Real-Time Stereo Camera Calibration Using Stereo Synchronization and Erroneous Input Image Pair Elimination
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Computer stereo vision
Stereo cameras
Stereo imaging
<p>In this paper, a mathematical approach was used to improve stereo vision. The speed at which objects in multiple stereo images could be matched was improved using keypoints, and the use of multiple stereo images helped to improve the accuracy of the system. A three camera stereo vision system was built as a testbed to validate the approaches proposed in this paper. The research in this project can be applied as a fast and less costly method to optimize stereo vision systems with many uses such as in household robotics and autonomous vehicles.</p> <p>Stereo vision is the process of passively recovering object depth from camera images by comparing two images from different cameras of the same scene. Distance to the object is computed by comparing the shift of an object between the two images. The objects that are closer in the scene will have a larger shift between the images, while objects that are further away will have a smaller shift. This shift of the objects in the image is known as the disparity. The larger the disparity, the closer the object.</p> <p>Sensitivity analysis was conducted on the stereo vision calculation formula to analyze how the separation between two stereo cameras affected the system's ability to accurately compute the distance to objects. The results showed that as the separation between two stereo cameras is increased, the accuracy and range for distance calculation also increases. A three camera system was built with cameras 3, 4 and 7 inches apart.</p> <p>Current approaches for calculating object distance compares the stereo images by matching a block of pixels in one image to a corresponding block in the other image to identify the same object in both images. This method, known as “block matching”, is time consuming, preventing usability on autonomous vehicles that rely on real-time information. SIFT keypoint feature detection along with k-Nearest Neighbor (knn) matching were studied and used to match the objects between two images. Keypoints are points in an image with unique features that can be identified on a corresponding stereo image such as corners or edges. Each keypoint has its own descriptor, which is a mathematical array and can be compared to other keypoints in the corresponding stereo image in order to match them. This approach is faster because it only requires matching of important regions of the image. Using the 3 camera test bed, data was collected in two environments. First in a noisy environment with a lot of background objects. Second, with the same objects and a plain background.The use of keypoints was faster and provided real-time data that was used to filter out the matches between these keypoints led to more accurate results. Furthermore, cameras with higher separation between them (7 inches) were able to more accurately detect the distances to objects further away while cameras with 3 inches and 4 inches of separation could only find the distances to nearby objects accurately. </p> <p>Results showed that in the more noisy environments, the 3in and 4in camera separation system exceeded an error of ±5in when an object was 85in away, while the 7in camera separation system exceeded an error of ±5in when an object was 165in away. In less noisy environments, the 3in camera separation system exceeded an error of ±5in when object was 90in away, the 4in camera separation system exceeded an error of ±5in when object was 100in away, and the 7in camera separation system exceeded an error of ±5in when object was 200in away.</p>
Stereo cameras
Computer stereo vision
Feature (linguistics)
Stereo imaging
Machine Vision
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Stereo cameras
Computer stereo vision
Stereo imaging
Machine Vision
Smart camera
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This paper presents the control of the image disparity of a parallel stereo camera and its application to an underwater stereo camera to enhance the working efficiency of underwater vehicles that are equiped with manipulators in seabed operation. The stereo camera consists of two parallel lenses mounted on a lateral moving base and two CCD cameras mounted on a longitudinal moving base, which is embedded in a small pressure canister for underwater application. Because the lateral shift is related to the backward shift with a nonlinear relation, only one control input is needed to control the vergence and focus of the camera with a special driving device. We can get clear stereo vision with the camera for all the range of objects in air and in water, especially in short range objects. The control system of the camera is so simple that we are able to realize a small stereo camera system and apply it to a stereo vision system for underwater vehicles. This paper also shows how to acquire the distance information of an underwater object with this stereo camera. Whenever we focus on an underwater object with the camera, we can obtain three-dimensional images and distance information in real-time.
Stereo cameras
Stereo imaging
Computer stereo vision
Vergence (optics)
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This paper presents a top-down approach to stereo for use in driver assistance systems. We introduce an asymmetric configuration where monocular object detection and range estimation is performed in the primary camera and then that image patch is aligned and matched in the secondary camera. The stereo distance measure from the matching assists in target verification and improved distance measurements. This approach, Stereo-Assist, shows significant advantages over the classical bottom-up stereo approach which relies on first computing a dense depth map and then using the depth map for object detection. The new approach can provide increased object detection range, reduced computational load, greater flexibility in camera configurations (we are no longer limited to side-by-side stereo configurations), greater robustness to obstructions in part of the image and mixed camera modalities FIR/VIS can be used. We show results with two novel configurations and illustrate how monocular object detection allows for simple online calibration of the stereo rig.
Robustness
Stereo cameras
Computer stereo vision
Monocular
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In this paper, detection of pedestrians using ldquosubtraction stereordquo is discussed. Subtraction stereo is a stereo vision method that focuses on the movement of objects to make a stereo camera robust and produces range images for moving regions. Features of pedestrians such as 3D position, height and width are obtained from range images obtained by subtraction stereo. Then a simple method to remove shadows is proposed. The basic algorithm of the subtraction stereo is implemented on a commercially available stereo camera, and the effectiveness of the method to detect pedestrians with removal of shadows is verified by experiments using the stereo camera.
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Pedestrian detection
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This study aims at developing a practical stereo camera that is suitable for applications such as surveillance, in which detection of anomalies or measurement of moving people are required. In such surveillance cases, targets to measure usually move. In this paper, "Subtraction stereo" is proposed that focuses on motion information to increase the robustness of the stereo matching. It realizes robust measurement of range images by detecting moving regions with each camera and then applying stereo matching for the detected moving regions. Measurement of three-dimensional position, height and width of a target object using the subtraction stereo is discussed. The basic algorithm is implemented on a commercially available stereo camera, and the effectiveness of the subtraction stereo is verified by several experiments using the stereo camera.
Computer stereo vision
Robustness
Stereo cameras
Stereo imaging
Subtraction
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A method for camera calibration in robotic binocular stereo vision is introduced in this paper. Camera calibration is a necessary step in 3D computer vision in order to extract metric information from 2D images. Being different from with single camera calibration, binocular stereo vision system not only needs to ascertain intrinsic parameters, but also the relative position relation of two cameras. We compute it in two steps: first, we compute the intrinsic and initial extrinsic parameters of camera by Zhang Plane-based calibration method; second, we suppose the intrinsic parameter is invariable, camera's moving parameters can be computed by self-calibration method, through finding the stereo matching point and calculating the fundamental matrix and essential matrix
Computer stereo vision
Camera matrix
Stereo cameras
Essential matrix
Position (finance)
Image plane
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Development of a practical stereo vision sensor for real-world applications must account for the variability of high-volume production processes and the impact of unknown environmental conditions during its operation. One critical factor of stereo depth estimation performance is the relative alignment of the stereo camera pair. While imperfectly aligned stereo cameras may be rectified in the image domain, there are some errors introduced by both the calibration recovery and image rectification processes. Finally, additional uncalibrated misalignments, for example due to thermal or mechanical deformation in a harsh automotive environment, may occur which will further deteriorate stereo depth estimation. This paper describes an experimental framework for determining these limits using image processing algorithms, operating on graphically synthesized imagery, with performance envelope validation on real stereo image data.
Computer stereo vision
Stereo cameras
Image rectification
Stereo image
Stereo imaging
Epipolar geometry
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In this paper, a practical method of stereo vision, “subtraction stereo” is proposed. A huge number of studies have been carried out for stereo vision until now, and several practical stereo vision systems have been reported. However, what is called the correspondence problem that stereo matching becomes difficult and not robust for weak textures or recurrent patterns is inevitable for stereo vision. Subtraction stereo realizes robust measurement of range images by detecting moving regions with each camera first and then applying stereo matching for the detected moving regions. Detection of moving regions is carried out with a subtraction process. Concept and fundamental algorithm of subtraction stereo are introduced. Then measurement of three-dimensional position, height and width of a target object using the subtraction stereo is discussed. The basic algorithm is implemented on a commercially available stereo camera and the effectiveness of the subtraction stereo is verified by several experiments using the stereo camera. Although objects are restricted to moving ones, subtraction stereo gives sufficient information robustly for many applications such as surveillance.
Computer stereo vision
Subtraction
Stereo cameras
Stereo imaging
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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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