請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5386
標題: | 影像與影片之顯著性偵測及一個利用超像素之馬可夫隨機場模型的方法 Saliency Detection of Image and Video and a Proposed Approach using Superpixel-Level Markov Random Field Model |
作者: | Wen-Wen Chang 張雯雯 |
指導教授: | 貝蘇章 |
關鍵字: | 顯著性偵測,視覺注意力,超像素,馬可夫隨機場, saliency detection,visual attention,superpixel,markov random field, |
出版年 : | 2014 |
學位: | 碩士 |
摘要: | 顯著性偵測(人類視覺注意力偵測),指的是人類觀測者第一眼會注意到的區域,這些區域和其周圍區域通常有著顯著的差異。顯著性偵測對很多電腦視覺上應用上有幫助,近年來很多研究致力於提升顯著性偵測的準確性。經由觀察發現大部分現有的偵測方法,都難以消去複雜的背景區域並偵測出完整的顯著物體。因此我們運用將影像邊緣區域視作背景的假設及考慮各區域在空間上的關連性以提升準確度。
在本篇論文中,我們提出一個新的影像顯著性偵測方法,並將其延伸至影片顯著性偵測。此方法是基於假設影像邊緣為背景,和一個以超像素為節點單位的馬可夫隨機場模型。首先將影像分割成超像素,再取出每個超像素內的色彩、紋理、聚焦程度的特徵值。然後建立一個馬可夫隨機場模型來描述空間和時間上節點之間的關連性。最後再將以超像素為單位的顯著圖轉換為以像素為單位的顯著圖。實驗結果證實我們的方法可以準確的提取出影像和影片中的顯著性物體,並優於大部分現有的方法。 Saliency, also known as visual attention, refers to the areas distinct from its surroundings that human observer would focus at a glance. Saliency detection benefits many computer vision tasks, and extensive efforts have been devoted to achieving better saliency detection performance. We observe that most of the previous works are hard to deal with the non-homogeneous color distribution within an object. Motivated by this observation, we consider the spatial structure between image regions to obtain better results. In this thesis, a proposed approach for image saliency detection and its extension for video saliency detection are introduced. The approach is based on background prior and superpixel-level Markov Random Field (MRF) model. First, we separate the image into middle-level superpixels and extract low-level features (color, texture energy, and defocus level) within each superpixel. Then, we build up a Markov-Random-Field (MRF) on the superpixels and adopt simplified propagation technique to optimize the superpixel saliency. Afterward, we refine this superpixel-level solution to pixel-level saliency map. Experimental results demonstrate that our proposed method is promising as compared to the state-of-the-art methods in two public available datasets. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5386 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 電信工程學研究所 |
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檔案 | 大小 | 格式 | |
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ntu-103-1.pdf | 6 MB | Adobe PDF | 檢視/開啟 |
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