請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50806
標題: | 應用於影像切割之適應性生長和合併演算法 Adaptive Growing and Merging Algorithm for Image Segmentation |
作者: | Hsuan-Yi Ko 柯宣亦 |
指導教授: | 丁建均 |
關鍵字: | 影像切割,區域生長,區域合併,平均位移,輪廓,顯著性偵測,電腦視覺, image segmentation,region growing,region merging,mean shift,contour,saliency detection,computer vision, |
出版年 : | 2016 |
學位: | 碩士 |
摘要: | 影像切割因為其廣泛的應用,像是物體追蹤和影像壓縮,所以在電腦視覺中扮演了非常重要的角色。影像切割是一種將像素聚集成一致且顯著的區域的過程,而且現今已有很多針對不同應用所發展出來的影像切割演算法和技巧。而我們提出來的適應性生長和合併演算法是為了將一張圖分割成使用者所想要的區域數。我們切割方法的流程如下: 首先,產生原始影像的超像素來減少運算量並且提供有用的區域資訊。接著我們使用顏色直方圖和質地來量測兩相鄰超像素的相似度,然後根據該相似度來進行超像素生長,生長的過程會受限於邊緣強度。最後,我們藉由顏色、質地、輪廓、顯著值、以及區域大小替整張圖建立一個相異度矩陣,並按照相異度的大小依序合併區域。合併區域的過程會適應於區域數及影像局部特徵。
經過了超像素生長後,超像素被擴展成比較大的區域,這些大區域擁有更準確的邊緣和區域資訊像是平均顏色和平均質地,有助於最後的區域合併過程。實驗結果顯示我們提出來的方法可以將大部分的圖都切得很好,而且表現還勝過現今較新穎的方法。 In computer vision, image segmentation plays an important role due to its widespread applications such as object tracking and image compression. Image segmentation is a process of clustering pixels into homogeneous and salient regions, and a number of image segmentation algorithms and techniques have been developed for different applications. To segment an image accurately with the number of regions user gives, we propose an adaptive growing and merging algorithm. Our procedure is described as follows: First, a superpixel segmentation is applied to the original image to reduce the computation time and provide helpful regional information. Second, we exploit the color histogram and textures to measure the similarity between two adjacent superpixels. Then we conduct the superpixel growing based on the similarity under the constraint of the edge’s intensity. Finally, we generate a dissimilarity matrix for the entire image according to color, texture, contours, saliency values and region size, and subsequently merge regions in the order of the dissimilarity. The region merging process is adaptive to the number of regions and local image features. After the superpixel growing has been finished, some superpixels expand to larger regions, which contain more accurate edges and regional information such as mean color and texture, to help with the final process of region merging. Simulations show that our proposed method segments most of images well and outperforms state-of-the-art methods. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50806 |
DOI: | 10.6342/NTU201600348 |
全文授權: | 有償授權 |
顯示於系所單位: | 電信工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-105-1.pdf 目前未授權公開取用 | 6.38 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。