Using Image Pair Method to Share Stereo Images through Network

2011 ◽  
Author(s):  
Huang Jie ◽  
Wu Pingdong
Keyword(s):  
Image Pair ◽  
Stereo Images ◽  
Pair Method

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.

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.

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.

Stereo imaging in the shadow electron microscope

1978 ◽  
Vol 36 (1) ◽  
pp. 120-121
Author(s):  
W. C. T. Dowell
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Convenient Method ◽  
Patent Application ◽  
Standard Technique ◽  
Stereo Imaging ◽  
Stereo Images ◽  
Specimen Height

Stereo imaging is not new to electron microscopy. Von Ardenne, who first published transmission pairs nearly forty hears ago, himself refers to a patent application by Ruska in 1934. In the early days of the electron microscope von Ardenne employed a pair of magnetic lenses to view untilted specimens but soon opted for the now standard technique of tilting the specimen with respect to the beam.In the shadow electron microscope stereo images can, of course, be obtained by tilting the specimen between micrographs. This obvious method suffers from the disadvantage that the magnification is very sensitive to small changes in specimen height which accompany tilting in the less sophisticated stages and it is also time consuming. A more convenient method is provided by horizontally displacing the specimen between micrographs. The specimen is not tilted and the technique is both simple and rapid, stereo pairs being obtained in less than thirty seconds.

scholarly journals Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation

2021 ◽  
pp. 1-1
Author(s):  
Rui Fan ◽  
Hengli Wang ◽  
Peide Cai ◽  
Jin Wu ◽  
Junaid Bocus ◽  
...  
Keyword(s):  
Free Space ◽  
Data Augmentation ◽  
Stereo Images

scholarly journals Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Least Square ◽  
Affine Invariant ◽  
Stereo Images ◽  
Distance Ratio ◽  
Matching Algorithm ◽  
End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

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