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研究生:楊欣泰
研究生(外文):Hsin-Tai Yang
論文名稱:形態學結構元件分解與影片人像擷取技術之研究
論文名稱(外文):Decomposition of Morphological Structuring Elements and Segmentation of Human Objects in Video Sequences
指導教授:李錫智李錫智引用關係李錫智李錫智引用關係
指導教授(外文):Shie-Jue LeeShie-Jue Lee
學位類別:博士
校院名稱:國立中山大學
系所名稱:電機工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:125
中文關鍵詞:影像處理形態學類神經網路人像擷取分群分群人像擷取類神經網路形態學影像處理
外文關鍵詞:image processingmorphologyclusteringhuman object segmentationneural networkneural networkhuman object segmentationclusteringmorphologyimage processing
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隨著影像處理技術的快速發展,各種科學學門如醫學、氣象、天文、工業控制等都出現了許多前所未見的應用。本論文的主要內容係描述我們對於影像處理領域進行研究後所獲致的成果。就技術上而言,這些成果可分為兩部分。第一部份是關於形態學結構元件的分解,第二部分則探討了影像中人體物件辨識的問題。

就第一部份成果而言,一個可將結構元件分解成較小元件的整合性方法被提出。藉由將分解問題轉化成一個線性限制集,整數線性規劃技術即可被用來解最佳分解。相較於其它現存的方法,我們提出的方法更為一般性,並有許多優點。第一,它是以系統化的方式對任意形狀的結構元件進行分解。第二,對於凸影像,分解後所獲得的小結構元件並不限制為3x3大小。第三,使用者可自由選定候選結構元件集。最後,最佳分解的標準可依應用不同而進行彈性調整。

就第二部份成果而言,我們提出一個包含三階段的人體影像辨識系統。在第一階段中,針對要進行分割的主影像,我們提出運用了結合空間觀念與顏色屬性的混和式自我分群技術來達到減少小斷片數量的目的。在第二階段中,臉形由八向多邊形模擬表示,而臉部特徵則包含雙眼與嘴。我們將這些資訊參數化後輸入到已訓練完成的類神經網路進行臉部辨識。在最後階段中,利用辨識出的臉部區域的大小與方向,並運用影片中各畫面間的動作資訊來偵測大致的身體區域,然後再利用類神經網路來辨識模糊未分類的區域。
With the rapid development of image processing techniques, many unprecedented applications are emerging from all kinds of science branches, such as medicine, meteorology, astronomy, industrial control, etc. This dissertation presents an achievement of our research work related to the image processing field. Technically, the work consists of two parts. The first part concerns decomposition of morphological structuring elements while the second part explores the problems of human object segmentation in video/image data.

In the first part, an integrated method, aiming to decompose a morphological structuring element into dilations of smaller ones, is proposed. By first formulating the decomposition problem into a set of linear constraints, the integer linear programming echnique is then applied to obtain an optimal decomposition. Compared to other existing approaches, the proposed method is more general and has several advantages. Firstly, it provides a systematic way of decomposing arbitrarily shaped structuring elements. Secondly, for convex images, factors can be of any size, not restricted to 3x3. Thirdly, the candidate set can be freely assigned by the user and finally the criteria of optimality can be flexible.

In the second part, we present a three-stage system for segmentation of multiple human objects in a video stream. In the first stage, for a base frame to be segmented, we propose a hybrid self-clustering technique that incorporates the spatial concept as well as color attributes to reduce the number of small segments. In the second stage, the face shape modeled by the eight-directional convex polygons and the face features including two eyes and a mouth are extracted, parameterized, and fed to a trained neural network for detection of a human face. In the last stage, the size and orientation of the detected face region as well as the motion information among frames are used to roughly detect the corresponding body. To locate human objects more accurately, another neural network is constructed for recognizing the ambiguous regions.
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Organization of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . 3
2 Preliminary Background 5
2.1 Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Dilation and Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Opening and Closing . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.3 Hit-and-Miss Transform . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Integer Linear Programming (ILP) . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.2 Solving the ILP Problems . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.1 Architecture and Operation . . . . . . . . . . . . . . . . . . . . . . 22
2.3.2 Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.3 Backpropagation Networks . . . . . . . . . . . . . . . . . . . . . . 26
3 Optimal Decomposition of Morphological Structuring Elements 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Chain Code Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 Decomposition Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4 Formulating Decomposition in Constraints . . . . . . . . . . . . . . . . . . 36
3.4.1 Images without Origins Specified . . . . . . . . . . . . . . . . . . . 36
3.4.2 Images with Origins Specified . . . . . . . . . . . . . . . . . . . . . 39
3.5 Optimal Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5.1 Solving by Integer Linear Programming . . . . . . . . . . . . . . . 42
3.5.2 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.6 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4 Segmentation of Multiple Human Objects in Video Sequences 52
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2 Overview of Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.3.1 Block Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.3.2 Segment Labeling and Small SegmentsMerging . . . . . . . . . . . 62
4.4 Detection of Faces and Bodies . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.4.1 Detection of Skin Segments . . . . . . . . . . . . . . . . . . . . . . 65
4.4.2 Generation of Candidate Face Regions . . . . . . . . . . . . . . . . 66
4.4.3 Postprocessing of Candidate Face Regions . . . . . . . . . . . . . . 71
4.4.4 Verification of Human Faces . . . . . . . . . . . . . . . . . . . . . . 72
4.4.5 Detection of Human Bodies . . . . . . . . . . . . . . . . . . . . . . 77
4.5 Refinement of Human Objects . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.6.1 Hybrid Fuzzy Clustering . . . . . . . . . . . . . . . . . . . . . . . . 82
4.6.2 Face Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.6.3 Human Object Segmentation . . . . . . . . . . . . . . . . . . . . . 89
5 Conclusion and Future Work 94
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
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