Study on clear stereo image pair acquisition method for small objects with big vertical size in SLM vision system

2016 ◽  
Vol 79 (5) ◽  
pp. 408-421 ◽  
Author(s):  
Yuezong Wang ◽  
Yan Jin ◽  
Lika Wang ◽  
Benliang Geng
Keyword(s):  
Vision System ◽  
Stereo Image ◽  
Image Pair ◽  
Acquisition Method ◽  
Vertical Size

scholarly journals AUTOMATIC DETECTION AND TRACKING OF HUMAN HEADS USING AN ACTIVE STEREO VISION SYSTEM

2000 ◽  
Vol 14 (02) ◽  
pp. 137-166 ◽  
Author(s):  
CHENG-YUAN TANG ◽  
ZEN CHEN ◽  
YI-PING HUNG
Keyword(s):  
Template Matching ◽  
Vision System ◽  
Tracking System ◽  
Human Head ◽  
Stereo Image ◽  
Head Tracking ◽  
Detection And Tracking ◽  
Stereo Cameras ◽  
Active Stereo ◽  
Elliptical Model

A new head tracking algorithm for automatically detecting and tracking human heads in complex backgrounds is proposed. By using an elliptical model for the human head, our Maximum Likelihood (ML) head detector can reliably locate human heads in images having complex backgrounds and is relatively insensitive to illumination and rotation of the human heads. Our head detector consists of two channels: the horizontal and the vertical channels. Each channel is implemented by multiscale template matching. Using a hierarchical structure in implementing our head detector, the execution time for detecting the human heads in a 512×512 image is about 0.02 second in a Sparc 20 workstation (not including the time for image acquisition). Based on the ellipse-based ML head detector, we have developed a head tracking method that can monitor the entrance of a person, detect and track the person's head, and then control the stereo cameras to focus their gaze on this person's head. In this method, the ML head detector and the mutually-supported constraint are used to extract the corresponding ellipses in a stereo image pair. To implement a practical and reliable face detection and tracking system, further verification using facial features, such as eyes, mouth and nostrils, may be essential. The 3D position computed from the centers of the two corresponding ellipses is then used for fixation. An active stereo head has been used to perform the experiments and has demonstrated that the proposed approach is feasible and promising for practical uses.

A Fast Binocular Vision Stereo Matching Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3735-3738
Author(s):  
Hui Zhang ◽  
Ling Tao Zhang ◽  
Yi Ren
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Stereo Image ◽  
Feature Points ◽  
Image Pair ◽  
Canny Operator ◽  
Canny Edge Detection ◽  
Matching Algorithm ◽  
Canny Edge ◽  
Image Edge

In this paper we present a fast indoor stereo matching algorithm based on canny edge detection and line moments. We first detect image edge by using Canny operator, then find the target objects according line moments, the feature points of the objects’ contours are extracted. Finally, matching the pixel in stereo image pair according the angle vector. The algorithm effectively reduces the computational complexity, computational cost is decreased greatly. The experimental results show that the algorithm is possible and valid.

Stereo Image Ranging For An Autonomous Robot Vision System

1985 ◽  
Author(s):  
James R. Holten III ◽  
Steven K. Rogers ◽  
Matthew Kabrisky ◽  
Steven Cross
Keyword(s):  
Vision System ◽  
Robot Vision ◽  
Autonomous Robot ◽  
Stereo Image
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