Perceptual Grouping in Computer Vision                      (Computer and Information Science)
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
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Perceptual grouping is the phenomenon of putting items in               the visual field together, or of "organizing" image data.  It is                an "intermediate" level process in vision.  Such a process of                   organizing image data requires the use of rules and constraints                 defined in terms of the image plane entities, such as the Gestalt               laws of organization.  These constaints are those that are                      invariant under the projection process.  The problems of                        perceptual grouping in the context of computer vision then are                  (i) To identify important intrinsic properties of the image plane               tokens and important image plane relationships among them, and                  (ii) To devise a computational framework in which these                         properties and their image plane relationships can be identified                and larger units can be constructed from these tokens and groups                of tokens.  The rules to detect the organization need to be                     completely stated in terms of intrinsic properties of tokens                    being grouped and their image plane relationships.                                      This research is aimed at studying the grouping phenomenon              from a computational viewpoint for patterns containing                          "generalized" tokens.  These tokens can be a subset of dots, line               segments, curves, and globular or elongated blobs with arbitrary                shapes.  Our approach primarily involves the use of the Voronoi                 tessellation (and its dual Delaunay graph) as the underlying                    geometric representation of the spatial distribution of tokens in               the scene.  A grouping built upon this representation will                      enforce non-local Gestalt constraints such as alignment of tokens               along smooth curves to resolve ambiguous situations.  This                      grouping process integrates the information from different                      sources each of which detects groupings independently based on                  token properties such as orientation or size.                                           The algorithms developed in this project will be tested on              real images and the usefulness of these grouping algorithms will                be assessed in such areas as gap filling in object boundaries,                  segmenting homogeneous textured regions, and computing optical                  flow in dynamic images.
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{'Code': '684000', 'Text': 'ROBOTICS'}
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