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Filtering, Segmentation and Depth (Lecture Notes in Computer Science)

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Computer vision seeks a process that starts with a noisy, ambiguous signal from a TV camera and ends with a high-level description of discrete objects located in 3-dimensional space and identified in a human classification. In this book we address this process at several levels. We first treat the low-level image-processing issues of noise removal and smoothing while preserving important lines and singularities in an image. At a slightly higher level, we describe a robust contour tracing algorithm that produces a cartoon of the important lines in the image. Finally, we begin the high-level task of reconstructing the geometry of objects in the scene.

The problems in computer vision are so interrelated that to solve one we must solve them all. This book manages to make serious progress at several levels of visual recon struction by working in a restricted world of simple pictures of simple objects. We use a model that represents a scene as a group of overlapping shapes corresponding to the projections of objects. In constructing this representation from an image, the algorithm must imitate the process in human perception of inferring contours and surfaces that are occluded or are not present in the luminance function. Consequently, the work depends strongly on what we know about the psychology of perception, especially from the Gestalt school and its heirs.

We define the problem in this way: to find a decomposition of the domain D of an image that has the fewest disrupted edges-junctions of edges, crack tips, corners, and cusps-by creating suitable continuations for the disrupted edges. The result is a decomposition of D into overlapping regions R\ U ... U Rn ordered by occlusion, called the 2.1-D Sketch. Chapters 2 through 5 describe algorithms that have been implemented in the С language for a SUN workstation running Unix1 and X-Windows, using a library of computer vision functions called HVision. The programs from this book are available via anonymous FTP from internet host math .harvard. edu, in the directory vision.

If computer vision is to have a hope of serious long-term progress in the engineering sense, we must adopt the tradition from the numerical analysis community of sharing computer code. In this way, the next generation of results is built upon the combined best parts of the previous generation.

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