臺灣博碩士論文加值系統

我們提出了一個從影片中擷取出人體物件的方法，其中人體物件包含了人臉以及身體。在MPEG-4和MPEG-7等應用中，擷取出人體物件通常是最重要的課題。我們提出了利用時間和空間的資訊並結合neuro-fuzzy的方法來擷取人體物件。首先，我們利用提出的fuzzy self-clustering technique將整張video frame分為一個個的區塊，並從中找出的膚色區塊，將其合併產生出可能的候選臉部。對於候選臉部內是否真的存在人臉，我們先尋找可能的臉部器官位置，找出眼睛和嘴巴所形成的三角形配對。再將所找到的配對與事先定義的template作比對。然後，前景與背景中大略屬於人體的區塊藉由多種資訊被找出來。最後，人體物件不確定的邊緣地帶利用訓練完成的fuzzy neural network作判斷，產生更精準的人體物件。其中類神經網路使用SVD-based hybrid learning algorithm作學習。經由實驗和比較結果，我們的方法可以找到準確的人臉的位置，並且擷取出更精準的人體物件。

We propose a novel approach for segmentation of human objects, including face and body, in image sequences. In modern video coding techniques, e.g., MPEG-4 and MPEG-7, human objects are usually the main focus for multimedia applications. We combine temporal and spatial information and employ a neuro-fuzzy mechanism to extract human objects. A fuzzy self-clustering technique is used to divide the video frame into a set of segments. The existence of a face within a candidate face region is ensured by searching for possible constellations of eye-mouth triangles and verifying each eye-mouth combination with the predefined template. Then rough foreground and background are formed based on a combination of multiple criteria. Finally, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network which is trained by a SVD-based hybrid learning algorithm. Through experiments, we compare our system with two other approaches, and the results have shown that our system can detect face locations and extract human objects more accurately.

第一章 簡介 1
第二章　其他方法之介紹 4
2.1 GARCIA''S APPROACH 4
2.2 FAN''S APPROACH 10
第三章　OUR APPROACH 17
3.1 OVERVIEW 17
3.2 ROUGH IMAGE SEGMENTATION 18
3.2.1 FUZZY SELF-CLUSTERING ALGORITHM 18
3.2.2 LABELING AND SMALL SEGMENT MERGING 18
3.3 INITIAL HUMAN OBJECT EXTRACTION 23
3.3.1 SKIN SEGMENT DETECTION 23
3.3.2 CANDIDATE FACE GENERATION 23
3.3.3 CANDIDATE FACE NORMALIZATION 28
3.3.4 旋轉人臉之偵測與特徵點偵測之SURVEY 28
3.3.5 FACIAL FEATURE DETECTION & FACE VERIFICATION 30
3.3.6 HUMAN BODIES DETECTION 37
3.4 HUMAN OBJECT REFINEMENT 39
第四章　實驗及比較結果 43
4.1 臉部偵測之比較結果 43
4.2 人體物件分割之比較結果 48

第五章　結論 58
附錄 - APPENDIX 59
A.1 BEST-FIT ELLIPSE CALCULATION 59
A.2 HAUSDORFF DISTANCE 61
A.3 VALLEY DETECTION FILTER 62
A.4 EYE-MOUTH TRIANGLE以及平均臉部等相關數據之統計 63
參考文獻 65

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