台灣近海鮪魚之魚類辨識

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姓名

張朝鈞(Chao-Chun Chang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

台灣近海鮪魚之魚類辨識
(Fish Recognition for Tunas in Adjacent Seas of Taiwan)

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摘要(中)

觀察國際間近年研究魚類主題以來，絕大部分都以辨識海洋景觀魚類為主要研究方向，較少研究是以辨識大型經濟魚類做為主題，因此本論文針對台灣近海之四種鮪魚，透過對鮪魚外觀特徵辨識，達成鮪魚和其它十九種非鮪魚的魚類辨識。
本論文在訓練階段時將魚部位手動切割開並個別訓練其對應之分類器(classifier)，並以各魚鰭部位之HOG descriptors個別訓練其對應之分類器(classifier)，增加分類器對於局部影像特徵的辨識能力。辨識階段以本論文提出之二值投影法(binary projection)將魚類身體外觀三種特徵切割出，再透過Histogram of Oriented Gradients (HOG)將切割後的部位影像抓出其特徵描述子(descriptor)，將其結果輸入至訓練階段所得之三種支持向量機分類器(support vector machine)，在最後階段時，則整合三個(第一背鰭、第二背鰭、尾鰭)支持向量機分類結果透過投票(majority voting)機制決定是否為所要辨識的鮪魚種類。實驗結果顯示，四種鮪魚之平均辨識率約為72%，若只採用第二背鰭分類器，則辨識率可上升至80%。

摘要(英)

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%.

關鍵字(中)

★ 鮪魚
★ 魚類辨識
★ 部位切割
★ 梯度向量直方圖
★ 支持向量機

關鍵字(英)

★ Tuna
★ fish recognition
★ part segmentation
★ histogram of oriented gradients
★ support vector machines

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 研究方法 3
1.4 論文架構 4
第二章魚類辨識技術之現況 5
2.1 以魚體結構為特徵描述之辨識現況 5
2.2 改善分層分類器的魚類辨識技術現況 8
2.3 總結 10
第三章支持向量機與梯度向量直方圖(Support Vector Machine and Histogram of Oriented Gradient) 11
3.1 支持向量機(Support Vector Machine) 11
3.1.1 訓練階段(Training Stage) 13
3.1.2 測試階段(Test Stage) 17
3.2 梯度向量直方圖(histogram of oriented gradient) 18
3.3 總結 20
第四章本論文所提出之台灣近海的四種鮪魚辨識方案 21
4.1系統流程概述 21
4.2 鮪魚辨識之訓練階段 23
4.2.1 雙立方內插法縮放影像大小 23
4.2.2 梯度向量直方圖描述區塊歸一化 24
4.2.3 支持向量機訓練階段 25
4.3 鮪魚辨識之測試階段 26
4.3.1 魚體部位自動切割演算法(Fish Part Segmentation) 26
4.3.2 雙立方內插法縮放自動切割後影像大小 29
4.3.3 梯度向量直方圖影像二值化 29
4.3.4 支持向量機測試階段 32
4.4 總結 32
第五章實驗結果與討論 33
5.1 實驗影像資料庫參數 33
5.2 魚體部位自動切割之結果分析 36
5.3 梯度向量直方圖參數 44
5.4 辨識系統準確率評測法 45
5.5 辨識結果分析 47
5.5.1 鮪魚三個部位特徵辨識結果分析 47
5.5.2 鮪魚第二背鰭辨識結果分析 51
5.6 總結 52
第六章結論與未來展望 53
參考文獻 54

參考文獻

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2016-7-22

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