Article ID Journal Published Year Pages File Type
534917 Pattern Recognition Letters 2008 15 Pages PDF
Abstract

This work aims at defining a new method for matching correspondences in stereoscopic image analysis. A representation of occlusions drives the overall matching process. Based on the taxonomy proposed by Scharstein and Szelinsky (2002, IJCV, 47, 7–42), the dense stereo matching process is divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second and third phases new strategies are introduced in an attempt to improve the reliability of results. Aggregation is based on a new local matching measure, and neural techniques compute disparities adaptively. Two experimental studies were conducted to evaluate and compare the solutions proposed. The first uses a standard well-known dataset including data with true disparity maps; the second study was conducted on complex real images acquired by a scanning electron microscope (SEM).

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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