Real-time Motion Stereo on SFU Pyramid

doi:10.1006/rtim.1995.1040

Real-Time Imaging

Volume 1, Issue 6, December 1995, Pages 385-396

https://doi.org/10.1006/rtim.1995.1040 Get rights and content

Abstract

In a manufacturing environment, objects are often presented to inspection systems via conveyor belts. If multiple snapshots of a moving object are taken by a fixed camera, the motion of the belt provides the necessary stereo disparity. Moreover, it also guarantees that the disparity occurs only along epipolar lines. This method is called motion stereo.

A simple algorithm for calculating depth from motion stereo was initially implemented which makes the assumption that the incremental disparity is less than the minimum distance between edges. To relax this constraint, a more general multi-scale algorithm is developed. Reduced images are matched first and this guides the matching of subsequent images. The parallel and hierarchical algorithm facilitates a tight pipeline and interactions among multiple motion stereo images and their multi-scale versions in a combined bottom-up and top-down manner in a pyramidal framework.

The two algorithms have been tested on the SFU pyramidal vision machine which was recently completed for real-time computer vision applications. The total processing time was 50 ms per image for the simple algorithm and approximately 0.5s per image for the multi-scale algorithm.

References (0)

Cited by (4)

From nomad to explorer: Active object recognition on mobile robots
1998, Pattern Recognition
This paper demonstrates the importance of active behavior in machine vision systems. It proposes using activity of the observer for object finding and recognition. The presented robotic system is based on active and qualitative vision approaches which are supplemented by a hierarchical, multiresolution image representation and a parallel implementation of the active vision algorithms. The system uses a hierarchical search strategy to locate and then identify objects in a three-dimensional space. The paper describes the recognition algorithm and its parallel implementation on the SFU hybrid pyramid vision machine.
We also introduce a new method for depth recovery—Stereo from Head and Body Movements (SHABM). As shown in our analysis and experiment, it provides a practical and economic means of 3-D sensing on ordinary (wheeled) mobile robots.
Image Analysis and Computer Vision: 1996
1997, Computer Vision and Image Understanding
This paper presents a bibliography of nearly 2150 references related to computer vision and image analysis, arranged by subject matter. The topics covered include computational techniques; feature detection and segmentation; image and scene analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, including geometry and graphics, compression and processing, sensors and optics, visual perception, neural networks, artificial intelligence and pattern recognition, as well as on applications.
Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels
2023, IET Image Processing
Classification and overview of research in real-time imaging
1996, Journal of Electronic Imaging

View full text

Regular ArticleReal-time Motion Stereo on SFU Pyramid

Abstract

Regular Article
Real-time Motion Stereo on SFU Pyramid