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Nowadays, research focusing on robotics, autonomous vehicles, and scene analysis shows a clear need for the ability to accurately reconstruct three-dimensional environments. One of the methods allowing to conduct such a reconstruction is to use a set of cameras and image processing techniques. This is a passive method. Despite being, in general, less accurate than its active counterparts, it offers significant advantages in numerous applications in which active systems cannot be deployed due to limited performance. This paper provides a theoretical analysis of the accuracy of estimating 3D positions of objects present at a given scene, based on images from a set of cameras. The analysis assumes a known geometrical configuration of the camera system. The important limiting factor in the considered scenario is the physical resolution of sensors-especially in the case of systems that are supposed to work in real time, with a high FPS rate, as the use of high-resolution cameras is difficult in such circumstances. In the paper, the influence of the geometric arrangement of the cameras is studied and important conclusions about the potential of three-camera configurations are drawn. The analysis performed and the formulas derived help predict the boundary accuracy values of any system using a digital camera. The results of an experiment that confirm the theoretical conclusions are presented as well. [ABSTRACT FROM AUTHOR]

Abstract Image rectification is an important stage of digital image processing for three-dimensional (3D) coordinate measurements performed using stereoscopic technique. Rectification minimizes image distortions and radically reduces the calculation time necessary for stereo matching. Calibration of prism-based stereoscopic optical systems using the ray tracing camera model instead of a pinhole one leads to significant reduction of measurement errors, but this model has no single center of projection and does not allow applying conventional rectification techniques. This leads to the necessity of separate calibrations of the pinhole model for image rectification and the ray tracing model for 3D reconstruction. To overcome this contradiction, we propose the rectification technique, which uses the inverse ray tracing to find intersection points of the rays with the intermediate plane in the object space and then projects these points onto image planes of the virtual cameras. The distance to the rectification plane has significant impact on the misalignment of rectified images, so it was analyzed using computer simulation for various distances to the object. The technique has been integrated into the 3D model acquisition algorithm and verified by applying to the images of test and real objects obtained by two different miniature prism-based stereovision systems. For the typical working range 12–30 mm of endoscopic stereo imagers and the single rectification distance of 15 mm, it provides the rectification accuracy better than the accuracy provided by the pinhole rectification technique. Additionally, we have shown that the ray tracing camera model combined with the proposed rectification method can compensate the prism angular displacement around the optical axis, which hinders a correct matching of rectified images. The proposed technique is applicable to many non-central camera models used for catadioptric systems, depth-from-refraction systems and systems using lenses with significant pupil aberrations. Highlights • Rectification minimizes image distortions and reduces the stereo matching time. • For the images obtained using prism-based stereovision systems, a special rectification method is necessary. • Ray tracing camera model combined with rectification method can compensate the prism mis-rotation. • Rectification accuracy is dependent on the distance from the object to the rectification plane. [ABSTRACT FROM AUTHOR]

Abstract Most of the humps in India are not being constructed and maintained according to the public safety guidelines of Indian Road Congress (IRC) i.e., IRC099, which is resulting in damage to the vehicles, severe discomfort to the driver and even causing loss of direction control which is leading to fatalities. Very few methods were discussed in literature for un-marked speed hump/bump detection. We propose a method that detects and informs the driver about the upcoming un-marked and marked speed hump/bump in real time using deep learning techniques and gives the distance the vehicle is away from it using stereo-vision approaches. We have achieved using NVIDIA GPU and Stereolabs ZED Stereo camera hardware. With this driver or autonomous mode of the vehicle can control the vehicle speeds to be at safer limits in order to not cause any kind of discomfort to the passengers as well as damage to the vehicle. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel computational model of stereovision for improving the accuracy of three-dimensional data extracted from a stereo-pair image with no effect of changes in focal length. For decades, most previous studies on stereovision have focused on the establishment of stereo matching, and have made conclusions on the premise of a fixed focus. In general, error in the depth estimate becomes bigger when the focus and aperture are unknown or not fixed. For that reason, a three-stage framework is proposed in this paper to modify the conventional stereovision model for improving the accuracy of depth estimation. The first stage is to modify the computational model of conventional stereovision for varifocal cameras. Then, the spacing of depth intervals in the non-uniform spacing of discrete depth levels can be altered, in particular, to be unaffected by changes in focal length. Finally, by considering the affine transformation, we add the deformation coefficient into the modified stereovision model for correcting three-dimensional affine deformations. Experimental results demonstrated that the depth estimation from stereo images using the proposed scheme was more accurate than conventional methods. The percentage error of most estimates fell between 0.06%-0.82%, and the error value increased from 0.02 cm to 2.21 cm within 6 m. [ABSTRACT FROM AUTHOR]

In this paper we describe a method for registration of 3D point clouds that represent objects of interest. A stereovision system is used to capture point clouds of a static environment, robot arm and an unknown object. By moving the robot arm in the environment the proposed system defines known occupied zones and is able to identify the robot arm. In order to identify a complete point cloud presentation of the robot gripper it is rotated in front of a stereovision camera and its geometry is captured from different angles. Iterative closest point algorithm is used to determine a rigid transformation between every new robot pose so the original point cloud can be appended with the transformed one. When the robot is holding a new object the registration procedure is repeated and known elements (environment, robot arm and gripper) are removed so that the object can be identified. [ABSTRACT FROM AUTHOR]

In this paper, a stereovision system for measuring the geometrical properties of fabrics is presented. Measurement of the parameter of spacings between the yarns is the main application of the system proposed. The algorithms implemented are described in detail which explain how one can measure the area of the front of the spacing as well as its orientation in a 3D space. The calibration procedure, using a specially prepared calibrator, is outlined. An accuracy analysis is presented both for the area and angle measurements. Exemplary results of measuring the areas and angles for 100 spacings between the yarns are presented in the form of histograms and tables. [ABSTRACT FROM AUTHOR]

Abstract: At present, stereo video sequences are actively used in the movie industry, in geographical information systems, and in navigation systems, among others. A novel method improves the frames interpolation by forming an invariant set of local motion vectors. First, the motion in a scene is estimated by block-matching algorithm. Second, accurate estimations by using Hu moments (for a noisy video sequence Zernike moments) are calculated. Such approach provides smooth motion that significantly improves the resulting stereo video sequence. Experimental results show the efficiency of frames interpolation based on such approach. The detection of local motion vectors achieves 86% accuracy. [Copyright &y& Elsevier]

Abstract: Road detection is one of the key issues for intelligent vehicles and advanced driver assistance systems (ADAS). In this paper, we present a stereovision-based approach for estimating the boundary between the drivable road region and the non-road region. It is based on a formulation of stereo with the ground-plane-induced homography in a hidden Markov model (HMM). Under this formulation, we employ the Viterbi algorithm and propose a sophisticated measure of the probability of the state sequence to find the most likely road/non-road boundary. Experimental results on a wide variety of typical but challenging real road scenes have substantiated the effectiveness as well as robustness of the proposed approach. [Copyright &y& Elsevier]

Overweight and obesity status is often categorized by body mass index (BMI), although this is not a measurement of body fat. Adiposity, especially in the abdominal area, is a better predictor of obesity-related diseases. However, current methods for assessment of body composition have limitations of bulkiness and expense. The purpose of this study was to evaluate a stereovision imaging system for analysis of body fat. A sample of 105 subjects was measured for body volume using the stereovision imaging system, as compared to air displacement plethysmography and hydrodensitometry. Body density was calculated from total body volume via stereovision imaging, air displacement plethysmography, and hydrodensitometry with weight. Then fat was computed via the Siri equation, and compared to body fat measurements via dual energy X-ray absorptiometry. Mean volume and fat measurements by stereovision and air displacement plethysmography did not differ significantly (mean differences -0.07 ± 0.17 L, -0.36 ± 0.82 kg, respectively, P > 0.05). Stereovision measurements of regional body volumes, lengths, and circumferences were used to develop a prediction equation via internal cross-validation for improved estimation of fat mass. This prediction equation reduced variation in individuals and improved effectiveness of the stereovision imaging system. [ABSTRACT FROM AUTHOR]

Abstract: The article describes a stereoscopic camera system based on a pair of separate cameras. The cameras are stationary and tilted together. The value of their tilting angle is derived in regards to their utilization of the chip area. By optimizing tilting we can maximally utilize the chip area, without the unused parts. It can increase the size of the cropped image of both left and right images as well as their resolution. The results were tested and verified on a real stereoscopic system used for an exploratory mobile robot. [Copyright &y& Elsevier]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: In this paper, a novel position estimation method of prism was proposed for single-lens stereovision system. The prism with multi faces was considered as a single optical system composed of some refractive planes. A transformation matrix which can express the relationship between an object point and its image by the refraction of prism was derived based on geometrical optics, and a mathematical model was introduced which can denote the position of prism with arbitrary faces only by 7 parameters. This model can extend the application of single-lens stereovision system using prism to a more widely area. Experimentation results are presented to prove the effectiveness and robustness of our proposed model. [Copyright &y& Elsevier]

This paper describes a multi-resolution stereovision system for detecting the front-vehicle in advanced safety vehicles (ASVs). The two asynchronous CMOS cameras in the proposed system are mounted on a platform that can be easily clamped to the rear-view mirror of a vehicle for detecting vehicles ahead. The asynchronous binocular platform provides a small low-cost obstacle detection system for practical ASVs that is easy to set up. The system uses a stereovision vehicle detection algorithm for real-time matching because the exposure times of the CMOS cameras are not synchronous. The algorithm uses a line segment matching module to match the extreme points of the horizontal and vertical edge segments at different resolutions to decrease the search area and computing complexity. As the distance of each matched segment can be calculated from the disparity value, each vehicle can be detected by clustering the segments that have similar distances in a searching and distance estimation module. The system was evaluated using static and dynamic analyses. Experimental results show that the proposed system can robustly and accurately detect the front-vehicles in real time under different illumination and road conditions. [ABSTRACT FROM AUTHOR]

Generally, extraction of boundary lines from a binocular stereo image is effective for a plane with patterns or a curved surface with shadows, and a place with uniform surface. Since a uniform plain cannot directly be reconstructed, however, another process to reconstruct the plain is needed after its boundary line is reconstructed. If a simple algorithm is applied to this process, a false plane is extracted sometimes. This paper describes a method uniquely to determine a plane from the three-dimensional boundary lines of an object in the stereo image. The method is effective for any object consisting of planes in the image. The method can also locally detect a fault in the boundary line so that this makes its compensation process easy. The final output image of this method is available in a form of a 'geometrical mode.' [ABSTRACT FROM AUTHOR]