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|Title: ||Maximum-Likelihood Directivity For Depicting Perceptual Contours (SCI)|
|Authors: ||Chun-Shun Tseng|
|Keywords: ||Maximum-Likelihood Estimation|
|Issue Date: ||2019-12-09T07:39:15Z
|Publisher: ||Journal of Marine Science and Technology|
|Abstract: ||ABSTRACT: This paper presents a novel method based on MaximumLikelihood Estimation (MLE) to evaluate pixel directivity for
depicting image contours of objects as perceived by human
eyes. The method is characterized by employing discrete masks
with different shapes centered at a target pixel to sample gradient orientations of neighboring pixels for evaluating directivity of the target pixel, and applying MLE to determine one
of these discrete sampling masks that best fits the orientation
similarity of the target pixel. We show that such a fitting process
in effect fulfils the similarity and proximity laws in Gestalt
theory, and a salient alignment location can be determined by
subjecting the optimal directivity in conjunction with the
gradient magnitude of the target pixel to a Bayesian process.
Finally, the directivity of salient alignment locations is incorporated with the extension field (Guy and Medioni, 1992)
to detect perceptual contours. Experiments tested on complex
images and underwater images are provided to justify the superiority of the work over others.
|Relation: ||24(2) pp 152-162|
|Appears in Collections:||[電機工程學系] 期刊論文|
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