A novel modular framework for stereo vision

Oon-Ee, Ng; Ganapathy, Velappa

doi:10.1109/aim.2009.5229904

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Conversely, the proposed method produces lower accuracy percentages due to the presence of salt-and-pepper noise. While this can be easily reduced with simple filters, the raw results are shown here since the focus is on the cost calculation stage and not the post-processing stage [2]. method gives good performance at object boundaries while still having acceptable efficiency.…”

Section: Resultsmentioning

confidence: 99%

“…Stereo vision algorithms generally involve several modules or stages ( [1] and [2]). One of these stages is the cost calculation stage, where a 'matching cost' for a pixel at some disparity is calculated.…”

Section: Introductionmentioning

confidence: 99%

“…The modular nature of stereo vision means that focusing on a specific module can improve the overall result of any algorithm which uses that particular module. In this paper we focus on the aggregation module from the cost calculation stage, which requires prior estimates from an initialization module [2].…”

Section: Introductionmentioning

confidence: 99%

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Connected-component stereo aggregation

Oon-Ee

Ganapathy

Ponnambalam

2010

2010 IEEE Asia Pacific Conference on Circuits and Systems

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Stereo vision algorithms can be divided into several general stages. Detailed analysis on each specific stage allows focused improvement of the entire algorithm. Existing stereo vision algorithms typically prioritize pixel and sub-pixel accuracy, focusing on the optimization stage, while commercial algorithms which prioritize real-time operation typically focus on the cost calculation stage. This paper presents a novel approach to cost aggregation, a module within the cost calculation stage, based on connected-component analysis. The conventional connectedcomponent analysis algorithm is extended for use in the stereo vision matching cost aggregation problem, resulting in an algorithm which is comparable in efficiency to the best current aggregation algorithms while performing well at object boundaries, typically the most difficult problem for aggregation-based approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Connected-component stereo aggregation

Oon-Ee

Ganapathy

Ponnambalam

2010

2010 IEEE Asia Pacific Conference on Circuits and Systems

Self Cite

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“…In the past, stereo vision was mainly used in robot vision detection and navigation. 10,11 With the development of science and technology, our requirements for 3D information are more prominent, especially in the fields of medical treatment, automobile, living tree reconstruction, digital city, aviation, and other fields. 12 According to the different application areas, the technologies adopted are also different.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional mechanical parts reconstruction technology based on two-dimensional image

Huo

2020

International Journal of Advanced Robotic Systems

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With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo vision system based on two-dimensional image modeling technology. The system can be divided into image processing, camera calibration, stereo matching, three-dimensional point reconstruction, and model reconstruction.In the part of image processing, common image processing methods, feature point extraction algorithm, and edge extraction algorithm are studied. On this basis, interactive local corner extraction algorithm and interactive local edge detection algorithm are proposed. It is found that the Harris algorithm can effectively remove the features of less information and easy to generate clustering phenomenon. At the same time, the method of limit constraints is used to match the feature points extracted from the image. This method has high matching accuracy and short time. The experimental research has achieved good matching results. Using the platform of binocular stereo vision system, each step in the process of three-dimensional reconstruction has achieved high accuracy, thus achieving the three-dimensional reconstruction of the target object. Finally, based on the research of three-dimensional reconstruction of mechanical parts and the designed binocular stereo vision system platform, the experimental results of edge detection, camera calibration, stereo matching, and three-dimensional model reconstruction in the process of three-dimensional reconstruction are obtained, and the full text is summarized, analyzed, and prospected.

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A Review of 3-D Reconstruction Based on Machine Vision

Hong

Yang

et al. 2014

Intelligent Robotics and Applications

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A novel modular framework for stereo vision

Cited by 5 publications

References 31 publications

Connected-component stereo aggregation

Connected-component stereo aggregation

Three-dimensional mechanical parts reconstruction technology based on two-dimensional image

A Review of 3-D Reconstruction Based on Machine Vision

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