English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28607/40644
Visitors : 5275044      Online Users : 512
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51838

Title: Bayesian Approach to Perceptual Edge Preservation in Computer Vision
Authors: Ren-Jie Huang
Jung-Hua Wang
Chun-Shun Tseng
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: Perceptual Edges
Deep Learning
Date: 2017-05
Issue Date: 2018-12-27T03:02:57Z
Publisher: Computer Science and Information Technology
Abstract: Abstract: This paper presents a novel approach for preserving perceptual edges representing boundaries of objects as perceived by human eyes. First, a subset of pixels (pixels of interest, POI) in an input image is selected by a pre-process of removing background and noise. One by one as a target pixel, each of POI is subjected to a Bayesian decision. This approach is characterized by iteratively employing a shape-variable mask to sample gradient orientations of pixels for measuring the directivity of a target pixel, the mask shape is updated after each iteration. We show that a converged mask covers pixels that best fit the orientation similarity with the target pixel, which in effect fulfills the similarity and proximity principles in Gestalt theory. Subsequently, a Bayesian rule is applied to the converged directivity to determine whether the target pixel belongs to a perceptual edge. Instead of using state-of-the-art edge detectors such as Canny detector [1], a pre-process combining Gaussian Mixture Model (GMM) [2] and Difference of Gaussian (DoG) [3] is devised to select POI, wherein GMM is responsible for removing the background of an input image (first screening), whereas DoG for filtering noisy or false contours (second screening). Experimental results indicate that a great amount of computational load can be saved, in comparison with the use of Canny detector in our previous work [4]. Since the perceptual edges are useful for forming a complete object contour corresponding to the human visual perception, the results of this paper can be potentially cooperated with other more advanced object detection methods such as the deep learning-based SSD [5] to achieve the same effect as the human visual system in dealing with obscured or corrupted input images, whereby even if a target object is occluded by other objects or corrupted by rainy water, it can be still identified correctly, this feature should greatly enhance the operational safety of unmanned vehicles, unmanned aircraft and other autonomous systems.
Relation: 5(3) pp.113-119
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51838
Appears in Collections:[電機工程學系] 期刊論文

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback