Abstract
Conventional image entropy merely involves the overall pixel intensity statistics which cannot respond to intensity patterns over spatial domain. However, spatial distribution of pixel intensity is definitely crucial to any biological or computer vision system, and that is why gestalt grouping rules involve using features of both aspects. Recently, the increasing integration of knowledge from gestalt research into visualization-related techniques has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. This paper presents a Bayesian edge detector called GestEdge, which is effective in detecting gestalt edges, especially useful for forming object boundaries as perceived by human eyes. GestEdge is characterized by employing a directivity-aware sampling window or mask that iteratively deforms to probe or explore the existence of principal direction of sampling pixels; when convergence is reached, the window covers pixels best representing the directivity in compliance with the similarity and proximity laws in gestalt theory. During the iterative process based on the unsupervised Expectation-Minimization (EM) algorithm, the shape of the sampling window is optimally adjusted. Such a deformable window allows us to exploit the similarity and proximity among the sampled pixels. Comparisons between GestEdge and other edge detectors are shown to justify the effectiveness of GestEdge in extracting the gestalt edges.
Highlights
IntroductionThe terms of edge and contour are often used interchangeably in the field of image processing
The terms of edge and contour are often used interchangeably in the field of image processing.Still, the term “edge” is mostly used to denote image points where intensity difference between pixels is significant
We present a novel method called GestEdge, in which the directivity the directivity of a target pixel is iteratively evaluated with a sampling window of which the shape of a target pixel is iteratively evaluated with a sampling window of which the shape is deformable is deformable by the EM algorithm
Summary
The terms of edge and contour are often used interchangeably in the field of image processing. The proposed method mainly comprises the following steps: (i) First, a subset of pixels is selected from the input image as POI (pixels of interest); (ii) we take each pixel of the POI as a target pixel and iteratively update the shape of a detection window center at the target pixel; when convergence is reached, a directivity value representing the likelihood of perceiving the target pixel as edge point is obtained; (iii) we invoke the Bayesian process [10], to determine whether the target pixel is a gestalt edge, and if it is not, the target pixel is eliminated; (iv) we slide the window to the pixel in POI and go to Step (ii), until all pixels in POI are processed; and, (v) the remaining candidate pixels are outputted as the gestalt edge pixels
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