scholarly journals Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

2019 ◽  
Vol 15 (4) ◽  
pp. 450-462
Author(s):  
Deepambika V. A. ◽  
M. Abdul Rahiman
Keyword(s):  
Real Time ◽  
Disparity Estimation ◽  
Stereo Correspondence

Trinocular adaptive window size disparity estimation algorithm and its real-time hardware

2015 ◽  
Author(s):  
Abdulkadir Akin ◽  
Raffaele Capoccia ◽  
Jonathan Narinx ◽  
Ipek Baz ◽  
Alexandre Schmid ◽  
...  
Keyword(s):  
Real Time ◽  
Window Size ◽  
Estimation Algorithm ◽  
Disparity Estimation ◽  
Adaptive Window ◽  
Size Disparity

scholarly journals Stereo correspondence using an assisted discrete cosine transform method.

2021 ◽  
Author(s):  
Edward Rosales
Keyword(s):  
Discrete Cosine Transform ◽  
Error Rates ◽  
Optimization Techniques ◽  
Disparity Estimation ◽  
Computational Time ◽  
Stereo Correspondence ◽  
Disparity Map ◽  
Cosine Transform ◽  
Short Period ◽  
The Common

Many approaches have been taken towards the development of a compliant stereo correspondence algorithm that is capable of producing accurate disparity maps within a short period of time. There has been great progress over the past decade due to the vast increase in optimization techniques. Currently, the most successful algorithms contain explicit assumptions of the real world such as definitive differences in disparity among objects and constant textures within objects. This thesis starts by giving a brief description of disparity, along with descriptions of some common applications. Next, it explores various methods used in common stereo correspondence algorithms, as well as gives an in depth description and analysis of top performing algorithms. These algorithms are later used to compare with the proposed algorithm. In the proposed algorithm, frequency stereo correspondence in parallel with the traditional color intensity stereo correspondence is used to develop an initial disparity map. Frequency stereo correspondence is achieved using a winner-take-all block based Discrete Cosine Transform (DCT) to find the largest frequency components as well as their positions to use in disparity estimation. The proposed algorithm uses methods that are computationally inexpensive to reduce the computational time that plagues many of the common stereo correspondence algorithms. The proposed algorithm achieves an average correct disparity rate of 95.3%. This results in a disparity error rate of 4.07% compared to the top performing algorithms in the Middlebury website [1]; the DoubleBP, CoopRegion, AdaptingBP, and ADCensus algorithms that have error rates of 4.19%, 4.41%, 4.23%, and 3.97%, respectively. Additionally, experimental results demonstrate that the proposed algorithm is computationally efficient and significantly reduces the processing time that plagues many of the common stereo correspondence algorithms.

scholarly journals Improving stereo matching algorithm with adaptive cross-scale cost aggregation

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775154 ◽  
Author(s):  
Cong Bai ◽  
Qing Ma ◽  
Pengyi Hao ◽  
Zhi Liu ◽  
Jinglin Zhang
Keyword(s):  
Stereo Matching ◽  
Disparity Estimation ◽  
Data Sets ◽  
Human Beings ◽  
Stereo Correspondence ◽  
Matching Accuracy ◽  
Automatic Driving ◽  
Cost Aggregation ◽  
Correspondence Matching ◽  
Consistency Constraints

Human beings process stereoscopic correspondence across multiple purposes like robot navigation, automatic driving, and virtual or augmented reality. However, this bioinspiration is ignored by state-of-the-art dense stereo correspondence matching methods. Cost aggregation is one of the critical steps in the stereo matching method. In this article, we propose an optimized cross-scale cost aggregation scheme with coarse-to-fine strategy for stereo matching. This scheme implements cross-scale cost aggregation with the smoothness constraint on neighborhood cost, which essentially extends the idea of the inter-scale and intra-scale consistency constraints to increase the matching accuracy. The neighborhood costs are not only used in the intra-scale consistency to ensure that the regularized costs vary smoothly in an eight-connected neighbors region but also incorporated with inter-scale consistency to optimize the disparity estimation. Additionally, the improved method introduces an adaptive scheme in each scale with different aggregation methods. Finally, experimental results evaluated both on classic Middlebury and Middlebury 2014 data sets show that the proposed method performs better than the cross-scale cost aggregation. The whole stereo correspondence algorithm achieves competitive performance in terms of both matching accuracy and computational efficiency. An extensive comparison, including the KITTI benchmark, illustrates the better performance of the proposed method also.

Sign in / Sign up

Export Citation Format

Share Document