Intelligent line segment perception with cortex-like mechanisms

Liu, Xilong, Cao, Zhiqiang, Gu, Nong, Nahavandi, Saeid, Zhou, Chao and Tan, Min 2015, Intelligent line segment perception with cortex-like mechanisms, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1522-1534, doi: 10.1109/TSMC.2015.2415764.

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Title Intelligent line segment perception with cortex-like mechanisms
Author(s) Liu, Xilong
Cao, Zhiqiang
Gu, Nong
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Zhou, Chao
Tan, Min
Journal name IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume number 45
Issue number 12
Start page 1522
End page 1534
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-12
ISSN 2168-2216
Keyword(s) Science & Technology
Technology
Automation & Control Systems
Computer Science, Cybernetics
Computer Science
Artificial cells
biological visual cortex
line segment perception (LSP)
self-organization
PROBABILISTIC HOUGH TRANSFORM
RECEPTIVE-FIELDS
OBJECT RECOGNITION
STRIATE CORTEX
EDGE-DETECTION
RECONSTRUCTION
IMAGES
CAT
Summary This paper proposes a novel general framework for line segment perception, which is motivated by a biological visual cortex, and requires no parameter tuning. In this framework, we design a model to approximate receptive fields of simple cells. More importantly, the structure of biological orientation columns is imitated by organizing artificial complex and hypercomplex cells with the same orientation into independent arrays. Besides, an interaction mechanism is implemented by a set of self-organization rules. Enlightened by the visual topological theory, the outputs of these artificial cells are integrated to generate line segments that can describe nonlocal structural information of images. Each line segment is evaluated quantitatively by its significance. The computation complexity is also analyzed. The proposed method is tested and compared to state-of-the-art algorithms on real images with complex scenes and strong noises. The experiments demonstrate that our method outperforms the existing methods in the balance between conciseness and completeness.
Language eng
DOI 10.1109/TSMC.2015.2415764
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082119

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
2018 ERA Submission
GTP Research
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