本文選題：局部立體匹配 + 圖像分割��； 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：雙目立體視覺是計算機(jī)視覺的重要研究方向,利用雙目立體視覺可以獲取三維場景的深度信息,已經(jīng)廣泛應(yīng)用在軍用與民用的各個領(lǐng)域。雙目立體視覺的關(guān)鍵在于立體匹配,因此成為目前該領(lǐng)域的主要研究內(nèi)容。本文重點(diǎn)研究了如何提高局部立體匹配算法在深度不連續(xù)區(qū)域以及低紋理區(qū)域的匹配精度,同時搭建了基于FPGA的立體匹配以及顯著目標(biāo)測距系統(tǒng)。研究了基于圖像分割的局部立體匹配算法,針對算法在深度不連續(xù)區(qū)域的匹配精度不高以及對亮度差異敏感等缺陷,提出并研究了一種基于非參數(shù)變換和圖像分割的高效聚合立體匹配算法。該算法以分割塊作為支持窗口,以Census變換后的序列值作為匹配基元,采用本文提出的匹配代價函數(shù)進(jìn)行代價計算。同時提出了一種動態(tài)視差范圍矯正的技術(shù),以鄰域像素視差值為參考對當(dāng)前像素的視差搜索范圍進(jìn)行調(diào)整,提高匹配效率。實(shí)驗結(jié)果證明該算法能有效解決深度不連續(xù)區(qū)域的誤匹配問題。研究了基于低紋理區(qū)域的局部立體匹配算法,針對低紋理區(qū)域缺少特征差異性,提出并研究了一種基于像素顏色空間和窗口位置的立體匹配算法。該算法首先對圖像中的低紋理區(qū)域進(jìn)行檢測,然后采用本文提出的基于像素顏色空間的匹配代價函數(shù)進(jìn)行代價計算,最后根據(jù)像素在聚合窗口中的不同位置分配相應(yīng)的權(quán)值并將代價值聚合起來。實(shí)驗結(jié)果證明該算法可以有效提高低紋理區(qū)域的匹配精度。搭建了一套以FPGA為圖像處理核心的雙目立體視覺系統(tǒng)。利用FPGA內(nèi)部的并行計算和流水線設(shè)計,探索并設(shè)計了一種基于Box濾波的方法來實(shí)現(xiàn)Census變換;探索并設(shè)計實(shí)現(xiàn)了通過圖像銳化和閾值分割來提取紅外圖像中的顯著目標(biāo)以及測距;設(shè)計實(shí)現(xiàn)了對左右視頻流進(jìn)行立體顯示。實(shí)驗結(jié)果表明,該硬件系統(tǒng)能夠?qū)崟r對圖像進(jìn)行立體匹配和顯著目標(biāo)提取,近距離的測距精度較高,系統(tǒng)的穩(wěn)定性較好。
[Abstract]:Binocular stereo vision is an important research direction of computer vision. Using binocular stereo vision to obtain depth information of 3D scene has been widely used in military and civil fields. Stereo matching is the key of binocular stereo vision, so it has become the main research content in this field. This paper focuses on how to improve the matching accuracy of local stereo matching algorithm in deep discontinuous region and low texture region. At the same time, a stereo matching system based on FPGA is built. The local stereo matching algorithm based on image segmentation is studied. An efficient aggregate stereo matching algorithm based on nonparametric transformation and image segmentation is proposed and studied. The algorithm takes the partition block as the supporting window and the sequence value of Census transform as the matching primitive. The proposed matching cost function is used to calculate the cost. At the same time, a technique of dynamic disparity range correction is proposed, in which the parallax range of the current pixel is adjusted to improve the matching efficiency by using the parallax value of neighboring pixels as a reference. Experimental results show that the algorithm can effectively solve the problem of mismatch in depth discontinuous region. The local stereo matching algorithm based on low texture region is studied, and a stereo matching algorithm based on pixel color space and window position is proposed and studied for the lack of feature difference in low texture region. The algorithm firstly detects the low-texture region in the image, and then calculates the cost by using the matching cost function based on the pixel color space proposed in this paper. Finally, the corresponding weights are assigned according to the different positions of pixels in the aggregation window and the generation values are aggregated. Experimental results show that the algorithm can effectively improve the matching accuracy of low texture regions. A binocular stereo vision system with FPGA as the core of image processing is set up. Using the parallel computing and pipeline design in FPGA, this paper explores and designs a method based on Box filtering to realize the Census transform, and explores and designs to extract the prominent targets and ranging from infrared images by image sharpening and threshold segmentation. The stereoscopic display of left and right video streams is designed and realized. The experimental results show that the hardware system can carry out stereo matching and salient target extraction in real time, and the precision of ranging in close range is higher, and the stability of the system is better.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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