Analysis and interpretation of range images
Computer vision researchers have been frustrated in their attempts to automatically derive depth information from conventional two-dimensional intensity images. Research on "shape from texture," "shape from shading," and "shape from focus" is still in a laboratory stage and had not seen much use in commercial machine vision systems. A range image or a depth map contains explicit information about the distance from the sensor to the object surfaces within the field of view in the scene. Information about "surface geometry" which is important for, say, three-dimensional object recognition is more easily extracted from "2 1/2 D" range images than from "2D" intensity images. As a result, both active sensors such as laser range finders and passive techniques such as multi-camera stereo vision are being increasingly utilized by vision researchers to solve a variety of problems. This book contains chapters written by distinguished computer vision researchers covering the following areas: Overview of 3D Vision Range Sensing Geometric Processing Object Recognition Navigation Inspection Multisensor Fusion A workshop report, written by the editors, also appears in the book. It summarizes the state of the art and proposes future research directions in range image sensing, processing, interpretation, and applications. The book also contains an extensive, up-to-date bibliography on the above topics. This book provides a unique perspective on the problem of three-dimensional sensing and processing; it is the only comprehensive collection of papers devoted to range images. Both academic researchers interested in research issues in 3D vision and industrial engineers in search of solutions to particular problems will find this a useful reference book.
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1988 NSF Range linage Understanding Workshop
A RuleBased Approach to Binocular Stereopsis
Geometric Signal Processing
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applications approach approximation B-splines basis functions binocular fusion boundary camera chain code channel computer vision constraints contour segments control matrix coordinates corresponding data points defined described detection discussed disparity early processing edge elevation map entities Equation estimate example extracted features extracted figural continuity fusion Gaussian curvature geometric signal processing given global gray level hypothesis image data implementation knot vector left image lines of curvature locus algorithm machine perception matcher matching algorithm measurement methods mobile robots navigation noise object recognition obstacles orientation pair parameters pixel plane primitives principal curvatures problem procedure range data range image range sensor regions represented resolution right image robust robust statistics rule-based scene search window second fundamental form Section sensed shape shown in Figure smooth space curve specific stereopsis straight line features superquadric surface normal tangent techniques terrain representation tion transformation umbilic umbilic points visual volumetric model zero-crossing contours