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We propose a novel joint registration and segmentation approach to estimate scene flow from RGB-D images. Instead of assuming the scene to be composed of a number of independent rigidly-moving parts, we use non-binary labels to capture non-rigid deformations at transitions between the rigid parts of the scene. Thus, the velocity of any point can be computed as a linear combination (interpolation)...
We propose that left- and right-half-occlusion regions contain information that can distinguish between stereoscopic and pseudoscopic display conditions. A machine vision method is presented based on this idea, which detects pseudo copy using only the histograms of left and right half-occlusion pixel locations. Two psychophysical experiments are described which study the ability of human viewers to...
In this paper, Kinect based sensors are used to enable automatic animal detection for monitoring and assessment the growth of livestock. Benefitted from the fast developing 3-D machine vision devices and techniques, it is liable to detect the object using the point cloud data. In the present work, a Kinect sensor is installed on the roof of the pig pen to obtain the 3-D data. After thresholding the...
In this paper we estimate optical flow by using spatiotemporal differentiation scheme based on compensation method. Conventional spatiotemporal differentiation scheme enables us to calculate velocity distribution rapidly, but error in the approximation of derivative coefficients increases as the displacement of the moving pattern between the successive frames becomes large. To expand the range of...
In this paper we introduce a new data driven method to novelty detection and object definition in dynamic video streams that indiscriminately detects both static and moving objects in the scene. A sliding window density estimation is introduced in order to reliably detect texture edges. A Sobel filtering process is used to extract gradient of edges. Using this new approach, the detection of object...
Estimating the localization of an autonomous robot is a key problem in robotics. Monocular cameras have been successfully used to solve this problem, but they suffer with the absence of depth information. It limits measurements made with only monocular cameras to be recoverable only up to a scale. This condition is known as scale ambiguity. In this paper we propose an approach to solve the scale ambiguity...
This paper proposes an Eye Gaze Tracking (EGT) technique using a single eye image that can be easily calibrated and mapped for Human Computer Interaction (HCI). The technique employs both geometric and trigonometric relationships to find a user's Point of Regard, followed by calculating user-dependent variables for final mapping onto a user interface (UI). Experimental results show acceptable accuracy...
The combining method of neighborhood and non-neighborhood is proposed to be used for amending the computed results of optical flow, which has improved the robustness of flow estimation. This method uses the estimated frame based on CLG-TV model, carries out the decomposition of structure and texture for the images, proceeds weighted approach for data term and smooth term, uses coarse-to-fine method...
Optical Flow is the pixels apparent movement pattern of two consecutives images. There are differents methods to estimate the optical flow: variational methods and exaustive methods. Exhaustive methods take a neighborhood around a point in the first image of the sequence and search for the most similar in the second image. This procedure is known as correspondency estimation. This work evaluate the...
Either any of the current global or non-local stereo matching algorithms cannot be good enough to show both matching accuracy and calculation efficiency during the matching processing, especially while there are less texture regions or the images are captured from real scene. Therefore, the goal of our research is to break current bottleneck of stereo matching in aspects of the precision and speed,...
Underwater environments present a considerable challenge for computer vision, since water is a scattering medium with substantial light absorption characteristics which is made even more severe by turbidity. This poses significant problems for visual underwater navigation, object detection, tracking and recognition. Previous works tackle the problem by using unreliable priors or expensive and complex...
Real-time technology of stereo matching to generate depth map from stereo images is one of the important computer vision challenges in recent years. The depth map are used in various fields such as the robot binocular vision, 3D recognition system, 3D model reconstruction and so on. Because the initial depth estimation method cannot generate good results, most of the methods will accompany a refinement...
As the development of robot autonomous navigation, visual odometry gains more and more attention in the last few years. It can estimate the egomotion of a robot by analyzing the changes of the on-board camera view. This paper gives an overview of visual odometry and the development of its key technologies, such as feature detection, description and matching/tracking, camera pose estimation. The advantages...
Motion estimation from underwater images is an active research area of the vision system devoted to the applications of robots. In this paper, a vision based system for tracking the motion of moving objects is presented. The aim of this paper is to give an optimal performance against radiometric features such as non-uniform lighting, blurring and noise. The moving object detection is performed by...
Recovering 3D depth from a single outdoor image is a basic problem in Computer Vision and Close-Range Photogrammetry. In this paper, an efficient depth estimation approach from a single outdoor image is presented. According to scene classification, depth of regions marked as sky, ground and vertical labels is respectively predicated. Firstly, a more accurate depth calculation model for ground regions...
A new region-based local stereo matching algorithm with accurate disparity estimation is proposed. For the local stereo matching, finding an appropriate support window is crucial to the performance of disparity estimation. In order to generate an accurate support region, a modified cross-based local approach combined with mean-shift segmentation is performed. We then further improve the reliability...
This paper presents a new inlier-based outlier detection scheme for the analysis of abnormal behavior in crowded scenes. First, we segment the video into a set of cubes, and then extract the three feature descriptors from each cube, including the histogram of oriented gradients (HOG), the histogram of motion directions, and the motion magnitude descriptors. Thereafter, for each feature descriptor...
This paper presents a novel method to conduct camera pose estimation though combining Kinect and Perspective-n-points algorithms. Most existing camera pose estimation methods suffer from the errors caused by inevitable outliers between 2D–3D correspondences. To this end, we propose to use a random down sampling process to deal with outliers in this paper. The proposed method is divided into two main...
Light field (LF), a promising representation to describe the visual appearance of a scene, implicitly captures 3D scene geometry. Inspired by this, we exploit the special linear structure of epipolar plane image (EPI) and propose a novel framework for depth estimation for 4D LF. Our approach estimates disparities through locating the optimal slope of each line segmentation on EPIs, which are projected...
The results of experimental research of both homogeneous and heterogeneous image superimposition quality by means of multiple view geometry. One of the most important tasks in the problem of image superimposition which has not been solved till now is the task to make the right selection of a set of key (corresponding) point pairs on superimposed images. The research shows image superimposition by...
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