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 Detection of False Synchronization of Stereo Image Transmission Using a Convolutional Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 78
Author(s):  
Joanna Kulawik ◽  
Mariusz Kubanek
Keyword(s):  
Image Transmission ◽  
Stereo Image ◽  
Correct Classification ◽  
Stereo Images ◽  
Synchronization Problem ◽  
Average Probability ◽  
Degree Of Certainty ◽  
The Subject ◽  
The Right ◽  
High Degree

The subject of the work described in this article is the detection of false synchronization in the transmission of digital stereo images. Until now, the synchronization problem was solved by using start triggers in the recording. Our proposal checks the discrepancy between the received pairs of images, which allows you to detect delays in transferring images between the left camera and the right camera. For this purpose, a deep network is used to classify the analyzed pairs of images into five classes: MuchFaster, Faster, Regular, Slower, and MuchSlower. As can be seen as a result of the conducted work, satisfactory research results were obtained as the correct classification. A high percentage of average probability in individual classes also indicates a high degree of certainty as to the correctness of the results. An author’s base of colorful stereo images in the number of 3070 pairs is used for the research.

scholarly journals Review of Wide-Baseline Stereo Image Matching Based on Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3247
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Fei Meng ◽  
Li Zhang
Keyword(s):  
Deep Learning ◽  
Image Matching ◽  
Feature Detection ◽  
State Of The Art ◽  
Stereo Image ◽  
Stereo Images ◽  
Deep Convolutional Neural Networks ◽  
Advantages And Disadvantages ◽  
Wide Baseline Stereo ◽  
Baseline Image

Strong geometric and radiometric distortions often exist in optical wide-baseline stereo images, and some local regions can include surface discontinuities and occlusions. Digital photogrammetry and computer vision researchers have focused on automatic matching for such images. Deep convolutional neural networks, which can express high-level features and their correlation, have received increasing attention for the task of wide-baseline image matching, and learning-based methods have the potential to surpass methods based on handcrafted features. Therefore, we focus on the dynamic study of wide-baseline image matching and review the main approaches of learning-based feature detection, description, and end-to-end image matching. Moreover, we summarize the current representative research using stepwise inspection and dissection. We present the results of comprehensive experiments on actual wide-baseline stereo images, which we use to contrast and discuss the advantages and disadvantages of several state-of-the-art deep-learning algorithms. Finally, we conclude with a description of the state-of-the-art methods and forecast developing trends with unresolved challenges, providing a guide for future work.

scholarly journals ACCURACY IMPROVEMENT BY THE LEAST SQUARES IMAGE MATCHING EVALUATED ON THE CARTOSAT-1

2015 ◽  
Vol XL-1-W5 ◽  
pp. 11-14 ◽  
Author(s):  
H. Afsharnia ◽  
A. Azizi ◽  
H. Arefi
Keyword(s):  
High Resolution ◽  
Least Squares ◽  
Optical Sensors ◽  
Image Matching ◽  
Satellite Images ◽  
Functional Model ◽  
Stereo Image ◽  
Optical Data ◽  
High Resolution Satellite Images ◽  
Matching Techniques

Generating accurate elevation data from satellite images is a prerequisite step for applications that involve disaster forecasting and management using GIS platforms. In this respect, the high resolution satellite optical sensors may be regarded as one of the prime and valuable sources for generating accurate and updated elevation information. However, one of the main drawbacks of conventional approaches for automatic elevation generation from these satellite optical data using image matching techniques is the lack of flexibility in the image matching functional models to take dynamically into account the geometric and radiometric dissimilarities between the homologue stereo image points. The classical least squares image matching (LSM) method, on the other hand, is quite flexible in incorporating the geometric and radiometric variations of image pairs into its functional model. The main objective of this paper is to evaluate and compare the potential of the LSM technique for generating disparity maps from high resolution satellite images to achieve sub pixel precision. To evaluate the rate of success of the LSM, the size of the y-disparities between the homologous points is taken as the precision criteria. The evaluation is performed on the Cartosat-1 stereo along track images over a highly mountainous terrain. The precision improvement is judged based on the standard deviation and the scatter pattern of the y-disparity data. The analysis of the results indicate that, the LSM has achieved the matching precision of about 0.18 pixels which is clearly superior to the manual pointing that yielded the precision of 0.37 pixels.

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.

scholarly journals A comparison of semiglobal and local dense matching algorithms for surface reconstruction

2014 ◽  
Vol XL-5 ◽  
pp. 187-194 ◽  
Author(s):  
E. Dall'Asta ◽  
R. Roncella
Keyword(s):  
Computer Vision ◽  
Stereo Matching ◽  
Least Square ◽  
Stereo Image ◽  
Dense Matching ◽  
Consistency Constraints ◽  
Global Matching ◽  
Matching Cost ◽  
Software Codes ◽  
Matching Techniques

Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision.<br><br> The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed.<br><br> The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.

scholarly journals Taylor expansion based stereo image reversible data hiding

2020 ◽  
Author(s):  
Xinyang Ying ◽  
Guobing Zhou
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Taylor Expansion ◽  
Stereo Image ◽  
Embedding Capacity ◽  
Stereo Images ◽  
Secret Data ◽  
Stego Image ◽  
Multi Level

Abstract The reversible data hiding allows original image to be completely recovered from the stego image when the secret data has been extracted, it is has drawn a lot of attentions from researchers. In this paper, a novel Taylor Expansion (TE) based stereo image reversible data hiding method is presented. Since the prediction accuracy is essential to the data hiding performance, a novel TE based predictor using correlations of two views of the stereo image is proposed. TE can fully exploit strong relationships between matched pixels in the stereo image so that the accuracy of the prediction can be improved. Then, histogram shifting is utilized to embed data to decrease distortion of stereo images, and multi-level hiding can increase embedding capacity. Experimental results show that the proposed method is superior to some existing data hiding methods considering embedding capacity and the quality of the stego stereo images.

scholarly journals Constructing Stereo Presentations of Textual Information on an Autostereoscopic Monitor

2019 ◽  
Author(s):  
Сергей Андреев ◽  
Sergey Andreev ◽  
Николь Бондарева ◽  
Nicole Bondareva
Keyword(s):  
Numerical Simulation ◽  
Scientific Research ◽  
Practical Experience ◽  
Stereo Image ◽  
Stereo Images ◽  
General Study ◽  
Textual Information ◽  
Stereoscopic Images ◽  
Depth Maps

This paper presents practical experience in constructing stereo presentations of texts and formulas on an autostereoscopic monitor in stereo presentations designed to display the results of numerical simulation. The task of constructing stereo images of texts and formulas is a structural subtask of a general study devoted to the development of methods and algorithms for constructing stereo presentations of the results of scientific research. This paper discusses the construction of stereoscopic images on an autostereoscopic monitor. The autostereoscopic monitor allows one to observe a stereo image without glasses, while ensuring the quality of the stereo image, which is not inferior to the quality of the stereo image, presented using a classic 3D projection stereo system. Various methods of obtaining stereo images supported by the monitor were tested, namely, the multi-view presentation of the object and the construction of depth maps. The results for both methods are presented.

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