Estimating the minimum redundancy in stereo image pair

2008 ◽  
Vol 26 (5) ◽  
pp. 725-730 ◽  
Author(s):  
Wang Yuanqing
Keyword(s):  
Stereo Image ◽  
Image Pair

A Fast Binocular Vision Stereo Matching Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3735-3738
Author(s):  
Hui Zhang ◽  
Ling Tao Zhang ◽  
Yi Ren
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Stereo Image ◽  
Feature Points ◽  
Image Pair ◽  
Canny Operator ◽  
Canny Edge Detection ◽  
Matching Algorithm ◽  
Canny Edge ◽  
Image Edge

In this paper we present a fast indoor stereo matching algorithm based on canny edge detection and line moments. We first detect image edge by using Canny operator, then find the target objects according line moments, the feature points of the objects’ contours are extracted. Finally, matching the pixel in stereo image pair according the angle vector. The algorithm effectively reduces the computational complexity, computational cost is decreased greatly. The experimental results show that the algorithm is possible and valid.

scholarly journals Optics system design applying a micro-prism array of a single lens stereo image pair

2008 ◽  
Vol 16 (20) ◽  
pp. 15495 ◽  
Author(s):  
Chien-Yue Chen ◽  
Ting-Ting Yang ◽  
Wen - Shing Sun
Keyword(s):  
System Design ◽  
Stereo Image ◽  
Image Pair ◽  
Single Lens

scholarly journals Evaluation of Stereo Images Matching

2021 ◽  
Vol 318 ◽  
pp. 04002
Author(s):  
Ali Hasan Hadi ◽  
Abbas Zedan Khalaf
Keyword(s):  
Image Matching ◽  
Stereo Image ◽  
Mean Square ◽  
Image Pair ◽  
Stereo Images ◽  
Minimum Value ◽  
Local Point ◽  
Feature Based ◽  
High Degree ◽  
Matching Techniques

Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an evaluation of implementing manual as well as auto-match between a pair of images that acquired with an overlapped area. Particular target points are selected to be matched manually (22 target points). Auto-matching, based on feature-based matching (FBM) method, has been applied to these target points by using BRISK, FAST, Harris, and MinEigen algorithms. Auto matching is conducted with two main phases: extraction (detection and description) and matching features. The matching techniques used by the prevalent algorithms depend on local point (corner) features. Also, the performance of the algorithms is assessed according to the results obtained from various criteria, such as the number of auto-matched points and the target points that auto-matched. This study aims to determine and evaluate the total root mean square error (RMSE) by comparing coordinates of manual matched target points with those obtained from auto-matching by each of the algorithms. According to the experimental results, the BRISK algorithm gives the higher number of auto-matched points, which equals 2942, while the Harris algorithm gives 378 points representing the lowest number of auto-matched points. All target points are auto-matched with BRISK and FAST algorithms, while 3 and 9 target points only auto-matched with Harris and MinEigen algorithms, respectively. Total RMSE in its minimum value is given by FAST and manual match in the first image, it is 0.002651206 mm, and Harris and manual match provide the minimum value of total RMSE in the second image is 0.002399477 mm.

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