On the Role of Structure in Vision

Witkin, Andrew; Tenenbaum, Jay M.

doi:10.1016/b978-0-12-084320-6.50022-0

Cited by 276 publications

(167 citation statements)

References 26 publications

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“…However, any definition of simplicity has depended entirely on the language that is used for description, and no single language has been found to encompass the range of grouping phenomena. Greater success has been achieved by basing the analysis of perceptual organization on a functional theory which assumes that the purpose of perceptual organization is to detect stable image groupings that reflect actual structure of the scene rather than accidental properties [30]. This parallels other areas of early vision in which a major goal is to identify image features that are stable under changes in imaging conditions.…”

Section: Perceptual Organizationmentioning

confidence: 97%

Three-dimensional object recognition from single two-dimensional images

Lowe

1987

Artificial Intelligence

1,148

561

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A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision.

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Section: Perceptual Organizationmentioning

confidence: 97%

Three-dimensional object recognition from single two-dimensional images

Lowe

1987

Artificial Intelligence

1,148

561

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“…In contrast are those accounts which emphasize the sufficiency of an edge-based (or contour) representation of an object (e.g., Ullman, 1984;Biederman, 1987;Witkin & Tenenbaum, 1983). One recent account, recognition by components (RBC) (Biederman, 1987), assumes that the image is segmented at regions of sharp concavity into simple, convex volumetric primitives such as blocks, cones, wedges, and cylinders.…”

Section: Theoretical Significancementioning

confidence: 99%

Surface versus edge-based determinants of visual recognition

Biederman

1988

Cognitive Psychology

607

434

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Two roles hypothesized for surface characteristics, such as color, brightness, and texture, in object recognition are that such information can (a) define the gradients needed for a 2%-D sketch so that a 3-D representation can be derived (e.g., Marr & Nishihara, 1978) and (b) provide additional distinctive features for accessing memory. In a series of five experiments, subjects either named or verified (against a target name) brief (50-100 ms) presentations of slides of common objects. Each object was shown in two versions: professionally photographed in full color or as a simplified line drawing showing only the object's major components (which typically corresponded to its parts). Although one or the other type of picture would be slightly favored in a particular condition of exposure (duration or masking), overall mean reaction times and error rates were virtually identical for the two types of stimuli. These results support a view that edge-based representations mediate real-time object recognition in contrast to surface gradient or multiple cue representations. A previously unexplored distinction of color diagnosticity allowed us to determine whether color (and brightness) was employed as an additional feature in accessing memory for those objects or conditions where there might have been an advantage for the color slides. For some objects, e.g., banana, fork, fish, and camera, color is diagnostic as to the object's classification. For other objects, e.g., chair, pen, mitten, and bicycle pump, color is not diagnostic, as such objects can be of any color. If color was employed in accessing memory, color-diagnostic objects should have shown a relative advantage when presented as color slides compared to the line drawing versions of the same objects. Also, this advantage would be magnified when subjects could anticipate the color of an object in the verification task, particularly on NO trials when the foil was of a different color. Neither an overall advantage for color-diagnostic objects when presented in color nor a magnification of a relative advantage on the NO trials in the verification task was obtained. Although differences in surface characteristics such as color, brightness, and texture can be instrumental in defining edges and can provide cues for visual search, they play only a secondary role in the real-time recognition of an intact object when its edges can be readily extracted.

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“…Regularities and structure in the world serve to constrain the forms of knowledge representation schernes we need to consider ( Witkin & Tenenbaum, 1983;Bobick & Richards, 1986). In particuiar, as suggested in (Feldman, 1992; Richards & Jepson, 1995;Richards et al, 1996), natural categories of world structure can be described by placing qualitative probabilities over a discrete set of "modaln processes operating at different spatiotemporai scales.…”

Section: Designing Computational Perceiversmentioning

confidence: 99%

The Computational Perception of Scene Dynamics

Mann

Jepson

Siskind

1997

Computer Vision and Image Understanding

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CanadaThe author has ganted a nonexclusive licence allowing the National Library of Canada to reproduce, loan, distribute or selI copies of this thesis in microform, paper or electronic formats.The author retains ownership of the copyright in this thesis. Neither the thesis nor substantial extracts fkom it may be printed or othelurise reproduced without the author's permission.L'auteur a accordé une licence non exclusive permettant à la BibIiotheque nationale du Canada de reproduire, prêter' distribuer ou vendre des copies de cette thèse sous la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique.L'auteur conserve la propriété du droit d'auteur qui protège cette thèse. Ni la thèse ni des extraits substantiels de celle-ci ne doivent être imprimés -ou autrement reproduits sans son autorisation. AbstractThe Computational Perception of Scene Dynamics Richard Mann Doctor of Philosophy Graduate Depart ment of Computer ScienceUniversity of Toronto 1998Understanding observations of image sequences requires one to reason about qualitative scene dynamics. For example, on observing a hand lifting a cup, we may infer that an 'active' hand is applying an upwards force (by graspingj on a 'passive' cup. In order to perform such reasoning, we require an ontology that describes object properties and the generation and transfer of forces in the scene. Such an ontology should include, for example: the presence of gravity, the presence of a ground plane, whether objects are 'active' or 'passive', whether objects are contacting and/or attached to other objects, and so on. In this work we make these ideas precise by presenting an implemented computational system that derives symbolic force-dynamic descriptions fÎom video sequences.Our approach to scene dynamics i s based on an analysis of the Newtonian mechanics of a simpmed scene model. The critical requirement is that, @en image sequences, we can obtain estimates for the shape and motion of the objects in the scene. To do this, we assume that the objects can be approximated by a two-dimensional 'layered' scene model. The input to our system consists of a set of polygonal outlines dong with estimates for their velocities and acceIerations, obtained kom a view-based tracker. Given such input, we prment a system t hat ext rac ts force-dynamic descriptions for the image sequence. We provide computat ional examples to demonstrate that our ontology is sufiiciently rich to describe a wide variety of image sequences. aThis work rnakes three central contributions. First, we provide an ontology suitable for describing object properties and the generation and transfer of forces in the scene. Second. we provide a computational procedure to test the feasibiLity of such interpretations by reducing the problem to a feasibility test in linear progrnmming. Finally, we provide a theory of preference orderhg between muitipIe interpretations dong with an efficient computational procedure to determine maximal element s in such orderings AcknowledgementsStudying in the Compute...

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On the Role of Structure in Vision

Cited by 276 publications

References 26 publications

Three-dimensional object recognition from single two-dimensional images

Three-dimensional object recognition from single two-dimensional images

Surface versus edge-based determinants of visual recognition

The Computational Perception of Scene Dynamics

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