Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
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This paper studied a model that is able to extract occluding surfaces of subjective contour figures based on the mechanism of feature extraction found in a visual system. A common factor in all such subjective contour figures, such as the Kanizsa triangle is having a surface occluding part of a background, i.e. subjective contours are always accompanied by subjective surfaces. In this paper we propose a neural network model that predicts the shape of subjective surfaces. This model employed an important two-stage process of the Induced Stimuli Extraction System (ISES) and Subjective Surfaces Perception System (SSPS). The former system extracted the induced stimuli for the perception of subjective surfaces, and the latter formed the subjective surfaces from the induced stimuli. The proposed model is demonstrated on a variety of Kanizsa-type subjective contour displays. The results of the experiment showed that the proposed model was successful not only in extracting the induced stimuli for the perception of subjective contours, but also in perceiving the subjective surface from the induced stimuli.