Publication

Segment-Based Real-Time Stereo Vision Matching using Characteristics Vectors

Jean-Philippe Thiran
2011
Journal Articles

Abstract

Stereo vision is a usual method to obtain depth information from images. The problems encountered when applying well established algorithms to real-time applications are due to the high computational load required. In this article we address this issue by performing a region-based analysis which considers each pixel only once. Additionally, matching is carried out over statistical descriptors of the image regions. In this paper we present a new algorithm that has been specifically designed to solve some commonly observed problems which arise from other well known techniques. This algorithm was designed using a previous algorithm implemented by the authors. The complete analysis has been carried out over gray scale images. The results obtained from both real and synthetic images are presented in terms of matching quality and time consumption and are compared to other published results. Finally, a discussion on additional features related to the matching process is provided.

Official source

https://infoscience.epfl.ch/entities/publication/e46caa56-fd3e-42d9-842f-54d19cc6e85e

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Jean-Philippe Thiran
2011
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e46caa56-fd3e-42d9-842f-54d19cc6e85e

About this result

Related concepts (27)

Computer stereo vision

Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera. By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels. This is similar to the biological process of stereopsis. In traditional stereo vision, two cameras, displaced horizontally from one another, are used to obtain two differing views on a scene, in a manner similar to human binocular vision.

Image stitching

Image stitching or photo stitching is the process of combining multiple photographic s with overlapping fields of view to produce a segmented panorama or high-resolution image. Commonly performed through the use of computer software, most approaches to image stitching require nearly exact overlaps between images and identical exposures to produce seamless results, although some stitching algorithms actually benefit from differently exposed images by doing high-dynamic-range imaging in regions of overlap.

Image segmentation

In and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

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