Abstract:
<p>In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.</p>Keywords:
Computer stereo vision
Stereo cameras
Topics:
Binocular stereo vision is an important branch of the research area in computer vision. Stereo matching is the most important process in binocular vision. In this paper, a new stereo matching scheme using shape-based matching (SBM) is presented to improve the depth reconstruction method of binocular stereo vision systems. The method works in two steps. First, an operator registers the pattern including the key features of an object to be measured. Then during the operation stage, the stereo camera snaps stereo images and finds the patterns in right and left images separately by means of the SBM. The 3D positions of the object are calculated by using the corresponding points of the stereo images and the projection matrices of the stereo camera. Since we apply robust image processing algorithms, such as the SBM, the proposed method becomes more reliable than the conventional stereo vision systems.
Computer stereo vision
Stereo cameras
Binocular disparity
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The paper discusses the main stages of creating and implementing a model of a universal, inexpensive measuring system based on the principles of stereo vision. Particularly special attention had been paid to the development, analysis, study of the features and accuracy of measurements of a passive stereo vision system, as a measuring device. In the course of the presented work, the following procedures were performed: calibration of a separate camera, calibration of a stereo camera, straightening and comparison of stereo images. The software part of the work was done in the C ++ programming language in the QtCreator editor using the computer vision library OpenCV 3.2.
Stereo cameras
Computer stereo vision
Machine Vision
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<p>In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.</p>
Computer stereo vision
Stereo cameras
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Stereo vision is a growing research domain which seeks the attention of various researchers to attain deeper scene extraction. This work provides an extensive analysis towards the stereo-matching algorithms and stereo-vision to resolve the problems related to it. The analysis towards the stereo matching technologies is executed with benchmark standards with the focus on stereo vision methods. Thus the comparison of stereo matching algorithms can be done through the implementation of stereo vision application in a particular domain so that the results for the algorithms are comparatively analyzed. In most cases, the analysis and comparison are performed with statistical analysis and emphasize the benefits of various stereo algorithms. Some approaches give higher computational cost with expected outcomes over the lower time frame and provides competency towards parallel processing. The results obtained from the various stereo matching algorithms through the identified different parameters of bad pixels.
Computer stereo vision
Stereo cameras
Benchmark (surveying)
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Stereo cameras
Computer stereo vision
Stereo imaging
Machine Vision
Smart camera
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In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.
Computer stereo vision
Stereo cameras
Optimization algorithm
Machine Vision
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<p>In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.</p>
Computer stereo vision
Stereo cameras
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Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance
Computer stereo vision
Stereo cameras
Distance measurement
Epipolar geometry
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In stereo vision, camera modeling is very important because the accuracy of the three dimensional locations depends considerably on it. In the existing stereo camera models, two camera planes are located in the same plane or on the optical axis. These camera models cannot be used in the active vision system where it is necessary to obtain two stereo images simultaneously. In this paper, we propose four kinds of stereo camera models for active stereo vision system where focal lengths of the two cameras are different and each camera is able to rotate independently. A single closed form solution is obtained for all models. The influence of the stereo camera model to the field of view, occlusion, and search area used for matching is shown in this paper. And errors due to inaccurate focal length are analyzed and simulation results are shown. It is expected that the three dimensional locations of objects are determined in real time by applying proposed stereo camera models to the active stereo vision system, such as a mobile robot.
Computer stereo vision
Stereo cameras
Camera matrix
Stereo imaging
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This paper discusses the application of thermal infrared stereo vision for distance mapping in fire environments for mobile robotic systems and humans. The capabilities of thermal infrared stereo vision are presented. The relationship between the development of thermal IR stereo vision systems and the development of visual stereo vision systems is discussed. Aspects of thermal infrared stereo vision that require a different approach than visual stereo vision systems are then presented with a discussion of techniques used to address these items. A comparison of a thermal IR stereo vision system with an off-the-shelf visual stereo vision system is provided.
Stereo cameras
Computer stereo vision
Thermal infrared
Stereo imaging
Machine Vision
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Citations (15)