Abstract
Virtual binocular sensors, composed of a camera and catoptric mirrors, have become popular among machine vision researchers, owing to their high flexibility and compactness. Usually, the tested target is projected onto a camera at different reflection times, and feature matching is performed using one image. To establish the geometric principles of the feature-matching process of a mirror binocular stereo vision system, we proposed a single-camera model with the epipolar constraint for matching the mirrored features. The constraint between the image coordinates of the real target and its mirror reflection is determined, which can be used to eliminate nonmatching points in the feature-matching process of a mirror binocular system. To validate the epipolar constraint model and to evaluate its performance in practical applications, we performed realistic matching experiments and analysis using a mirror binocular stereo vision system. Our results demonstrate the feasibility of the proposed model, suggesting a method for considerable improvement of efficacy of the process for matching mirrored features.
Highlights
With the rapid development of machine vision technology and increasing demand on three-dimensional (3-D) measurements, binocular stereo vision technology has been widely applied in many fields such as noncontact measurements, robot navigation, and on-line monitoring.[1,2,3] Traditionally, a binocular stereo vision system is composed of two cameras or a moving camera, capturing the object’s images from different directions
We develop a precise description of the epipolar constraint model of a single-camera binocular vision system
For a more scrupulous validation of the proposed mirror epipolar constraint model, three additional experiments were performed according to the same process as the first experiment
Summary
With the rapid development of machine vision technology and increasing demand on three-dimensional (3-D) measurements, binocular stereo vision technology has been widely applied in many fields such as noncontact measurements, robot navigation, and on-line monitoring.[1,2,3] Traditionally, a binocular stereo vision system is composed of two cameras or a moving camera, capturing the object’s images from different directions. Sensors that are built using two cameras are characterized by large size and poor flexibility, while those that utilize one camera lack instantaneity and synchronization. In the applications that use such virtual binocular stereo vision systems for 3-D measurements, the two pivotal tasks, calibration of the system and feature matching, are different from those used in traditional two-camera systems
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