Integration of optical flow and Multi-Path-Viterbi algorithm for stereo vision

This paper proposes a novel stereo matching algorithm to solve environment sensing problems. It integrates a non-convex optical flow and Viterbi process. The non-convex optical flow employs a new adaptive weighted non-convex Total Generalized Variation (TGV) model, which can obtain sharp disparity maps. Structural similarity, total variation constraint, and a specific merging strategy are combined with the 4 bi-directional Viterbi process to improve the robustness. In the fusion of the optical flow and Viterbi process, a new occlusion processing method is incorporated in order to get more sharp disparity and more robust result. Extensive experiments are conducted to compare this algorithm with other state-of-the-art methods. Experimental results show the superiority of our algorithm.

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STEREO MATCHING ALGORITHM BASED ON ILLUMINATION CONTROL TO IMPROVE THE ACCURACY

Image Analysis & Stereology ◽

10.5566/ias.1369 ◽

2016 ◽

Vol 35 (1) ◽

pp. 39 ◽

Cited By ~ 4

Author(s):

Rostam Affendi Hamzah ◽

Haidi Ibrahim ◽

Anwar Hasni Abu Hassan

Keyword(s):

Image Quality ◽

Stereo Vision ◽

Stereo Matching ◽

State Of The Art ◽

Experimental Results ◽

New Method ◽

Absolute Difference ◽

Matching Algorithm ◽

Disparity Maps

This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

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A New Basic Correlation Measurement for Stereo Matching

10.31219/osf.io/jqux2 ◽

2019 ◽

Author(s):

Bowen Shi ◽

Shan Shi ◽

Junhua Wu ◽

Musheng Chen

Keyword(s):

Time Complexity ◽

Stereo Matching ◽

Computation Method ◽

Local Correlation ◽

Matching Algorithm ◽

Cost Aggregation ◽

Image Patches ◽

Disparity Maps ◽

Aggregation Step ◽

The Cost

In this paper, we propose a new stereo matching algorithm to measure the correlation between two rectified image patches. The difficulty near objects' boundaries and textureless areas is a widely discussed issue in local correlation-based algorithms and most approaches focus on the cost aggregation step to solve the problem. We analyze the inherent limitations of sum of absolute differences (SAD) and sum of squared differences (SSD), then propose a new difference computation method to restrain the noise near objects' boundaries and enlarge the intensity variations in textureless areas. The proposed algorithm can effectively deal with the problems and generate more accurate disparity maps than SAD and SSD without time complexity increasing. Furthermore, proved by experiments, the algorithm can also be applied in some SAD-based and SSD-based algorithms to achieve better results than the original.

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Towards an Embedded Stereo Matching Algorithm Based on Multiple Correlation Windows

Proceedings of the 13th International Conference on Distributed Smart Cameras ◽

10.1145/3349801.3357128 ◽

2019 ◽

Author(s):

M. A. Palacios-Ramos ◽

H. D. Vázquez-Delgado ◽

Abiel Aguilar-González ◽

M. Pérez-Patricio

Keyword(s):

Stereo Matching ◽

Multiple Correlation ◽

Matching Algorithm

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

Remote Sensing ◽

10.3390/rs13020274 ◽

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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