Illusory contour detection using MRF models

Madarasmi, Suthep; Pong, Ting-Chuen; Kersten, Daniel

doi:10.1109/icnn.1994.374966

Cited by 3 publications

(4 citation statements)

References 7 publications

(4 reference statements)

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“…Most of the existing models do extract special image features. For example, Madarasmi et al (1994) use stochastic minimization of a functional to predict real and illusory contours of objects at different depth planes. The model is successfully applied to Kanizsa square illusion, where it detects both the illusory square and the overlapped inducer objects.…”

Section: Discussionmentioning

confidence: 99%

“…An approach that has the potential of not extracting special image features is the functional optimization, used by some boundary detection models capable of generating illusory contours (Kass et al, 1988; Madarasmi et al, 1994; Williams and Hanson, 1994; Geiger et al, 1996; Saund, 1999; Gao et al, 2007). The functional is used to give a score for each contour configuration, and the final contours are not “constructed” by the model, but rather “come out” as the minimizer of the functional.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Illusory Contours Without Extracting Special Image Features

Yankelovich

Spitzer

2019

Front. Comput. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Illusory Contours Without Extracting Special Image Features

Yankelovich

Spitzer

2019

Front. Comput. Neurosci.

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“…In this paper, we extend these earlier works [8,9,22] by embedding explicit decision rules for contour continuation and surface depth propagation in local units of a Hierarchical Markov random field model. The multiscale hierarchy is sensitive to the topology of image structures and is used to facilitate rapid long range propagation of local cues.…”

Section: Introductionmentioning

confidence: 99%

“…These sparse occlusion cues provide important constraints for the emergent global perception of figure and ground. The formation of global percepts from such local cues and the computation of layer organizations have been modeled as an optimization process with a surface diffusion mechanism [8,9,22].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Markov Random Field Model for Figure-Ground Segregation

Lee

Kanade

2001

Lecture Notes in Computer Science

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Abstract.To segregate overlapping objects into depth layers requires the integration of local occlusion cues distributed over the entire image into a global percept. We propose to model this process using hierarchical Markov random field (HMRF), and suggest a broader view that clique potentials in MRF models can be used to encode any local decision rules. A topology-dependent multiscale hierarchy is used to introduce long range interaction. The operations within each level are identical across the hierarchy. The clique parameters that encode the relative importance of these decision rules are estimated using an optimization technique called learning from rehearsals based on 2-object training samples. We find that this model generalizes successfully to 5-object test images, and that depth segregation can be completed within two traversals across the hierarchy. This computational framework therefore provides an interesting platform for us to investigate the interaction of local decision rules and global representations, as well as to reason about the rationales underlying some of recent psychological and neurophysiological findings related to figure-ground segregation.

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