Evaluation of confidence-driven cost aggregation strategies

In this thesis I describe eight new stereo matching algorithms that perform the cost-aggregation step using a guided filter with a confidence map as guidance image, and share the structure of a linear stereo matching algorithm. The results of the execution of the proposed algorithms on four pictures from the Middlebury dataset are shown as well. Finally, based on these results, a ranking of the proposed algorithms is presented

A New Approach for Stereo Matching Algorithm with Dynamic Programming

Stereo matching algorithms are one of heavily researched topic in binocular stereo vision. Massive 3D information can be obtained by finding correct correspondence of different points between images captured from different views. Development of stereo matching algorithm is done for obtaining disparity maps i.e. depth information. When disparities computed for scan lines then dense reconstruction becomes time consuming for vision navigation systems. So for pair of stereo images proposed method extracts features points those are at contours in images and then a dynamic program is used to find the corresponding points from each image and calculates disparities. Also to reduce the noise which may lead to incorrect results in stereo correspondence, a new stereo matching algorithm based on the dynamic programming is proposed. Generally dynamic programming finds the global minimum for independent scan lines in polynomial time. While efficient, its performance is far from desired one because vertical consistency between scan lines is not enforced. This method review the use of dynamic programming for stereo correspondence by applying it to a contour instead to individual scan lines. Proposed methodology will obtain the global minimum for contour array in linear time using Longest Common Subsequent (LCS) dynamic programming method with no disparity space image (DSI). DOI: 10.17762/ijritcc2321-8169.15025

Optimized fixed point implementation of a local stereo matching algorithm onto C66x DSP

International audienceStereo matching techniques aim at reconstructing disparity maps from a pair of images. The use of stereo matching techniques in embedded systems is very challenging due to the complexity of the state-of-the-art algorithms. An efficient local stereo matching algorithm has been chosen from the literature and implemented on a c6678 DSP. Arithmetic simplifications such as approximation by piecewise linear functions and fixed point conversions are proposed. Thanks to factorisation and pre-computing, the memory footprint is reduced by a factor 13 to fit on the memory footprint available on embedded systems. A 14.5 fps speed (factor 60 speed-up) has been reached with a small quality loss on the final disparity map

Efficient 3D stereo vision stabilization for multi-camera viewpoints

In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Depth Recovery of Complex Surfaces from Texture-less Pairs of Stereo Images

In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT indexes are defined by means of disparity between the matching invariant features in rectified stereo images. The integration process is based on correcting the 3D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework