Contribution towards a fast stereo dense matching.

Stereo matching is important in the area of computer vision as it is the basis of the reconstruction process. Many applications require 3D reconstruction such as view synthesis, robotics... The main task of matching uncalibrated images is to determine the corresponding pixels and other features where the motion between these images and the camera parameters is unknown. Although some methods have been carried out over the past two decades on the matching problem, most of these methods are not practical and difficult to implement. Our approach considers a reliable image edge features in order to develop a fast and practical method. Therefore, we propose a fast stereo matching algorithm combining two different approaches for matching as the image is segmented into two sets of regions: edge regions and non-edge regions. We have used an algebraic method that preserves disparity continuity at the object continuous surfaces. Our results demonstrate that we gain a speed dense matching while the implementation is kept simple and straightforward.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Z42. Source: Masters Abstracts International, Volume: 44-03, page: 1420. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Stereo matching Using Edge information and a Genetic algorithm

본 논문은 경계선 정보와 유전 알고리즘을 이용한 스테레오 정합 방법을 제안하고 있다. 제안한 방법은 조밀한 기준영상의 경계선을 검출하기 위해 LoG 연산자를 사용하고 이를 이용해 동일한 변위(disparity)값을 갖는 개체의 영역과 염색체의 구조를 정의하였다. 그리고 스테레오 정합문제를 최적화 문제로 간주하고 최적화 문제 해결에 좋은 성능을 보이는 유전 알고리즘을 스테레오 정합 환경에 맞게 변형하여 적용하였다. 제안한 방법은 경계선 정보를 이용하여 개체의 영역을 쉽게 추정할 수 있었고 이를 통해 경계선 부근에서 오정합을 발생시키는 기존의 영역기반 정합의 단점을 보완할 수 있었다. 그리고 검출한 경계선 정보로 염색체의 구조를 영상에 따라 적응적으로 정의할 수 있기 때문에 유전 알고리즘을 적용하기가 용이했다. 정합비용함수를 영역기반 정합의 정합비용함수와 변위 평활성, 변위 순서성의 조합으로 정의하고 유전 알고리즘의 목적함수로 사용하여 진화를 통해 효율적인 정합이 되도록 했다. 제안한 방법에서 기존의 특징기반 정합보다 정확도가 향상되고, 영역기반 정합보다 경계선 부분의 변위가 개선되는 결과를 볼 수 있었다. 실험에서 기존의 다른 방법들과 비교하여 제안한 방법이 우수함을 보였다.1. 서 론 ................................................................ 1 2. 스테레오 시각과 유전 알고리즘................................ 4 2.1. 스테레오 시각.................................................. 4 2.2. 스테레오 정합.................................................. 7 2.3. 유전 알고리즘.................................................. 11 3. 경계선 정보와 유전 알고리즘을 이용한 스테레오 정합.. 16 3.1. 경계선 정보에 의한 염색체 구조 정의................... 16 3.2. 유전 알고리즘을 이용한 스테레오 정합................. 20 3.2.1. 염색체 표현 및 집단의 초기화........... 20 3.2.2. 유전 연산자................................... 22 3.2.3. 목적 함수 및 적합도........................ 28 4. 실험 및 고찰.......................................................... 32 4.1. 합성 영상 실험.................................................. 33 4.2. 실제 영상 실험.................................................. 44 5. 결 론................................................................... 48 참고 문헌 ................................................................. 5

Miniaturized embedded stereo vision system (MESVS)

Stereo vision is one of the fundamental problems of computer vision. It is also one of the oldest and heavily investigated areas of 3D vision. Recent advances of stereo matching methodologies and availability of high performance and efficient algorithms along with availability of fast and affordable hardware technology, have allowed researchers to develop several stereo vision systems capable of operating at real-time. Although a multitude of such systems exist in the literature, the majority of them concentrates only on raw performance and quality rather than factors such as dimension, and power requirement, which are of significant importance in the embedded settings. In this thesis a new miniaturized embedded stereo vision system (MESVS) is presented, which is miniaturized to fit within a package of 5x5cm, is power efficient, and cost-effective. Furthermore, through application of embedded programming techniques and careful optimization, MESVS achieves the real-time performance of 20 frames per second. This work discusses the various challenges involved regarding design and implementation of this system and the measures taken to tackle them

MRF Stereo Matching with Statistical Estimation of Parameters

For about the last ten years, stereo matching in computer vision has been treated as a combinatorial optimization problem. Assuming that the points in stereo images form a Markov Random Field (MRF), a variety of combinatorial optimization algorithms has been developed to optimize their underlying cost functions. In many of these algorithms, the MRF parameters of the cost functions have often been manually tuned or heuristically determined for achieving good performance results. Recently, several algorithms for statistical, hence, automatic estimation of the parameters have been published. Overall, these algorithms perform well in labeling, but they lack in performance for handling discontinuity in labeling along the surface borders. In this dissertation, we develop an algorithm for optimization of the cost function with automatic estimation of the MRF parameters – the data and smoothness parameters. Both the parameters are estimated statistically and applied in the cost function with support of adaptive neighborhood defined based on color similarity. With the proposed algorithm, discontinuity handling with higher consistency than of the existing algorithms is achieved along surface borders. The data parameters are pre-estimated from one of the stereo images by applying a hypothesis, called noise equivalence hypothesis, to eliminate interdependency between the estimations of the data and smoothness parameters. The smoothness parameters are estimated applying a combination of maximum likelihood and disparity gradient constraint, to eliminate nested inference for the estimation. The parameters for handling discontinuities in data and smoothness are defined statistically as well. We model cost functions to match the images symmetrically for improved matching performance and also to detect occlusions. Finally, we fill the occlusions in the disparity map by applying several existing and proposed algorithms and show that our best proposed segmentation based least squares algorithm performs better than the existing algorithms. We conduct experiments with the proposed algorithm on publicly available ground truth test datasets provided by the Middlebury College. Experiments show that results better than the existing algorithms’ are delivered by the proposed algorithm having the MRF parameters estimated automatically. In addition, applying the parameter estimation technique in existing stereo matching algorithm, we observe significant improvement in computational time