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Describing surfaces
COMPUTER VISION, GRAPHICS,AND IMAGEPROCESSING30, 368-372 (1985)
Abstracts of Papers Accepted for Publication SPECIAL
PAPERS:
HUMAN
AND
Perception of Transparency in Man and Machine. 97403. Received October 31, 1984.
MACHINE
VISION
JACOBBECK. University of Oregon, Eugene, Oregon
The different tactics employed by human and machine vision systems in judging transparency are compared. Instead of luminance or reflectance (relative luminance), the human visual system uses lightness, a nonlinear function of reflectance, to estimate transparency. The representation of intensity information in terms of lightness restricts the operations that can be applied, and does not permit solving the equations describing the occurrence of transparency. Instead, the human visual system uses algorithms based on simple order and magnitude relations. One consequence of the human visual system not using a mathematically correct procedure is the occurrence of nonveridical perceptions of transparency. A second consequence is that the human visual system is not able to make accurate judgments of the degree of transparency. Figural cues are also important in the human perception of transparency. The tendency for the human visual system to see a simple organization leads to the perception of transparency even when the intensity pattern indicates transparency to be physically impossible. In contrast, given the luminances or reflectances, a machine vision system can apply the relevant equations for additive and subtractive color mixture to give veridical and quantitatively correct judgments of transparency.
Describing Surfaces. MICHAEL BRADY, JEAN PONCE, ALAN YUILLE, AND HARUO ASADA. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139. Received August 30, 1984. This paper continues our work on visual representations of 3-dimensional surfaces. The theoretical component of our work is a study of classes of surface curves as a source of constraint on the surface on which they lie, and as a basis for describing it. We analyze bounding contours, surface intersections, lines of curvature, and asymptotes. Our experimental work investigates whether the information suggested by our theoretical study can be computed reliably and efficiently. We demonstrate algorithms that compute lines of curvature of a (Gaussian smoothed) surface; determine planar patches and umbilic regions; extract axes of surfaces of revolution and tube surfaces. We report preliminary results on adapting the curvature primal sketch algorithms to detect and describe surface intersections.
Connectionist Models and Parallelism in High Level Vision. JEROME A. FELDMAN. University of Rochester, Rochester, New York 14627. Received February 8, 1985. Students of human and machine vision share the belief that massively parallel processing characterizes early vision. For higher levels of visual organization, considerably less is known and there is much less agreement about the best computational view of the processing. This paper lays out a computational framework in which all levels of vision can be naturally carried out in highly parallel fashion. One key is the representation of all visual information needed for high level processing as discrete parameter values which can be represented by units. Two problems that appear to require sequential attention are described and their solutions within the basically parallel structure are presented. Some simple program results are included.
Toward a Theory of the Perceived Spatial Layout of Scenes. RALPH NORMAN HABER. University of Illinois at Chicago, Chicago, Illinois. Received January 7, 1985. 368 0734-189X/85 $3.00 Copyright © 1985 by AcademicPress,Inc. All rightsof reproductionin any formreserved.
Abstracts of Papers Accepted for Publication SPECIAL
PAPERS:
HUMAN
AND
Perception of Transparency in Man and Machine. 97403. Received October 31, 1984.
MACHINE
VISION
JACOBBECK. University of Oregon, Eugene, Oregon
The different tactics employed by human and machine vision systems in judging transparency are compared. Instead of luminance or reflectance (relative luminance), the human visual system uses lightness, a nonlinear function of reflectance, to estimate transparency. The representation of intensity information in terms of lightness restricts the operations that can be applied, and does not permit solving the equations describing the occurrence of transparency. Instead, the human visual system uses algorithms based on simple order and magnitude relations. One consequence of the human visual system not using a mathematically correct procedure is the occurrence of nonveridical perceptions of transparency. A second consequence is that the human visual system is not able to make accurate judgments of the degree of transparency. Figural cues are also important in the human perception of transparency. The tendency for the human visual system to see a simple organization leads to the perception of transparency even when the intensity pattern indicates transparency to be physically impossible. In contrast, given the luminances or reflectances, a machine vision system can apply the relevant equations for additive and subtractive color mixture to give veridical and quantitatively correct judgments of transparency.
Describing Surfaces. MICHAEL BRADY, JEAN PONCE, ALAN YUILLE, AND HARUO ASADA. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139. Received August 30, 1984. This paper continues our work on visual representations of 3-dimensional surfaces. The theoretical component of our work is a study of classes of surface curves as a source of constraint on the surface on which they lie, and as a basis for describing it. We analyze bounding contours, surface intersections, lines of curvature, and asymptotes. Our experimental work investigates whether the information suggested by our theoretical study can be computed reliably and efficiently. We demonstrate algorithms that compute lines of curvature of a (Gaussian smoothed) surface; determine planar patches and umbilic regions; extract axes of surfaces of revolution and tube surfaces. We report preliminary results on adapting the curvature primal sketch algorithms to detect and describe surface intersections.
Connectionist Models and Parallelism in High Level Vision. JEROME A. FELDMAN. University of Rochester, Rochester, New York 14627. Received February 8, 1985. Students of human and machine vision share the belief that massively parallel processing characterizes early vision. For higher levels of visual organization, considerably less is known and there is much less agreement about the best computational view of the processing. This paper lays out a computational framework in which all levels of vision can be naturally carried out in highly parallel fashion. One key is the representation of all visual information needed for high level processing as discrete parameter values which can be represented by units. Two problems that appear to require sequential attention are described and their solutions within the basically parallel structure are presented. Some simple program results are included.
Toward a Theory of the Perceived Spatial Layout of Scenes. RALPH NORMAN HABER. University of Illinois at Chicago, Chicago, Illinois. Received January 7, 1985. 368 0734-189X/85 $3.00 Copyright © 1985 by AcademicPress,Inc. All rightsof reproductionin any formreserved.