Estimation of the Color Image Gradient with Perceptual Attributes
Philippe Pujas,M.-J. Aldon +1 more
- pp 103-110
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TLDR
Classical gradient operators are generally defined for grey level images and are very useful for image processing such as edge detection, image segmentation, data compression and object extraction.Abstract:
Classical gradient operators are generally defined for grey level images and are very useful for image processing such as edge detection, image segmentation, data compression and object extraction. Some attempts have been made to extend these techniques to multi-component images. However, most of these solutions do not provide an optimal edge enhancement.read more
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Journal ArticleDOI
A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI
Using Canny's criteria to derive a recursively implemented optimal edge detector
TL;DR: It is shown that a solution to Canny's precise formulation of detection and localization for an infinite extent filter leads to an optimal operator in one dimension, which can be efficiently implemented by two recursive filters moving in opposite directions.
Proceedings ArticleDOI
Color gamut transform pairs
TL;DR: A set of alternative models of the RGB monitor gamut based on the perceptual variables hue, saturation, and value (V) or brightness (L) are presented and algorithms for transforming between these models are derived.
Journal ArticleDOI
A note on the gradient of a multi-image
TL;DR: Gradient based edge detection techniques can be extended to multispectral images in various ways: difference operators can be applied to each component of a multi-image, and the results can be combined, e.g., taking the RMS or the sum, or the maximum of their absolute values.
Journal ArticleDOI
Color information for region segmentation
TL;DR: In this article, a set of color features, (R + G + B) 3, R − B, and (2G − R− B) 2, were derived by systematic experiments of region segmentation.