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Stereo matching: Evaluation of three algorithms and two cost functions.

機(jī)譯：立體匹配：評(píng)估三種算法和兩種成本函數(shù)。

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摘要

The stereo matching or correspondence problem, which consists of finding the disparity map of a pair of stereo images, is an integral part of many computer vision techniques. For instance, Digital Elevation Model (DEM) generation from Synthetic Aperture Radar (SAR) images uses stereoscopy to deal with steep mountain regions that contain forests, and stereo matching is an integral part of the stereoscopy technique. Furthermore, automatic stereo processing, for which stereo matching is a critical component, is heavily used in obstacle detection and avoidance for unmanned vehicles and automated manufacturing processes, among many other applications. Stereo matching algorithms perform a combination of the following steps: cost computation, cost (support) aggregation, disparity computation/optimization, and disparity refinement. Depending upon how they compute/optimize the disparity of each pixel, they are classified as local or global algorithms. Due to its computational complexity, stereo matching is one of the most researched topics in computer vision.;This thesis performs a comparative performance study of six stereo matching codes. The codes employ (i) three different minimization algorithms, full version of graph cut, one iteration version of graph cut, and simulated annealing, and (ii) two cost functions, which are based on Absolute Difference (AD) and Normalized Cross Correlation (NCC). In addition, it includes the results of experiments that determine parameters of a cost function based on Zero Mean Normalized Cross Correlation (ZNCC). The execution time, final cost of minimization, output quality, and power and energy consumption are the performance metrics used in the evaluation. The results of the study show that the graph-cut stereo matching codes, in comparison to the simulated annealing codes, provide savings in execution time and energy consumption of up to 35%. In addition, it was discovered that by using a version of the graph-cut codes that performs a single alpha expansion (GC-1-Iter), the savings in execution time and energy increase up to 85%. Furthermore, the graph-cut codes provide better disparity maps, with up to 52% lower root mean square (RMS) errors than those produced by their simulated annealing counterparts. This demonstrates that the graph-cut stereo matching algorithm is promising for applications executed on field-deployable systems and other energy-constrained systems.
機(jī)譯：立體匹配或?qū)?yīng)問(wèn)題（包括查找一對(duì)立體圖像的視差圖）是許多計(jì)算機(jī)視覺(jué)技術(shù)不可或缺的一部分。例如，從合成孔徑雷達(dá)（SAR）圖像生成的數(shù)字高程模型（DEM）使用立體視覺(jué)處理包含森林的陡峭山區(qū)，并且立體匹配是立體視覺(jué)技術(shù)不可或缺的一部分。此外，立體匹配是關(guān)鍵組件的自動(dòng)立體處理在許多其他應(yīng)用中被大量用于障礙檢測(cè)和避免無(wú)人駕駛車(chē)輛和自動(dòng)化制造過(guò)程。立體匹配算法執(zhí)行以下步驟的組合：成本計(jì)算，成本（支持）匯總，視差計(jì)算/優(yōu)化和視差優(yōu)化。根據(jù)它們?nèi)绾斡?jì)算/優(yōu)化每個(gè)像素的視差，將它們分為局部算法或全局算法。由于立體匹配的計(jì)算復(fù)雜性，它是計(jì)算機(jī)視覺(jué)中研究最多的主題之一。本文對(duì)六個(gè)立體匹配代碼進(jìn)行了比較性能研究。這些代碼采用（i）三種不同的最小化算法，圖割的完整版本，圖割的一個(gè)迭代版本和模擬退火，以及（ii）兩個(gè)代價(jià)函數(shù)，它們基于絕對(duì)差（AD）和歸一化互相關(guān)（ NCC）。此外，它還包括基于零均值標(biāo)準(zhǔn)化互相關(guān)（ZNCC）確定成本函數(shù)參數(shù)的實(shí)驗(yàn)結(jié)果。執(zhí)行時(shí)間，最小化的最終成本，輸出質(zhì)量以及功耗和能耗是評(píng)估中使用的性能指標(biāo)。研究結(jié)果表明，與模擬退火代碼相比，圖形剪切立體匹配代碼可節(jié)省執(zhí)行時(shí)間，并節(jié)省多達(dá)35％的能耗。此外，還發(fā)現(xiàn)，通過(guò)使用執(zhí)行單個(gè)alpha擴(kuò)展的圖形切割代碼版本（GC-1-Iter），執(zhí)行時(shí)間和能源節(jié)省最多可提高85％。此外，圖割代碼可提供更好的視差圖，其均方根（RMS）誤差比其模擬退火副本產(chǎn)生的均方根誤差低52％。這證明了圖割立體匹配算法對(duì)于在可現(xiàn)場(chǎng)部署的系統(tǒng)和其他能耗受限的系統(tǒng)上執(zhí)行的應(yīng)用很有希望。

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  • 作者

    Jordan, Victor Jacob.;

  • 作者單位

    The University of Texas at El Paso.;

  • 授予單位 The University of Texas at El Paso.;
  • 學(xué)科 Computer Science.
  • 學(xué)位 M.S.
  • 年度 2012
  • 頁(yè)碼 50 p.
  • 總頁(yè)數(shù) 50
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類(lèi) 語(yǔ)言學(xué);
  • 關(guān)鍵詞

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