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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68948

    Title: 即時人臉偵測、姿態辨識與追蹤系統實現於複雜環境;Real Time Human Face Detection, Posture Recognition and Tracking System Realized in Complex Environment
    Authors: 梁守鈞;Liang,Shou-jyun
    Contributors: 電機工程學系
    Keywords: 人臉偵測;種子區域生長法;小波圖像分割法;2DPCA演算法;HPSO-TVAC演算法;自適應搜尋框;Face Detecting;Seed Region Growing;Wavelet Transform;2DPCA;HPSO-TVAC;Adaptive Seeking Window
    Date: 2015-08-19
    Issue Date: 2015-09-23 14:47:13 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 本文旨在實現人臉偵測、辨識人臉姿態與追蹤於即時動態影像,透過攝影機擷取影像,首先利用閥值把膚色與影像分離,經過形態學處理,把不必要的雜訊給移除,最後使用種子區域生長法,標記每個膚色的區塊,使用人臉判定法,假如膚色區塊不是人臉的話就被捨去掉。
    當正臉被辨識成功,找出中心點的位置,對人臉建構顏色直方圖模型,人臉追蹤演算法是使用自組織隨時調變係數的粒子最佳化 (HPSO-TVAC) 演算法,當人臉遇到遮蔽問題,本文使用自適應搜尋框,當找不到人臉時使用較大的搜尋框,搜尋框的縮放是依照群體最佳適應值。
    ;The main purposes of this thesis are to achieve human face detection and head posture recognition, as well as to track a dynamic image in real time via camera. First, skin-color region is detected, after morphological operations, unnecessary noise is removed, and the method of seed region growing is used to mark pixel blocks. Then the skin-color region is determined whether or not each block is a human face. If it is not human face, it is discarded. Otherwise, wavelet transform is used to decompose the face image. A low-frequency sub-band face image is captured by wavelet transform, and two-dimensional principle component analysis (2DPCA) is used to recognize head posture. Face color histograms are used to build face models, and faces are traced by the Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients (HPSO-TVAC) algorithm. In order to solve the face masking problem, adaptive seeking windows are applied. When a human face is not detected, a large seeking window will be used, which will zoom in or out depending on the best global fitness.
    Appears in Collections:[電機工程研究所] 博碩士論文

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