Abstract. In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined. Keywords: Grouping and segmentation; Figure-ground; Top-down processing; Object classification

Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in di erent color spaces. Therefore, we rst discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc. � then review some major color representation methods and their advantages/disadvantages� nally summarize the color image segmentation techniques using di erent color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics based method are investigated as well.

Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond to actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take and computer resources to handle. Nowadays, color imagery has definitely supplanted monochromatic information and computation power is no longer a limitation in processing large volumes of data. The attention has accordingly been focused in recent years on algorithms for segmentation of color images and various techniques, ofted borrowed from the background of gray-level image segmentation, have been proposed. This paper provides a review of methods advanced in the past few years for segmentation of color images.

In this paper, we cast natural-image segmentation as a problem of clustering texture features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. Unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures in an image. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using either 2D texture filter banks or simple fixed-size windows to obtain texture features, the algorithm effectively segments an image by minimizing the overall coding length of the feature vectors. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm compares favorably against other well-known image-segmentation methods on the Berkeley image database.

Tracking, or camera pose determination, is the main technical challenge in creating augmented realities. Constraining the degree to which the environment may be altered to support tracking heightens the challenge. This paper describes several years of work at the USC Computer Graphics and Immersive Technologies (CGIT) laboratory to develop self-contained, minimally intrusive tracking systems for use in both indoor and outdoor settings. These hybrid-technology tracking systems combine vision and inertial sensing with research in fiducial design, feature detection, motion estimation, recursive filters, and pragmatic engineering to satisfy realistic application requirements.

The aim of this paper is to present dierent algorithms, based on a combination of two structures of graph and of two color image processing methods, in order to segment color images. The structures used in this study are the region adjacency graph and the line graph associated. We will see how these structures can enhance segmentation processes such as region growing or watershed transformation. The principal advantage of these structures is that they give more weight to adjacency relationships between regions than usual methods. Let us note nevertheless that this advantage leads in return to adjust more parameters than other methods to best rene the result of the segmentation. We will show that this adjustment is necessarily image dependant and observer dependant.

A color image segmentation technique is presented for use in coding and/or compression of video-conferencing sequences. The proposed technique utilizes the perceptual HSI (hue,saturation,intensity) color space. The effectiveness of the scheme is improved by first splitting the pixels in the image into chromatic and achromatic regions using a classification method. A region growing scheme is then employed to each of the set of chromatic and achromatic pixels to segment the image. For the achromatic pixels a simple intensity difference metric is used. For the chromatic pixels three distance metrics were compared. Results are shown for three video-conferencing type images. Keywords: color image segmentation, HSI, region growing 1. INTRODUCTION Image analysis usually refers to the processing of images by computers with the goal of finding what objects are presented in the images. Image segmentation refers to partitioning an image into different regions that are homogeneous or "similar" i...

In this paper we study the ability of the cooperation of Bayesian color pixel classification in extracting seeds for color watershed. Using color pixel classification alone does not extract accurately enough color regions so we suggest to use a strategy based on three steps : simplification, Bayesian classification and color watershed. Color watershed is based on an aggregation function using local and global criteria. The strategy is performed on microscopic images. Quantitative measures are used to evaluate the resulting segmentations according to a set of reference images. 1.

Key words: This paper presents a color segmentation algorithm for embedded real-time systems with a special focus on latencies. The algorithm is part of a HardwareSoftware -System that realizes fast reactions on visual stimuli in highly dynamic environments. There is furthermore the constraint to use low-cost hardware to build the system. Our system is implemented on a RoboCup middle size league prototype robot.

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