights) and body reflection (object color). This theory is used in an algorithm that separates a color image into two parts: an image of just the highlights, and the original image with the highlights removed. In the past, we have applied this method to hand-segmented images. The current paper shows how to perform automatic segmentation method by applying this theory in stages to identify the object and highlight colors. The result is a combination of segmentation and reflection analysis that is better than traditional heuristic segmentation methods (such as histogram thresholding), and provides important physical information about the surface geometry and material properties at the same time. We also show the importance of modeling the camera properties for this kind of quantitative analysis of color. This line of research can lead to physics-based image segmentation methods that are both more reliable and more useful than traditional segmentation methods. 1 1.