Disparity Estimation with Scene Depth Cues

Stereo video object segmentation is a critical technology of the new generation of video coding, video retrieval and other emerging interactive multimedia fields. Determinations of distinctive depth of a frame features have become more popular in everyday life for automation industries like machine vision and computer vision technologies. This paper examines the evaluation of depth cues through dense of two frame stereo correspondence method. Experimental results show that the method can segment the stationary and moving objects with better accuracy and robustness. The contributions have higher accuracy in matching and reducing time of convergence.

Download Full-text

Relative size and pictorial depth cues in children and adults

PsycEXTRA Dataset ◽

10.1037/e666672011-230 ◽

1969 ◽

Author(s):

Barbara Lee Wilcox ◽

Martha Teghtsoonian

Keyword(s):

Relative Size ◽

Depth Cues ◽

Pictorial Depth

Download Full-text

An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

Journal of Perceptual Imaging ◽

10.2352/j.percept.imaging.2020.3.1.010501 ◽

2020 ◽

Vol 3 (1) ◽

pp. 10501-1-10501-9

Author(s):

Christopher W. Tyler

Keyword(s):

Belief Propagation ◽

Probability Distributions ◽

Three Dimensional ◽

Dimensional Structure ◽

Fusion Model ◽

Cue Combination ◽

Depth Estimate ◽

Appropriate Behavior ◽

Depth Cues ◽

Bayesian Averaging

Abstract For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the “belief propagation” algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or “modified weak fusion” model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.

Download Full-text

Fast disparity estimation algorithm based on features of disparity vector

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.01856 ◽

2013 ◽

Vol 32 (6) ◽

pp. 1856-1859

Author(s):

Xiao-wei SONG ◽

Lei YANG ◽

Zhong LIU ◽

Liang LIAO

Keyword(s):

Estimation Algorithm ◽

Disparity Estimation ◽

Disparity Vector

Download Full-text

Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment

Applied Sciences ◽

10.3390/app11010003 ◽

2020 ◽

Vol 11 (1) ◽

pp. 3

Author(s):

Laura Gonçalves Ribeiro ◽

Olli J. Suominen ◽

Ahmed Durmush ◽

Sari Peltonen ◽

Emilio Ruiz Morales ◽

...

Keyword(s):

Pose Estimation ◽

Stereo Matching ◽

Motion Parallax ◽

Extreme Temperature ◽

Single Camera ◽

Safety Critical ◽

Depth Cues ◽

Significant Challenge ◽

Visual Technologies ◽

Marker Detection

Visual technologies have an indispensable role in safety-critical applications, where tasks must often be performed through teleoperation. Due to the lack of stereoscopic and motion parallax depth cues in conventional images, alignment tasks pose a significant challenge to remote operation. In this context, machine vision can provide mission-critical information to augment the operator’s perception. In this paper, we propose a retro-reflector marker-based teleoperation aid to be used in hostile remote handling environments. The system computes the remote manipulator’s position with respect to the target using a set of one or two low-resolution cameras attached to its wrist. We develop an end-to-end pipeline of calibration, marker detection, and pose estimation, and extensively study the performance of the overall system. The results demonstrate that we have successfully engineered a retro-reflective marker from materials that can withstand the extreme temperature and radiation levels of the environment. Furthermore, we demonstrate that the proposed maker-based approach provides robust and reliable estimates and significantly outperforms a previous stereo-matching-based approach, even with a single camera.

Download Full-text