Proposed Stereo Vision System Research Articles

Accurate three-dimensional measurement for small objects based on the thin-lens model.

Both accuracy and depth of field (DOF) are required for small objects' measurement in advanced manufacture and accurate robotics industries. In this paper, a stereo vision system with structured light based on the thin-lens model is developed to measure small objects with high accuracy and extended DOF at high magnification. The model of the proposed stereo vision system is built. The new system calibration scheme and measurement procedures are proposed. The DOF of the used thin-lens model is extended by utilizing autofocus capability. With the thin-lens model, accurate calibration and extended DOF at high magnification can be achieved. Three measurement experiments are conducted with the proposed system to evaluate its effectiveness and accuracy. The error of the protrusions' height on the reconstructed aluminum part is lower than 0.55%, and the standard deviation (STD) of a fitting plane reaches 1.7 µm. Low cost, high accuracy, and extended DOF can be simultaneously achieved for measuring small objects with the proposed system based on the thin-lens model.

Significant obstacle location with ultra-wide FOV LWIR stereo vision system

Intelligent driving is an active area of research in both industry and academia. In order to overcome the shortcomings of traditional machine vision such as visibility is easily affected by illumination conditions, the blind area of infrared small field of view (FOV) is too large and could not provide depth information, this paper proposes a method for detecting significant obstacles based on ultra-wide FOV long-wave infrared (LWIR) stereo vision system. The stereo vision positioning location with ultra-wide FOV is established by the generalized fisheye camera model. On the basis of analyzing obstacle imaging scale and the structure characteristics of the proposed stereo vision system, a multi-scale salient region detection method based on composite pattern is proposed, and its implementation process is described in detail. Experiment shows that the proposed ultra-wide FOV LWIR stereo vision system is able to detect and locate significant obstacles in ultra-wide FOV and the detection rate of pedestrians and vehicles in real complex street scenes is over 92.6%. At the same time, the relative error of pedestrian positioning with a distance of 5 m to 30 m near the central FOV is between 1.6%-10.3%, and its spatial location ability and advantages are verified. The proposed stereo vision system effectively overcomes the shortcomings of existing vision systems, expands the scope of machine vision, and can be used in the field of assistant driving and intelligent driving.

Stereo Vision System for Accurate 3D Measurements of Connector Pins’ Positions in Production Lines

This paper addresses the need for a fast and precise non-contact measurement system to be used to assess the quality of connectors for electrical/electronic applications and particularly it proposes a technique to measure the position of the pins that are present in various shapes and number in every connector by a stereo vision system. The proposed stereo vision system is targeted to the automotive industry, but the same approach can be translated to many other sectors. The measurement of the position of the pins is a crucial aspect in assessing the quality both of the connectors (from the point of view of the connectors suppliers) and of the equipment where the connectors are used. It is of primary importance to verify the position of the pins not only in the 2D plane where they lie, but also to measure their height to verify that they can guarantee the continuity of the electrical circuit. In this paper, a technique based on the stereo vision principle is proposed to perform such an analysis. The stereo vision system, developed for a real production line, is compared against other available non-contact techniques and its advantages in terms of resolution, speed and robustness are pointed out. The system is described in terms of hardware, layout and software. System calibration and performance are described and verified. Afterwards, the on line implementation is described and the advantages and critical points are highlighted.

Persistent and automatic intraoperative 3D digitization of surfaces under dynamic magnifications of an operating microscope.

One of the major challenges impeding advancement in image-guided surgical (IGS) systems is the soft-tissue deformation during surgical procedures. These deformations reduce the utility of the patient's preoperative images and may produce inaccuracies in the application of preoperative surgical plans. Solutions to compensate for the tissue deformations include the acquisition of intraoperative tomographic images of the whole organ for direct displacement measurement and techniques that combines intraoperative organ surface measurements with computational biomechanical models to predict subsurface displacements. The later solution has the advantage of being less expensive and amenable to surgical workflow. Several modalities such as textured laser scanners, conoscopic holography, and stereo-pair cameras have been proposed for the intraoperative 3D estimation of organ surfaces to drive patient-specific biomechanical models for the intraoperative update of preoperative images. Though each modality has its respective advantages and disadvantages, stereo-pair camera approaches used within a standard operating microscope is the focus of this article. A new method that permits the automatic and near real-time estimation of 3D surfaces (at 1 Hz) under varying magnifications of the operating microscope is proposed. This method has been evaluated on a CAD phantom object and on full-length neurosurgery video sequences (∼1 h) acquired intraoperatively by the proposed stereovision system. To the best of our knowledge, this type of validation study on full-length brain tumor surgery videos has not been done before. The method for estimating the unknown magnification factor of the operating microscope achieves accuracy within 0.02 of the theoretical value on a CAD phantom and within 0.06 on 4 clinical videos of the entire brain tumor surgery. When compared to a laser range scanner, the proposed method for reconstructing 3D surfaces intraoperatively achieves root mean square errors (surface-to-surface distance) in the 0.28-0.81 mm range on the phantom object and in the 0.54-1.35 mm range on 4 clinical cases. The digitization accuracy of the presented stereovision methods indicate that the operating microscope can be used to deliver the persistent intraoperative input required by computational biomechanical models to update the patient's preoperative images and facilitate active surgical guidance.

Detection and tracking of ships using a stereo vision system

Maritime transportation continues to maintain the largest volume in the transportation sector, and this is increasing day-by-day. This means there is an ever-increasing demand for safe navigation. In this study, we propose a stereo vision system for detecting and tracking ships as a tool for increasing navigational safety. Ships are detected from obtained images by using an edge detection algorithm that uses a moving average to find high-intensity gradients. The detected points are clustered and the 3D location of a detected ship is calculated from a stereo image pair. The detected ship is then tracked for several tens of seconds to obtain its course. The result of our experiment indicates that the proposed stereo vision system is useful as a tool for safe navigation. Key words: Safety navigation, ship detection, tracking, computer vision, stereo vision.

Segmentation and Location Computation of Bin Objects

In this paper we present a stereo vision based system for segmentation and location computation of partially occluded objects in bin picking environments. Algorithms to segment partially occluded objects and to find the object location [midpoint,x, y and z co-ordinates] with respect to the bin area are proposed. The z co-ordinate is computed using stereo images and neural networks. The proposed algorithms is tested using two neural network architectures namely the Radial Basis Function nets and Simple Feedforward nets. The training results fo feedforward nets are found to be more suitable for the current application. The proposed stereo vision system is interfaced with an Adept SCARA Robot to perform bin picking operations. The vision system is found to be effective for partially occluded objects, in the absence of albedo effects. The results are validated through real time bin picking experiments on the Adept Robot.