NDLI: Boundary detection using unbiased sparseness-constrained colour-opponent response and superpixel contrast

Content Provider	IET Digital Library
Author	Wang, Gang Chen, Yong guang Gao, Ming Yang, Suo chang Feng, Fu qiang Baets, Bernard De
Abstract	Boundaries play a crucial role in various image-based tasks, but many existing non-learning-based boundary detection methods underperform in recognising authentic boundaries from a complex background. In this study, the authors address this problem using the sparseness-constrained colour-opponent response and the superpixel contrast. First, building on the biologically inspired colour-opponency mechanism, the authors elaborate a method to compute the unbiased sparseness-constrained colour-opponent response. In this procedure, locations showing colour variations are enhanced, while the textural locations are preliminarily suppressed by the cue of local sparseness measure. Second, with the help of superpixel segmentation, the authors present an effective approach to obtain the superpixel contrast map. This approach helps to exploit the object shape information in suppressing textures. Consequently, the authors propose a non-learning-based method to detect boundaries in images, combining the unbiased sparseness-constrained colour-opponent response and the overall superpixel contrast map. Experiment results on widely adopted datasets manifest that the authors method outperforms most of the competing methods. In particular, compared with the state-of-the-art surround-modulation method, the proposed method obtains a comparable performance while consuming much less runtime.
Starting Page	2976
Ending Page	2986
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0949
Journal	IET Image Processing
Publisher Date	2020-05-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authentic Boundaries Biologically Inspired Colour-opponency Mechanism Colour Variations Computer Vision And Image Processing Technique Edge Detection Image Colour Analysis Image Recognition Image Resolution Image Segmentation Image Texture Image-based Task Local Sparseness Measure Nonlearning-based Boundary Detection Method Superpixel Contrast Map Unbiased Sparseness-constrained Color-opponent Response Unbiased Sparseness-constrained Colour-opponent Response
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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