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KNN算法奶牛圖像的奶牛身份識別研究+源代碼

時間:2019-04-16 20:32來源:畢業論文
對分割后的奶牛圖像提取LBP特征和LBPV紋理特征并通過支持向量機的方法進行識別,識別率可以達到77.59%左右

摘要:為了方便奶牛場的管理人員對奶牛進行管理,本文以奶牛生活視頻為對象進行基于機器視覺的奶牛身份識別。首先從視頻中通過截取不同奶牛的4種方位(正面、背面、左側、右側)圖像,創建了1294幅奶牛圖像的圖片集。然后使用圖像增強、去噪、形態學重建等預處理方法和固定閾值分割的方法,得到了奶牛目標圖像。最后提取奶牛圖像的對比度特征、HSV特征、LBP紋理特征和LBPV紋理特征,運用KNN算法和支持向量機計算和比較不同特征下奶牛身份的識別率。本研究表明,對分割后的奶牛圖像提取LBP特征和LBPV紋理特征并通過支持向量機的方法進行識別,識別率可以達到77.59%左右。34611
畢業論文關鍵詞:奶牛身份識別;圖像處理;特征提取;LBPV;支持向量機
Research on identification of cow based on cow image
Abstract: In order to facilitate the manager of livestock farm to manage cows, this essay took cow life video as the object and the identification of cows was carried out based on machine vision. First of all, the video images of 4 positions (front, back, left and right) of different cows were intercepted, an image library containing 1294 cow images was established. Then, the method of image enhancement, noise reduction, morphological reconstruction and the method of fixed threshold segmentation were used to obtain the target of cow images. Finally, the contrast characteristics, HSV features, LBP texture features and LBPV texture features of cow images were extracted. The recognition rate of cows under different characteristics was calculated and compared by using KNN algorithm and support vector machines. The research demonstrates that LBP features and LBPV texture features were extracted from segmented cow images and identified by support vector machines, and the recognition rate can reach about 77.59%.
源Z自-六+維L論W文W網^www.aftnzs.live

Key words: cow identification; image processing; feature extraction; LBPV; support vector machine
目  錄
摘要    1
關鍵詞    1
Abstract    1
Key words    1
1 緒論    1
1.1 研究意義    1
1.2 國內外研究現狀    2
1.2.1 國外研究現狀    2
1.2.2 國內研究現狀    2
1.3 研究方法    3
1.4 技術路線    3
2 奶牛圖像庫的建立    3
2.1 獲取奶牛圖像    3
2.2 人工識別奶牛身份    4
2.3 建立訓練庫與測試庫    4
3 奶牛圖像的預處理    5
3.1 灰度直方圖    5
3.1.1 直接灰度變換    5
3.1.2 直方圖均衡    5
3.2 圖像平滑    6
3.2.1 均值濾波    6
3.2.2 中值濾波    7
3.3 形態學操作    7
3.3.1 灰度腐蝕    7
3.3.2 灰度膨脹    8
3.3.3 灰度開運算    8
3.3.4 灰度閉運算    8
3.3.5 形態學重建    8
4 奶牛圖像的分割    8
4.1 邊緣提取    9
4.1.1 拉普拉斯算子    10
4.1.2 Sobel算子    10
4.1.3 Roberts算子    11
4.1.4 Prewitt算子    11
4.2 閾值分割    12
4.2.1 迭代閾值分割    13
4.2.2 固定閾值分割    13
5 奶牛圖像特征提取    16 源Z自-六+維L論W文W網^www.aftnzs.live
5.1 圖像的對比度特征提取    16
5.2 圖像的顏色特征提取    16
5.3 基于LBP算法的紋理特征提取    17 KNN算法奶牛圖像的奶牛身份識別研究+源代碼:http://www.aftnzs.live/jisuanjilunwen/20190416/32169.html
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