A perceptual organization approach to contour estimation via composition, compression and pruning

Title from caption. "April 1, 1998."Includes bibliographical references (p. 36-39).Supported in part by MURI. DAAH04-96-1-0445 Supported in part the Foundations of Performance Metrics for Object Recognition.Stefano Casadei, Sanjoy Mitter

contour interpolation by vector field combination

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Predicting Illusory Contours Without Extracting Special Image Features

Boundary completion is one of the desired properties of a robust object boundary detection model, since in real-word images the object boundaries are commonly not fully and clearly seen. An extreme example of boundary completion occurs in images with illusory contours, where the visual system completes boundaries in locations without intensity gradient. Most illusory contour models extract special image features, such as L and T junctions, while the task is known to be a difficult issue in real-world images. The proposed model uses a functional optimization approach, in which a cost value is assigned to any boundary arrangement to find the arrangement with minimal cost. The functional accounts for basic object properties, such as alignment with the image, object boundary continuity, and boundary simplicity. The encoding of these properties in the functional does not require special features extraction, since the alignment with the image only requires extraction of the image edges. The boundary arrangement is represented by a border ownership map, holding object boundary segments in discrete locations and directions. The model finds multiple possible image interpretations, which are ranked according to the probability that they are supposed to be perceived. This is achieved by using a novel approach to represent the different image interpretations by multiple functional local minima. The model is successfully applied to objects with real and illusory contours. In the case of Kanizsa illusion the model predicts both illusory and real (pacman) image interpretations. The model is a proof of concept and is currently restricted to synthetic gray-scale images with solid regions

Boundary contour based surface representation.

We receive most information about our surrounding space and objects through the eyes. To reconstruct the 3D space and objects in the visual system from the 2D retinal images, surface representation must be a critical intermediate stage in the visual processing stream. It is hypothesized in the dissertation that the visual system represents textured surface by a border-to-interior strategy: boundary contours would be encoded first and then border-ownership assignments would be resolved. This process would solve the related problems such as figure-ground segregation, surface depth relationship, occlusion, transparency, etc. As a result, the boundary contours of the surfaces would be well defined and then the visual system could register the local features in different domains with the boundary contours, gradually from the adjacent areas of the boundary contours to the interior of the surfaces. To testify this hypothesis in the current proposal, a monocular boundary contour (MBC) paradigm is adapted from earlier studies by Ooi and He (2005, 2006). In Chapter 1, the boundary-contour-based hypothesis, with the MBC paradigm, is used to re-address a decade-long debate about binocular vision: whether (and how) binocular integration and inhibition coexist. In Chapter 2–5, the MBC-induced binocular suppression is systematically investigated, especially in Chapter 3 where the cortical speed of the hypothesized border-to-interior spreading is quantitatively estimated. In the end, the rules how the surface fragments are integrated to a global representation is further studied in Chapter 6 and 7, especially focusing on the role of luminance and color contrast polarities