17 November 1995 Region growing algorithm to detect segments featuring low contrast in multispectral images
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Abstract
The detection of image segments featuring low contrast is a task related to the behavior of the mammalian visual system which localizes contours where changes of contrast occur. In the first part, this paper describes how early-vision mechanisms in the mammalian visual system detect local changes in the image intensity gradient. Two definitions are proposed: (1) biological plausible contour detection algorithm; and (2) biologically compatible segmentation algorithm. In the second part of this paper, a new segmentation method, which features biological compatibility, is presented. This procedure detects image regions characterized by Low Contrast (LC) values and it is named the Low Contrast Segmentation (LCS) algorithm. LCS employs an iterative pairwise mutually best merge criterion to merge segment pairs, and the Normalized Vector Distance (NVD) metric to provide a normalized distance measurement between pairs of multivalued vectors. The relevant aspects of NVD is that it supports the independent detection of chromatic and achromatic contrast, which are further combined into a single contrast coefficient. Therefore, NVD makes LCD able to process multispectral as well as monochromatic images. In terms of user interaction, LCS is robust and easy to use, because it requires only two user-defined parameters, both having an intuitive physical meaning and featuring adaptively to local statistics. An example shows the LCS performance in comparison with those of other segmentation algorithms.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrea Baraldi, Flavio Parmiggiani, "Region growing algorithm to detect segments featuring low contrast in multispectral images", Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226840; https://doi.org/10.1117/12.226840
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