Abstract
Shape features are one of the most popular low-level image representations for computer vision (CV) tasks such as template matching, image collaboration and object recognition. In this paper, an application-originated research has been introduced for extracting representative shape characteristics from challenging real-world scenes based on the image 'textures'. The proposed new approach starts from registering image colour and texture regions within undirected weight graphs. Then through applying Mean-Shift clustering, the graph can be used to identify image regions that contain similar texture patterns judging by the pair-wise region comparison operations. Based on the theoretical study and practical trials carried out in the research, the devised clustering-based segmentation strategy has proven its effectiveness under complex real-world conditions. The innovative I-PWRC algorithm developed in this research has integrated a number of the state-of-the-art image processing techniques including MS, PWRC, and the hierarchical pyramid structures. Test and evaluations have recorded satisfactory segmentation outputs and indicated its promising perspective for future CV applications, including video processing.
Original language | English |
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Title of host publication | Proceedings of the 19th International Conference on Automation and Computing |
Subtitle of host publication | Future Energy and Automation |
Editors | Yi Cao, Shengfeng Qin, Alhaji Shehu Grema |
Publisher | IEEE Computer Society |
Pages | 179-184 |
Number of pages | 6 |
ISBN (Print) | 9781908549082 |
Publication status | Published - 14 Nov 2013 |
Event | 19th International Conference on Automation and Computing - London, United Kingdom Duration: 13 Sep 2013 → 14 Sep 2013 Conference number: 19 |
Conference
Conference | 19th International Conference on Automation and Computing |
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Abbreviated title | ICAC 2013 |
Country/Territory | United Kingdom |
City | London |
Period | 13/09/13 → 14/09/13 |