碩士 / 國立中央大學 / 通訊工程學系 / 104 / In recent years, most fish recognition algorithm focus on recognizing aquarium fish rather than large commercial fishes. Thus, this thesis focuses on 4 species of tuna in adjacent seas of Taiwan. Through recognizing features of appearance of tuna, the proposed scheme can differentiate 4 species of tuna from 19 species of non-tuna fish.
At the training stage, this thesis segments fish fins manually and train SVM classifiers using the HOG descriptors. Based on the local image features, the method can improve recognition accuracy. In the test stage, this paper proposes to use binary projection for part segmentation. Descriptors of histogram of oriented gradients of three fins are the input of SVM classifiers and the classification results are majority voted for final decisions. Tests show that the recognition accuracy is around 72%. If the classification decision only depends on the feature of the second dorsal fin, the recognition accuracy is around 80%.
| Identifer | oai:union.ndltd.org:TW/104NCU05650092 |
| Date | January 2016 |
| Creators | Chao-Chun Chang, 張朝鈞 |
| Contributors | Chih-Wei Tang, 唐之瑋 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 69 |
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