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研究生:黃膺任
研究生(外文):Ying-Jen Huang
論文名稱:使用影像處理與類神經網路分級胡蘿蔔之研究
論文名稱(外文):Carrots Grading with Image Processing and Neural Network
指導教授:李芳繁
指導教授(外文):Fang-Fan Lee
學位類別:碩士
校院名稱:國立中興大學
系所名稱:農業機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:1995
畢業學年度:83
語文別:中文
論文頁數:107
中文關鍵詞:影像處理 類神經網路 分級
外文關鍵詞:Image ProcessingNeural NetworkGrading
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本文主要目的是結合影像處理技術與類神經網路來建立一套胡蘿蔔的分級
系統。此系統以修改式矩來描述胡蘿蔔的形狀特徵,並配合分類器以判定
是否分叉。再以交叉梯度運算法輔以4相連標記法對破裂影像進行偵測。
凡被檢測未破裂及無分叉者則再進行分級處理,其分級方式,首先以間隙
追蹤法找出影像邊界,以Hotelling 法找出影像長軸,再根據長軸及影像
邊界的相對關係萃取出對稱率、直徑差異度、曲率及細密度等描述形狀的
特徵因子,並以根肩綠色與紅色在色度圖上有不同分佈的特性,找出屬於
綠色像素的判斷模式,藉以計算根肩綠色面積來作為描述顏色的特徵因子
,結合形狀及顏色因子作為分級參數,並配合一個三層式倒傳遞神經網路
來模擬人工分級作業,同時藉由改變輸入參數種類、輸入參數數目及隱藏
層節點數,以尋求分級胡蘿蔔之最佳模式。實驗結果,分叉辨識的正確率
為96 %,破裂辨識的正確率為92%,由神經網路所建立的分級模式與人工
分級結果比較,其相符程度為81%。
The purpose of the thesis is to combine vision processing
techniques with neural network to establish a grading system of
carrots. The system used modified moment to characterize carrot
contour and used classifier to identify forked carrot. Then the
ystem utilized the cross-gradient operator and the
4-connectivity labeling method to detect cracks. Those carrots
which were not forked or cracked were further graded into three
classes. In the grading processes, the crack-following method
was employed to find the contour pixels and the Hotelling
transform method was used to obtain the major axis. Then, the
symmetry, diameter difference, curvature, and compactness of
the carrot image were acquired according to the relationships
between the major axis and the contour. The green area of the
carrot image was calculated by adding all the green pixels of
the image. Whether a pixel was classified as green or red was
based on the chromaticity diagram. The size of the green area
was used as the color describing factor. A three-layer error
back-propagation neural network employing the shape and color
factors as the input parameters was used to simulate the manual
grading of carrots. To search for the best grading model, the
types and number of the input parameters and the number of the
nodes of the hidden layer were changed. The result of this
study revealed that the neural network grading system could
identify forks approximately at 96%, cracks at 92% and the
grading accuracy at 81% in comparison with manual grading.
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