Colour and Texture Descriptors for Visual Recognition: A Historical Overview

Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial (‘intelligent’) syste...

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Autores principales:	Francesco Bianconi, Antonio Fernández, Fabrizio Smeraldi, Giulia Pascoletti
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	texture colour visual recognition deep learning Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/34d5bd7ebdbc413db9437a2fd2645c97
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id	oai:doaj.org-article:34d5bd7ebdbc413db9437a2fd2645c97
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spelling	oai:doaj.org-article:34d5bd7ebdbc413db9437a2fd2645c972021-11-25T18:03:35ZColour and Texture Descriptors for Visual Recognition: A Historical Overview10.3390/jimaging71102452313-433Xhttps://doaj.org/article/34d5bd7ebdbc413db9437a2fd2645c972021-11-01T00:00:00Zhttps://www.mdpi.com/2313-433X/7/11/245https://doaj.org/toc/2313-433XColour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial (‘intelligent’) systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven (‘hand-crafted’) up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the ‘early years’ to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach.Francesco BianconiAntonio FernándezFabrizio SmeraldiGiulia PascolettiMDPI AGarticletexturecolourvisual recognitiondeep learningPhotographyTR1-1050Computer applications to medicine. Medical informaticsR858-859.7Electronic computers. Computer scienceQA75.5-76.95ENJournal of Imaging, Vol 7, Iss 245, p 245 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	texture colour visual recognition deep learning Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
spellingShingle	texture colour visual recognition deep learning Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95 Francesco Bianconi Antonio Fernández Fabrizio Smeraldi Giulia Pascoletti Colour and Texture Descriptors for Visual Recognition: A Historical Overview
description	Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial (‘intelligent’) systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven (‘hand-crafted’) up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the ‘early years’ to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach.
format	article
author	Francesco Bianconi Antonio Fernández Fabrizio Smeraldi Giulia Pascoletti
author_facet	Francesco Bianconi Antonio Fernández Fabrizio Smeraldi Giulia Pascoletti
author_sort	Francesco Bianconi
title	Colour and Texture Descriptors for Visual Recognition: A Historical Overview
title_short	Colour and Texture Descriptors for Visual Recognition: A Historical Overview
title_full	Colour and Texture Descriptors for Visual Recognition: A Historical Overview
title_fullStr	Colour and Texture Descriptors for Visual Recognition: A Historical Overview
title_full_unstemmed	Colour and Texture Descriptors for Visual Recognition: A Historical Overview
title_sort	colour and texture descriptors for visual recognition: a historical overview
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/34d5bd7ebdbc413db9437a2fd2645c97
work_keys_str_mv	AT francescobianconi colourandtexturedescriptorsforvisualrecognitionahistoricaloverview AT antoniofernandez colourandtexturedescriptorsforvisualrecognitionahistoricaloverview AT fabriziosmeraldi colourandtexturedescriptorsforvisualrecognitionahistoricaloverview AT giuliapascoletti colourandtexturedescriptorsforvisualrecognitionahistoricaloverview
_version_	1718411671057203200

Colour and Texture Descriptors for Visual Recognition: A Historical Overview

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