A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods

This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.

Research on human infants has begun to shed light on early-developing processes for segmenting perceptual arrays into objects. Infants appear to perceive obiects by analyzing three-dlmensional surface arrangements and motions. Their per-ception does not accord with a general tendency to maximize figural goodness or to attend to nonaccldentol geometric relations in visual arrays. Object perceptlan does accord with principles governing the motions of material bodies: Infants divide perceptual arrays into units that move as connected wholes, that move separately from one another, that tend to maintain their size ond shape over motion, and that tend to act upon each other only on contact. These findings sug-gest that a general representation of obiect unity and boundaries is interposed between representations of surfaces and representations of objects of famlllor kinds. The processes that construct this representotion may be related to pro-cesses of physical reasonlng. This article is animated by two proposals about perception and perceptual development. One proposal is substantive: In situations where perception develops through experience, but without instruction or deliberate reflection, development tends to enrich perceptual abilities but not to change them fundamentally. The second proposal is methodological: In the above situations, studies of the origins and early development of perception can shed light on perception in its mature state. These proposals will arise from a discussion of the early development of one perceptual ability: the ability to organize arrays of surfaces into unitary, bounded, and persisting objects. PERCEMNG OBJECTS In recent years, my colleagues and I have been studying young infants ’ perception of objects in complex displays in which objects are adjacent to other objects, objects are partly hidden behind other objects, or objects move fully Preparation of this article was supported by grants from NIH (I-ID-132r18) and NSF (BNS 06082). I am grateful to Carol Krumhansl, Doug Medin, and Herb Pick for penetrating com-ments on an earlier &aft of this manuscript. Correspondence and rquests for reprints should be sent to Elizabeth S. Spelke, Cornell

AbstractÐThe ThemeRiver visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time line andcorresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time line, selected thematic content, and thematic strength are indicated by the river's directed flow, composition, and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored ªcurrentsº flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme. Index TermsÐVisualization, metaphor, trendanalysis, time line. 1

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We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than use an edge-based approach to represent the segmentation of a scene, we propose a multi-layer estimation framework which uses support maps to represent the segmentation of the image into homogeneous chunks. This support-based approach can represent objects that are split into disjoint regions, or have surfaces that are transparently interleaved. Our framework is based on an extension of robust estimation methods which provide a theoretical basis for supportbased estimation. The Minimum Description Length principle is used to decide how many support maps to use in describing a particular image. We show results applying this framework to heterogeneous interpolation and segmentation tasks on range and motion imagery. 1 Introduction Real-world perceptual systems must deal with complicated and cluttered environments. To succeed in such environments, a system must be able to r...

. Existing methods for grouping edges on the basis of local smoothness measures fail to compute complete contours in natural images: it appears that a stronger global constraint is required. Motivated by growing evidence that the human visual system exploits contour closure for the purposes of perceptual grouping [6, 7, 14, 15, 25], we present an algorithm for computing highly closed bounding contours from images. Unlike previous algorithms [11, 18, 26], no restrictions are placed on the type of structure bounded or its shape. Contours are represented locally by tangent vectors, augmented by image intensity estimates. A Bayesian model is developed for the likelihood that two tangent vectors form contiguous components of the same contour. Based on this model, a sparsely-connected graph is constructed, and the problem of computing closed contours is posed as the computation of shortest-path cycles in this graph. We show that simple tangent cycles can be efficiently computed ...

Human face detection has always been an important problem for face, expression and gesture recognition. Though numerous attempts have been made to detect and localize faces, these approaches have made assumptions that restrict their extension to more general cases. We identify that the key factor in a generic and robust system is that of using a large amount of image evidence, related and reinforced by model knowledge through a probabilistic framework. In this paper, we propose a featurebased algorithm for detecting faces that is sufficiently generic and is also easily extensible to cope with more demanding variations of the imaging conditions. The algorithm detects feature points from the image using spatial filters and groups them into face candidates using geometric and gray level constraints. A probabilistic framework is then used to reinforce probabilities and to evaluate the likelihood of the candidate as a face. We provide results to support the validity of the approach and demo...

We study image segmentation on the basis of locally excitatory globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a potential for each oscillator so that only those oscillators with strong connections from their neighborhood can develop high potentials. Based on the concept of potential, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions, without affecting those corresponding to major regions. We show analytically that the resulting oscillator network separates an image into several major regions, plus a background consisting of all noisy regions, and illustrate network properties by computer simulation. The network exhibits a natural capacity in segmenting images. The oscillatory dynamics leads to a computer algorithm, which is applied successfully to segmenting real graylevel images. A number of issues regarding biological plausibility and perceptual organization are discussed. We argue that LEGION provides a novel and effective framework for image segmentation and figure-ground segregation. DeLiang Wang and David Terman Image Segmentation 1.

We present a new method for completing missing parts caused by the removal of foreground or background elements from an image. Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method iteratively approximates the unknown regions and composites adaptive image fragments into the image. Values of an inverse matte are used to compute a confidence map and a level set that direct an incremental traversal within the unknown area from high to low confidence. In each step, guided by a fast smooth approximation, an image fragment is selected from the most similar and frequent examples. As the selected fragments are composited, their likelihood increases along with the mean confidence of the image, until reaching a complete image. We demonstrate our method by completion of photographs and paintings.