Subtraction Stereo —A Stereo Vision Method That Focuses On Moving Regions—
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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.Keywords:
Computer stereo vision
Subtraction
Stereo cameras
Stereo imaging
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
Stereo imaging
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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.
Subtraction
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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Despite extensive study on it for decades, stereo vision is still widely regarded as a difficult problem. What makes it difficult is the stereo correspondence problem. Earlier Aloimonos and Herve (1992) proposed an algorithm that avoids the need of establishing correspondences across the stereo images. The algorithm however assumes a particular stereo configuration: the parallel-axes stereo geometry. This paper describes how the restriction could be removed and provides, in a closed-form format, an extension of the algorithm for general stereo geometry.
Computer stereo vision
Stereo cameras
Stereo image
Stereo imaging
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Computer stereo vision
Stereo cameras
Stereo imaging
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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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Citations (1)
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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