Extraction of early perceptual structure in dot patterns: Integrating region, boundary, and component gestalt

Abstract This paper presents a computational approach to extracting basic perceptual structure, or the lowest level grouping in dot patterns. The goal is to extract the perceptual segments of dots that group together because of their relative locations. The dots are interpreted as belonging to the interior or the border of a perceptual segment, or being along a perceived curve, or being isolated. To perform the lowest level grouping, first the geometric structure of the dot pattern is represented in terms of certain geometric properties of the Voronoi neighborhoods of the dots. The grouping is accomplished through independent modules that posses narrow expertise for recognition of typical interior dots, border dots, curve dots, and isolated dots, from the properties of the Voronoi neighborhoods. The results of the modules are allowed to influence and change each other so as to result in perceptual components that satisfy global, Gestalt criteria such as border and curve smoothness and component compactness. Such latera; communication among the modules makes feasible a perceptual interpretation of the local structure in a manner that best meets the global expectations. Thus, an integration is performed of multiple constraints, active at different perceptual levels and having different scopes in the dot pattern, to infer the lowest level perceptual structure. The local interpretations as well as lateral corrections are performed through constraint propagation using a probabilistic relaxation process. The result is a partitioning of the dot pattern into different perceptual segments or tokens. Unlike dots, these segments posses size and shape properties in addition to locations.

[1]  L. Glass,et al.  Pattern Recognition in Humans: Correlations Which Cannot be Perceived , 1976, Perception.

[2]  Michael Ian Shamos,et al.  Closest-point problems , 1975, 16th Annual Symposium on Foundations of Computer Science (sfcs 1975).

[3]  Narendra Ahuja,et al.  Extracting Perceptual Structure in Dot Patterns: An Integrated Approach , 1987 .

[4]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Samir I. Shaheen,et al.  A Modular Computer Vision System for Picture Segmentation and Interpretation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  W. Uttal,et al.  On the detectability of straight lines in visual noise: An extension of French’s paradigm into the millisecond domain , 1970 .

[7]  P. Wintz,et al.  A boundary finding algorithm and its applications , 1975 .

[8]  John Fairfield,et al.  Segmenting Dot Patterns by Voronoi Diagram Concavity , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  R. Pérez,et al.  Perception of Random Dot Interference Patterns , 1973, Nature.

[10]  Jaakko Hintikka,et al.  On the Logic of Perception , 1969 .

[11]  Roderick Urquhart,et al.  Graph theoretical clustering based on limited neighbourhood sets , 1982, Pattern Recognit..

[12]  Masahiko Yachida,et al.  Application of color information to visual perception , 1971, Pattern Recognit..

[13]  Steven W. Zucker,et al.  Early orientation selection: Tangent fields and the dimensionality of their support , 1985, Comput. Vis. Graph. Image Process..

[14]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[15]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[16]  John F. O'Callaghan,et al.  An Alternative Definition for "Neighborhood of a Point" , 1975, IEEE Transactions on Computers.

[17]  Anil K. Jain,et al.  Clustering techniques: The user's dilemma , 1976, Pattern Recognit..

[18]  Der-Tsai Lee.  Proximity and reachability in the plane. , 1978 .

[19]  Flavio R. Dias Velasco.  A method for the analysis of Gaussian-like clusters , 1980, Pattern Recognit..

[20]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[21]  M. Wertheimer.  Laws of organization in perceptual forms. , 1938 .

[22]  O. Reiser,et al.  Principles Of Gestalt Psychology , 1936 .

[23]  Steven W. Zucker,et al.  Region growing: Childhood and adolescence* , 1976 .

[24]  Godfried T. Toussaint,et al.  The relative neighbourhood graph of a finite planar set , 1980, Pattern Recognit..

[25]  C. Hofsten,et al.  Spatial determinants of depth perception in two-dot motion patterns , 1972 .

[26]  Anil K. Jain,et al.  Texture Segmentation Using Voronoi Polygons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  John F. O'Callaghan,et al.  Computing the perceptual boundaries of dot patterns , 1974, Comput. Graph. Image Process..

[28]  Georges Voronoi.  Nouvelles applications des paramètres continus à la théorie des formes quadratiques. Deuxième mémoire. Recherches sur les parallélloèdres primitifs. , 1908 .

[29]  Claude L. Fennema,et al.  Scene Analysis Using Regions , 1970, Artif. Intell..

[30]  Keith Price,et al.  Picture Segmentation Using a Recursive Region Splitting Method , 1998 .

[31]  Charles T. Zahn,et al.  Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.

[32]  John Fairfield.  Segmenting blobs into subregions , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[33]  L. Glass.  Moiré Effect from Random Dots , 1969, Nature.

[34]  K. Koffka.  Principles Of Gestalt Psychology , 1936 .

[35]  M L Mendelsohn,et al.  THE ANALYSIS OF CELL IMAGES * , 1966, Annals of the New York Academy of Sciences.

[36]  Edward A. Patrick,et al.  Interactive Use of Problem Knowledge for Clustering and Decision Making , 1971, IEEE Transactions on Computers.

[37]  R. Hummel,et al.  Toward a low-level description of dot clusters: Labeling edge, interior, and noise points , 1979 .

[38]  C. Chow,et al.  Automatic boundary detection of the left ventricle from cineangiograms. , 1972, Computers and biomedical research, an international journal.

[39]  Narendra Ahuja,et al.  Dot Pattern Processing Using Voronoi Neighborhoods , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  A. Witkin,et al.  On the Role of Structure in Vision , 1983 .

[41]  Claes von Hofsten,et al.  Visual perception of motion in depth: Application of a vector model to three-dot motion patterns , 1973 .