Improvement of stereo matching algorithm based on sum of gradient magnitude differences and semi‐global method with refinement step

Hamzah, Rostam Affendi; Ibrahim, Haidi

doi:10.1049/el.2017.3956

Cited by 8 publications

(3 citation statements)

References 10 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This leads to both cost and equipment being increased. There is one method to solve this matter, which is by using compression process, where it is possible to encode the database and the sequence of transmission effectively [20]. Compression is only possible when the information usually showed in the format which longer than needed.…”

Section: Introductionmentioning

confidence: 99%

Image compression using singular value decomposition by extracting red, green, and blue channel colors

et al. 2022

View full text Add to dashboard Cite

This paper presents an image compression using singular value decomposition (SVD) by extracting the red, green, and blue (RGB) channel colors. Image compression is needed in the development of various multimedia computer services and applications for example in the telecommunications and storage technologies. Now a days, video technology, digital broadcast codec and teleconferencing become popular and always requires high image compression process for display. Hence, efficient image compression is compulsory to reduce the number of storage sizes and maintain the image quality. Therefore, this article proposes image compression using SVD, which this method is efficiently reducing the image storage size and at the same time maintaining the image quality. The SVD removes redundant pixel values based on RGB colors to make the storage image size decreased. Based on the experimental analysis on two different type of image extensions (i.e., jpg and png), the SVD is capable to reduce the image size and at the same time preserving the image quality.

show abstract

Section: Introductionmentioning

confidence: 99%

Image compression using singular value decomposition by extracting red, green, and blue channel colors

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The development of stereo matching, there are two major approaches available in developing the algorithm framework. It is local methods as published in [8][9][10] and global method [11]. Mostly local methods use local properties or local contents using windows-based technique such as fixed windows implemented in [12][13], adaptive window [14], convolution neural network [15] and multiple windows [16].…”

Section: Introductionmentioning

confidence: 99%

A study of edge preserving filters in image matching

et al. 2021

View full text Add to dashboard Cite

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much better.

show abstract

“…Introduction: With the rapid development of computer vision, 3D vision has garnered substantial interest in various tasks, such as robotics, autonomous driving, and target tracking [1][2][3]. Generally, these 3D visionrelated tasks necessitate the acquisition of depth cues to reconstruct 3D environments.…”

mentioning

confidence: 99%

Stereo sample generation‐based domain generalization network for stereo matching

Xu,

Zhu,

Peng

et al. 2024

Electronics Letters

View full text Add to dashboard Cite

Recently, deep learning‐based stereo matching has achieved great success. However, models trained on the source domain dataset encounter substantial performance degradation when directly tested on an unseen target domain dataset because of neglecting the generalization to out‐of‐distribution (OOD) stereo samples. This paper proposes a stereo sample generation‐based domain generalization network (SGDG‐Net) for stereo matching. Specifically, to expand the distribution span of training samples, OOD stereo samples are generated to assist training. To effectively generate OOD left samples, a style transfer‐based generation mechanism is proposed to transmit perturbations to the source left samples. In addition, to generate the OOD right samples, a disparity‐assisted generation strategy is proposed by using disparity map labels as auxiliary information. Experimental results demonstrate that the proposed SGDG‐Net produces remarkable results on four benchmark datasets.

show abstract

Improvement of stereo matching algorithm based on sum of gradient magnitude differences and semi‐global method with refinement step

Cited by 8 publications

References 10 publications

Image compression using singular value decomposition by extracting red, green, and blue channel colors

Image compression using singular value decomposition by extracting red, green, and blue channel colors

A study of edge preserving filters in image matching

Stereo sample generation‐based domain generalization network for stereo matching

Contact Info

Product

Resources

About