白帶魚形狀特徵擷取與魚種辨識

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姓名

許致杰(Chih-Chieh Hsu) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

白帶魚形狀特徵擷取與魚種辨識
(Shape Feature Extraction of Trichiurus lepturus and Variety Identification)

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至系統瀏覽論文 (2025-7-28以後開放)

摘要(中)

白帶魚是臺灣近海重要的撈捕和消費的主要魚類之一。臺灣常見的白帶魚品種有三種，目前在撈捕和消費現場要進行白帶魚的品種辨識，都需藉由專家經驗人工辨識，因此難以達到普及應用目的。本研究提出一個白帶魚特徵擷取方法，藉由眼睛中心至頭部輪廓的距離向量，來鑑識白帶魚品種。我們首先透過U-Net神經網路進行白帶魚偵測以及頭部分割，接著提取輪廓以及偵測魚眼，最後計算每隻白帶魚的形狀特徵。為了驗證此一特徵的魚種鑑別性，我們以機率神經網路、決策樹、支援向量機以及k近鄰分類器等四種分類器來進行魚種辨識。

摘要(英)

Trichiurus lepturus is one of the main fishes that are harvested and consumed off the coast of Taiwan. There are three common species of Trichiurus lepturus in Taiwan. Currently, fish’s classification at the fishing and consumption sites, manually identified the species of Trichiurus lepturus by experts or experiencers is required. Therefore, it is difficult to achieve the popularization purpose. This research mentions about a feature extraction method for Trichiurus lepturus, which uses the distance vector from the center of the eye to the outline of the head, to identify the species of Trichiurus lepturus. First, we use the U-Net neural network to detect the Trichiurus lepturus and execute the head segmentation, then extract the contours and detect fish eyes, and calculate the shape features of each Trichiurus lepturus. In order to verify the "discriminative" of shape feature, we use four types of classifiers such as probability neural network, decision tree, support vector machine and k-nearest neighbor classifier to classify the fishes.

關鍵字(中)

★ 白帶魚
★ 形狀特徵擷取
★ 魚種辨識

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章、緒論 1
1-1 研究動機 1
1-2 研究目的 3
1-3 論文架構 3
第二章、技術回顧 4
2-1 U-net 4
2-1-1 U-net架構 4
2-1-2 提取輪廓 6
2-2 Canny edge detection 7
2-2-1 灰階(Gray Scale) 11
2-2-2 高斯濾波器(Gaussian filter) 11
2-2-3 影像梯度(Image gradient) 12
2-2-4 非極大值抑制(Non-maximum suppression) 15
2-2-5 雙閥值(Double Thresholding) 17
2-3 圓形霍夫變換(Circular Hough Transform) 18
2-4 機率神經網路(Probabilistic neural network) 20
2-4-1 貝氏分類器(Naive Bayes Classifier) 21
2-4-2 Parzen視窗法 22
2-4-3 PNN分類器架構 24
2-5 決策樹(Decision tree) 26
2-6 支援向量機(SVM) 28
2-7 k近鄰分類(KNN) 29
第三章、白帶魚辨識系統設計 32
3-1 系統架構 32
3-2 物件分割設計 33
3-3 提取輪廓設計 35
3-4 魚眼偵測設計 36
3-5 結合影像及正規化距離特徵向量計算 38
3-6 白帶魚分類器設計 42
3-6-1 PNN分類器 43
3-6-2 Scikit-learn 44
3-7 辨識率評估方法 45
第四章、實驗 47
4-1 實驗環境 47
4-1-1 資料庫 47
4-1-2 軟硬體環境 49
4-2 白帶魚切割性能評估實驗 51
4-3 白帶魚切割實驗 56
4-4 白帶魚特徵向量鑑別性分析 58
4-5 魚種辨識實驗 62
第五章、結論與未來展望 66
5-1 結論 66
5-2 未來展望 67
參考文獻 68

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指導教授

陳永芳陳慶瀚

審核日期

2020-7-29

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