Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/24215
DC FieldValueLanguage
dc.contributor吳憲珠zh_TW
dc.contributor婁德權zh_TW
dc.contributor.advisor蔡垂雄zh_TW
dc.contributor.author汪純妤zh_TW
dc.contributor.authorWang, Chun-Yuen_US
dc.contributor.other中興大學zh_TW
dc.date2012zh_TW
dc.date.accessioned2014-06-06T07:22:23Z-
dc.date.available2014-06-06T07:22:23Z-
dc.identifierU0005-1808201100444800zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/24215-
dc.description.abstract影像切割是一門重要的技術,可用來辨識影像中的物體。現今已有許多影像切割技術被提出,但尚未有任何一種方法能將所有類型的影像都切得好。而傳統影像切割法經常會造成一張影像過度切割,例如天空的顏色漸層變化,很容易被切成許多區塊。 本論文提出了兩種區域合併法來改進傳統影像切割演算法所造成的過度切割問題,也就是將過度切割影像的區塊逐步合併以減少區塊數量,形成最終切割結果。第一種方法先將過度切割圖建立成區域相鄰圖,再以特殊的選擇方式選出最小的區塊,最小的區塊可能有兩個以上。接著利用提出的方法來判斷這些區塊是否與其鄰近區塊相似,如果相似則予以合併。判斷的方式則是計算出兩區塊間L*、a*、b*三個頻帶的群內變異以及群間變異值,若兩區塊的群內變異值小於群間變異值,則判斷兩區塊相似且應該要被合併,每一回合可能有數個區塊會被合併。若執行時到達終止條件,程式會根據區塊間的色彩變異數來決定在哪一個回合合併得最好,程式會再重新執行區塊合併法,停在最好的回合並輸出最終結果。本論文提出的第二種方法則是先用最小成本擴張樹篩選出差異小的邊,再用劃分L*、a*、b*三個頻帶成不同份的方式判斷這些邊是否該合併。每一個頻帶的每一部份都會被編號,而每一個頂點都會得到一組由這些編號所組成的編碼。若兩區塊的編碼滿足門檻條件,則兩區塊會被判斷為相似,並在最後進行合併。每一回合會有數個頂點被合併,若頂點數無法再減少,則程式會終止並輸出最後結果。實驗結果顯示本論文提出的方法皆能有效改善過度切割的情形。zh_TW
dc.description.abstractImage segmentation is an important technique in image content understanding and pattern recognition. Nowadays, many of image segmentation techniques have been proposed, but none can segment all kinds of images well enough. Traditional image segmentation methods usually cause the results to be to be over-segmentation. For example, if the sky shows strong continuous variations of color, it is easily segmented into many regions. The purpose of this thesis aims to solve this problem. Two region merging methods are proposed to improve the over-segmentation problem produced by traditional image segmentation algorithms. By applying region merging, the regions of over-segmentation are reduced gradually to export final segmentation. In the first method, over-segmentation structure is built as region adjacency graph. A special vertex selection algorithm is employed to choose the vertex with the smallest region size, thus there may exist two or more vertices that satisfy the condition. These vertices and their neighbors are being examined to determine their similarity. The vertices are merged at the end of each iteration if they are found similar. The verification method is to calculate inter-class difference and extra-class difference between two vertices in L*, a*, b* channels. If the inter-class difference of two vertices is smaller than the extra-class difference in all three channels, two regions should be merged. There may occurs regions merged at each iteration. If merging criterion is reached, the algorithm would find the iteration number which gives the best segmentation, and then reruns and stops at that iteration. Finally, the final segmentation is exported. In the second method, the algorithm sifts some smaller edges by using minimum spanning tree. Histograms of three color channels are divided into different parts, respectively; each part is labeled as a number, and each vertex has a set of color code formed by these label numbers. If the color codes of two regions satisfy certain conditions, two regions would merge at the end of iteration. The algorithm stops when the number of regions in the image does not reduce. Experimental results showed that the proposed methods can improve the problem produced by traditional algorithms.en_US
dc.description.tableofcontentsTables of Contents Abstract in Chinese.......................................................................................ii Abstract in English.......................................................................................iii Tables of Contents.........................................................................................v List of Tables.............................................................................................. vii List of Figures............................................................................................viii Chapter 1 Introduction...............................................................................1 1.1. Background........................................................................................................1 1.2. Image Segmentation...........................................................................................2 1.3. Color Space........................................................................................................4 1.4. Organization.......................................................................................................5 Chapter 2 An Automatic Color Image Segmentation Using Inter-class Difference and Extra-class Difference.....................................6 2.1. EDISON Software..............................................................................................6 2.2. The Proposed Method........................................................................................7 2.2.1. Initial Segmentation................................................................................8 2.2.2. Building Region Adjacency Graph..........................................................9 2.2.3. Region Merging.....................................................................................10 2.2.4. Final Processing....................................................................................17 2.3. Experimental Results........................................................................................19 2.4. Summary...........................................................................................................23 Chapter 3 An Unsupervised Region Merging Method for Color Image Segmentation...........................................................................24 3.1. The Proposed Method.......................................................................................24 3.1.1. Initial Segmentation...............................................................................24 3.1.2. Region Adjacency Graph and Minimum Spanning Tree.......................25 3.1.3. Thresholds Selection Using Color Histograms......................................26 3.1.4 Color Code and Region Merging Process..............................................28 3.2. Comparison and Results...................................................................................31 3.2.1. Comparison between Different Parts of Histograms.............................31 3.2.2. Experimental Results.............................................................................32 3.3. Summary...........................................................................................................35 Chapter 4 Conclusions and Future Works..............................................36 4.1. Conclusions......................................................................................................36 4.2. Future Works.....................................................................................................38 References..................................................................................................40en_US
dc.language.isoen_USzh_TW
dc.publisher資訊管理學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1808201100444800en_US
dc.subjectimage segmentationen_US
dc.subject影像切割zh_TW
dc.subjectregion mergingen_US
dc.subjectregion adjacency graphen_US
dc.subjectminimum spanning treeen_US
dc.subject區域合併zh_TW
dc.subject區域相鄰圖zh_TW
dc.subject最小成本擴張樹zh_TW
dc.title應用區域相鄰特性的影像切割技術之研究zh_TW
dc.titleA Study on Image Segmentation Techniques Using Region Adjacency Propertyen_US
dc.typeThesis and Dissertationzh_TW
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