本文關鍵詞：針對樹型立體視覺匹配的視差預測層級加速算法研究　出處：《上海交通大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： 立體視覺 DPM 視差預測模型 核心視差置信區(qū)間 DPA 層級加速算法 匹配代價聚合

【摘要】：立體視覺是目前計算機視覺領域一個重要的課題。它的主要目的在于提取場景中的深度信息,利用立體匹配算法得到精準的視差圖,進而完成場景的三維重構。基于樹型結構的匹配是其中重要的立體匹配算法之一。近年來提出的樹型立體視覺匹配算法通過在圖像中構造最小生成樹(MST)來聚合匹配代價。該類算法結合了高效率局部匹配與高精度全局匹配的特點,匹配精度以及匹配效率較傳統(tǒng)匹配算法均有很大提高。其時間復雜度理論值為O(N·dmax),即運行時間只和像素點總數(shù)N和圖像最大視差dmax相關。但樹型匹配算法基于貪心搜索策略,對于構造匹配窗口缺乏一定的自適應性,在同一平面存在不同深度分布的情況下無法得到精確的深度信息。同時,該類算法運行效率受到最大視差限制,在最大視差較大的實際場景和高分辨率圖像中,運行速度較慢。本論文針對傳統(tǒng)樹型立體視覺匹配算法中出現(xiàn)的問題,引入視差預測模型(Disparity Prediction Model,DPM),提出針對樹型立體視覺匹配的視差預測層級加速算法(Disparity-Prediction-Based Hierarchical Accelerator,DPA)。該算法通過預測視差概率分布,構造出更精確的核心視差置信區(qū)間,使得匹配代價的計算,代價聚合,視差計算,視差優(yōu)化都只發(fā)生在這一核心視差置信區(qū)間內(nèi),從而降低樹型匹配算法的時間復雜度。本論文通過大量的實驗數(shù)據(jù),包括Middlebury數(shù)據(jù)庫的實驗室圖像,KITTI數(shù)據(jù)庫的實際場景的高分辨率圖像,以及自主拍攝的低質(zhì)量的實際場景圖像,驗證了DPA加速算法在提升匹配精度的同時,能夠優(yōu)化基于樹型結構匹配算法的復雜度,大大提高運行效率。本論文共有三個主要貢獻:1.提出了核心視差置信區(qū)間概念。對于每一個像素點,其視差值處于核心視差置信區(qū)間范圍內(nèi)的概率遠遠大于置信區(qū)間范圍外。通過精確的視差置信區(qū)間,可以在匹配代價計算、視差計算以及視差修復等步驟中大大減少計算量,提高運行效率。2.提出了基于視差分布的DPM視差預測模型。此模型在圖像金字塔結構基礎上,通過小規(guī)模視差預測出大規(guī)模視差的概率分布,并應用于構造核心視差置信區(qū)間。3.提出了普適的針對樹型立體視覺匹配的DPA層級加速算法。此算法可結合到現(xiàn)有的樹型匹配算法框架中。經(jīng)實驗驗證,該算法大大提高了原始樹型匹配算法的運行效率。圖像分辨率越高,視差范圍越大,效果越明顯。具體地,對于高分辨率圖像,應用了DPA層級加速算法后,匹配效率提升達7-10倍。
[Abstract]:Stereo vision is an important subject in the field of computer vision. The main purpose of stereo vision is to extract the depth information from the scene and use stereo matching algorithm to get accurate parallax images. The matching based on tree structure is one of the important stereo matching algorithms. In recent years, the tree stereo vision matching algorithm is proposed by constructing the minimum spanning tree MSTs in the image. This algorithm combines the characteristics of high efficiency local matching and high precision global matching. The accuracy and efficiency of matching are greatly improved compared with the traditional matching algorithm, and the theoretical value of time complexity is OGN 路dmax. That is, the running time is only related to the total number of pixels N and the maximum parallax dmax of the image. However, the tree matching algorithm is based on greedy search strategy, so it has no self-adaptability for constructing matching window. In the case of different depth distribution in the same plane, the accurate depth information can not be obtained. At the same time, the efficiency of this algorithm is limited by the maximum parallax, in the actual scene with large maximum parallax and in the high-resolution image. In this paper, the disparity Prediction Model is introduced to solve the problems in the traditional tree-type stereo vision matching algorithm. DPM. A hierarchical acceleration algorithm for disparity prediction based on tree-type stereo vision matching is proposed. Disparity-Prediction-Based Hierarchical Accelerator. By predicting the parallax probability distribution, the algorithm constructs a more accurate confidence interval of the core parallax, which makes the calculation of matching cost, cost aggregation and parallax calculation. Parallax optimization only occurs in this core disparity confidence interval, thus reducing the time complexity of the tree matching algorithm. It includes laboratory images of Middlebury database and high-resolution images of actual scenes in KITTI database, as well as low-quality actual scene images taken independently. It is verified that the DPA acceleration algorithm can improve the matching accuracy and optimize the complexity of the tree-based matching algorithm. In this paper, there are three main contributions: 1. The concept of the core parallax confidence interval is proposed. For each pixel point, the core parallax confidence interval is proposed. The probability that the apparent difference is in the confidence range of the core parallax is much larger than that in the confidence interval. Through the accurate parallax confidence interval, the matching cost can be calculated. In the steps of parallax calculation and parallax repair, the computation amount is greatly reduced, and the running efficiency is improved. 2. A parallax distribution based DPM disparity prediction model is proposed. The model is based on the image pyramid structure. The probability distribution of large parallax is predicted by small parallax. And it is applied to construct the confidence interval of core parallax. 3. A universal DPA hierarchical acceleration algorithm for tree stereo vision matching is proposed. This algorithm can be combined into the existing framework of tree matching algorithm. The algorithm greatly improves the efficiency of the original tree matching algorithm. The higher the image resolution, the larger the parallax range, the more obvious the effect. Specifically, for high-resolution images, the DPA hierarchical acceleration algorithm is applied. Matching efficiency increases by 7-10 times.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41

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