A novel stereo pair-coding algorithm based on hybrid block matching disparity estimation

2003 ◽  
Author(s):  
Jungong Han ◽  
Zhaoyang Lu
Keyword(s):  
Block Matching ◽  
Disparity Estimation ◽  
Stereo Pair

Depth Estimation for HDR Images

2012 ◽  
pp. 165-180
Author(s):  
S. Manikandan
Keyword(s):  
Dynamic Range ◽  
Depth Estimation ◽  
High Dynamic Range ◽  
Block Matching ◽  
Absolute Difference ◽  
Mapping Technique ◽  
Stereo Pair ◽  
Block Matching Algorithm ◽  
High Dynamic ◽  
Low Dynamic Range

In this chapter, depth estimation for stereo pair of High Dynamic Range (HDR) images is proposed. The proposed algorithm consists of two major techniques namely conversion of HDR images to Low Dynamic Range (LDR) images or Standard Dynamic Range (SDR) images and estimating the depth from the converted LDR / SDR stereo images. Local based tone mapping technique is used for the conversion of the HDR images to SDR images. And the depth estimation is done based on the corner features of the stereo pair images and block matching algorithm. Computationally much less expensive cost functions Mean Square Error (MSE) or Mean Absolute Difference (MAD) can be used for block matching algorithms. The proposed algorithm is explained with illustrations and results.

Motion Estimation Based on Adaptive Kalman Filter and Its Application in Video Coding

2011 ◽  
Vol 58-60 ◽  
pp. 2546-2551
Author(s):  
Cheng Guo ◽  
Hao Qian Wang
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
Moving Average ◽  
Arma Model ◽  
Block Matching ◽  
Disparity Estimation ◽  
Autoregressive Moving Average ◽  
Moving Average Process ◽  
Adaptive Kalman Filter ◽  
Filtering Algorithm

Most of the traditional block-matching algorithms for motion estimation (ME) can only yield local optimal motion vectors (MVs). In this paper, the autoregressive moving average process (ARMA) model is selected to formulate the correlation of neighboring blocks in a frame, and the adaptive Kalman filtering algorithm is applied to refine the MVs. The horizontal and vertical ARMA models are constructed to utilize the filtering algorithm twice to get a better performance. Our method can also be extended to realize disparity estimation (DE) in order to apply it in a multi-view video coding (MVC) system. The experiment results show the effectiveness of our method to improve the accuracy of conventional fast block matching algorithms.

scholarly journals 2D Sub-Pixel Disparity Measurement Using QPEC / Medicis

2016 ◽  
Vol XLI-B1 ◽  
pp. 291-298
Author(s):  
M. Cournet ◽  
A. Giros ◽  
L. Dumas ◽  
J. M. Delvit ◽  
D. Greslou ◽  
...  
Keyword(s):  
Earth Observation ◽  
Block Matching ◽  
Disparity Estimation ◽  
Estimation Methods ◽  
Two Dimensional ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Statistical Measures ◽  
Digital Elevation

In the frame of its earth observation missions, CNES created a library called QPEC, and one of its launcher called Medicis. QPEC / Medicis is a sub-pixel two-dimensional stereo matching algorithm that works on an image pair. This tool is a block matching algorithm, which means that it is based on a local method. Moreover it does not regularize the results found. It proposes several matching costs, such as the Zero mean Normalised Cross-Correlation or statistical measures (the Mutual Information being one of them), and different match validation flags. QPEC / Medicis is able to compute a two-dimensional dense disparity map with a subpixel precision. Hence, it is more versatile than disparity estimation methods found in computer vision literature, which often assume an epipolar geometry. <br><br> CNES uses Medicis, among other applications, during the in-orbit image quality commissioning of earth observation satellites. For instance the Pléiades-HR 1A & 1B and the Sentinel-2 geometric calibrations are based on this block matching algorithm. Over the years, it has become a common tool in ground segments for in-flight monitoring purposes. For these two kinds of applications, the two-dimensional search and the local sub-pixel measure without regularization can be essential. This tool is also used to generate automatic digital elevation models, for which it was not initially dedicated. <br><br> This paper deals with the QPEC / Medicis algorithm. It also presents some of its CNES applications (in-orbit commissioning, in flight monitoring or digital elevation model generation). Medicis software is distributed outside the CNES as well. This paper finally describes some of these external applications using Medicis, such as ground displacement measurement, or intra-oral scanner in the dental domain.

Evaluation of multiresolution block matching techniques for motion and disparity estimation

1994 ◽  
Vol 6 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Dimitrios Tzovaras ◽  
Michael G. Strintzis ◽  
Haralambos Sahinoglou
Keyword(s):  
Block Matching ◽  
Disparity Estimation ◽  
Matching Techniques

scholarly journals A Novel Stereo Matching Algorithm for Digital Surface Model (DSM) Generation in Water Areas

2020 ◽  
Vol 12 (5) ◽  
pp. 870 ◽  
Author(s):  
Wenhuan Yang ◽  
Xin Li ◽  
Bo Yang ◽  
Yu Fu
Keyword(s):  
Common Ground ◽  
Stereo Matching ◽  
Final Height ◽  
Block Matching ◽  
Disparity Estimation ◽  
Surface Model ◽  
Seed Point ◽  
Practical Applications ◽  
Dense Matching ◽  
Global Matching

Image dense matching has become one of the widely used means for DSM generation due to its good performance in both accuracy and efficiency. However, for water areas, the most common ground object, accurate disparity estimation is always a challenge to excellent image dense matching methods, as represented by semi-global matching (SGM), due to the poor texture. For this reason, a great deal of manual editing is always inevitable before practical applications. The main reason for this is the lack of uniqueness of matching primitives, with fixed size and shape, used by those methods. In this paper, we propose a novel DSM generation method, namely semi-global and block matching (SGBM), to achieve accurate disparity and height estimation in water areas by adaptive block matching instead of pixel matching. First, the water blocks are extracted by seed point growth, and an adaptive block matching strategy considering geometrical deformations, called end-block matching (EBM), is adopted to achieve accurate disparity estimation. Then, the disparity of all other pixels beyond these water blocks is obtained by SGM. Last, the median value of height of all pixels within the same block is selected as the final height for this block after forward intersection. Experiments are conducted on ZiYuan-3 (ZY-3) stereo images, and the results show that DSM generated by our method in water areas has high accuracy and visual quality.

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