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Invariant surface characteristics for 3-D object recognition in range images
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ABSTRACTS OF PAPERS ACCEPTED FOR PUBLICATION
Geometrical T-formations on Pictures Represented by Leufcouks. MARLOES L. P. VAN LIEROP. Department of Mathematics and Computing Science, Rindhoven University of Technology, 5600 MB Eindhoven, The Netherlands. Received January 22,1985; revised June 181985. Quadtrees that represent pictures composed of 2” X 2” pixels, may be economically implemented using linear tree representations. An example of such a representation is the leafcode, which is an ordered sequence of the encoded leaves of the quadtree. This paper describes how the leafcodes of geometrical transformed pictures can be derived from the original leafcodes. In addition, we present some execution times. Viwd Hyperacui@: Representation and Computation of High Precision Position Information. Eric P. KROTKOV. GRASP Laboratory, Computer and Information Science Department/DZ, University of Pennsylvania, Philadelphia, Pennsylvania 19104. Received April 11, 1985; revised June 1, 1985; accepted July 11,1985. Visual hyperacuity, acuity an order of magnitude finer than the optical resolution of a diffraction-limited visual system, is described and analyzed in the terms of information theory. It is shown that in principle, the computation and representation of both luminance and edge features can be performed with a precision commensurate with hyperacuity thresholds and human abilities. Luminance features are represented as the centroid of the intensity distribution. Edge features are represented as zero-crossings in the difference of two filtered intensity distributions; this method is shown to be very economical. Algorithms are formulated in accord with the different representations, and tested with vernier acuity tasks. The results indicate that both methods can extract relative position with an accuracy better than 1 s WC. Incuriant Smface Characteristics for 3-D Object Recognition in Range Images. PAUL J. BESL AND RAMESH C. JAIN. Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor, Michigan 48109-1109. Received January 12,1985; revised March 26, 1985 and June 26,1985. In recent years there has been a tremendous increase in computer vision research using range images (or depth maps) as sensor input data. The most attractive feature of range images is the explicitness of the surface information. Many industrial and navigational robotic tasks will be more easily accomplished if such explicit depth information can be efficiently obtained and interpreted. Intensity image understanding research has shown that the early processing of sensor data should be data-driven. The goal of early processing is to generate a rich description for later processing. Classical differential geometry provides a complete local description of smooth surfaces. The first and second fundamental forms of surfaces provide a set of differential-geometric shape descriptors that capture domain-independent surface information. Mean curvuture and Gaussian curvature are the fundamental second-order surface characteristics that possess desirable invariance properties and represent extrinsic and intrinsic surface geometry respectively. The signs of these surface curvatures are used to classify range image regions into one of eight basic viewpoint-independent surface types. Experimental results for real and synthetic range images show the properties, usefulness, and importance of differential-geometric surface characteristics. Human Image IJnderstandhg: Recent Research and Thery. New York at Buffalo, New York. Received July 11,1985.
IRVING BIEDEIIMAN. State University of
The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into simple volumetric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of components (N probably I 36) can be derived from contrasts of five readily detectable properties of a twodimensional image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position and image quality and consequently allows robust object perception when the image is projected from a novel
ABSTRACTS OF PAPERS ACCEPTED FOR PUBLICATION
Geometrical T-formations on Pictures Represented by Leufcouks. MARLOES L. P. VAN LIEROP. Department of Mathematics and Computing Science, Rindhoven University of Technology, 5600 MB Eindhoven, The Netherlands. Received January 22,1985; revised June 181985. Quadtrees that represent pictures composed of 2” X 2” pixels, may be economically implemented using linear tree representations. An example of such a representation is the leafcode, which is an ordered sequence of the encoded leaves of the quadtree. This paper describes how the leafcodes of geometrical transformed pictures can be derived from the original leafcodes. In addition, we present some execution times. Viwd Hyperacui@: Representation and Computation of High Precision Position Information. Eric P. KROTKOV. GRASP Laboratory, Computer and Information Science Department/DZ, University of Pennsylvania, Philadelphia, Pennsylvania 19104. Received April 11, 1985; revised June 1, 1985; accepted July 11,1985. Visual hyperacuity, acuity an order of magnitude finer than the optical resolution of a diffraction-limited visual system, is described and analyzed in the terms of information theory. It is shown that in principle, the computation and representation of both luminance and edge features can be performed with a precision commensurate with hyperacuity thresholds and human abilities. Luminance features are represented as the centroid of the intensity distribution. Edge features are represented as zero-crossings in the difference of two filtered intensity distributions; this method is shown to be very economical. Algorithms are formulated in accord with the different representations, and tested with vernier acuity tasks. The results indicate that both methods can extract relative position with an accuracy better than 1 s WC. Incuriant Smface Characteristics for 3-D Object Recognition in Range Images. PAUL J. BESL AND RAMESH C. JAIN. Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor, Michigan 48109-1109. Received January 12,1985; revised March 26, 1985 and June 26,1985. In recent years there has been a tremendous increase in computer vision research using range images (or depth maps) as sensor input data. The most attractive feature of range images is the explicitness of the surface information. Many industrial and navigational robotic tasks will be more easily accomplished if such explicit depth information can be efficiently obtained and interpreted. Intensity image understanding research has shown that the early processing of sensor data should be data-driven. The goal of early processing is to generate a rich description for later processing. Classical differential geometry provides a complete local description of smooth surfaces. The first and second fundamental forms of surfaces provide a set of differential-geometric shape descriptors that capture domain-independent surface information. Mean curvuture and Gaussian curvature are the fundamental second-order surface characteristics that possess desirable invariance properties and represent extrinsic and intrinsic surface geometry respectively. The signs of these surface curvatures are used to classify range image regions into one of eight basic viewpoint-independent surface types. Experimental results for real and synthetic range images show the properties, usefulness, and importance of differential-geometric surface characteristics. Human Image IJnderstandhg: Recent Research and Thery. New York at Buffalo, New York. Received July 11,1985.
IRVING BIEDEIIMAN. State University of
The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into simple volumetric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of components (N probably I 36) can be derived from contrasts of five readily detectable properties of a twodimensional image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position and image quality and consequently allows robust object perception when the image is projected from a novel