STEREO MATCHING ALGORITHMS BASED ON FUZZY APPROACH

2002 ◽  
Vol 16 (07) ◽  
pp. 883-899 ◽  
Author(s):  
S. SRINIVAS KUMAR ◽  
B. N. CHATTERJI
Keyword(s):  
Cross Correlation ◽  
Stereo Matching ◽  
Similarity Measures ◽  
Stereo Image ◽  
Image Pair ◽  
Fuzzy Relations ◽  
Stereo Images ◽  
Normalized Cross Correlation ◽  
Fuzzy Aggregation ◽  
The Right

Stereo matching is the central problem of stereovision paradigm. Area-based techniques provide the dense disparity maps and hence they are preferred for stereo correspondence. Normalized cross correlation (NCC), sum of squared differences (SSD) and sum of absolute differences (SAD) are the linear correlation measures generally used in the area-based techniques for stereo matching. In this paper, similarity measure for stereo matching based on fuzzy relations is used to establish the correspondence in the presence of intensity variations in stereo images. The strength of relationship of fuzzified data of two windows in the left image and the right image of stereo image pair is determined by considering the appropriate fuzzy aggregation operators. However, these measures fail to establish correspondence of the pixels in the stereo images in the presence of occluded pixels in the corresponding windows. Another stereo matching algorithm based on fuzzy relations of fuzzy data is used for stereo matching in such regions of images. This algorithm is based on weighted normalized cross correlation (WNCC) of the intensity data in the left and the right windows of stereo image pair. The properties of the similarity measures used in these algorithms are also discussed. Experiments with various real stereo images prove the superiority of these algorithms over normalized cross correlation (NCC) under nonideal conditions.

scholarly journals Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Viral H. Borisagar ◽  
Mukesh A. Zaveri
Keyword(s):  
Dynamic Programming ◽  
Stereo Matching ◽  
Stereo Image ◽  
Stereo Pair ◽  
Image Pair ◽  
Stereo Images ◽  
Disparity Map ◽  
Illumination Variation ◽  
Matching Problem ◽  
Wide Range

A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair.

scholarly journals Stereo Matching Methods for Imperfectly Rectified Stereo Images

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 570
Author(s):  
Phuc Nguyen Hong ◽  
Chang Wook Ahn
Keyword(s):  
Cross Correlation ◽  
Stereo Matching ◽  
State Of The Art ◽  
Depth Information ◽  
Important Process ◽  
Stereo Images ◽  
Illumination Variation ◽  
Matching Methods ◽  
Normalized Cross Correlation ◽  
Image Problems

Stereo matching has been under development for decades and is an important process for many applications. Difficulties in stereo matching include textureless regions, occlusion, illumination variation, the fattening effect, and discontinuity. These challenges are effectively solved in recently developed stereo matching algorithms. A new imperfect rectification problem has recently been encountered in stereo matching, and the problem results from the high resolution of stereo images. State-of-the-art stereo matching algorithms fail to exactly reconstruct the depth information using stereo images with imperfect rectification, as the imperfectly rectified image problems are not explicitly taken into account. In this paper, we solve the imperfect rectification problems, and propose matching stereo matching methods that based on absolute differences, square differences, normalized cross correlation, zero-mean normalized cross correlation, and rank and census transforms. Finally, we conduct experiments to evaluate these stereo matching methods using the Middlebury datasets. The experimental results show the proposed stereo matching methods can reduce error rate significantly for stereo images with imperfect rectification.

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 Adaptive Fine Distortion Correction Method for Stereo Images of Skin Acquired with a Mobile Phone

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4492
Author(s):  
Cho-I Moon ◽  
Onseok Lee
Keyword(s):  
Mobile Phone ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Skin Surface ◽  
Correction Method ◽  
Distortion Correction ◽  
Stereo Image ◽  
3D Analysis ◽  
Stereo Images ◽  
3D Information

With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images.

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