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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/7/11/245 |
| _version_ | 1797509771190861824 |
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| author | Francesco Bianconi Antonio Fernández Fabrizio Smeraldi Giulia Pascoletti |
| author_facet | Francesco Bianconi Antonio Fernández Fabrizio Smeraldi Giulia Pascoletti |
| author_sort | Francesco Bianconi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:23:28Z |
| format | Article |
| id | doaj.art-34d5bd7ebdbc413db9437a2fd2645c97 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T05:23:28Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-34d5bd7ebdbc413db9437a2fd2645c972023-11-22T23:52:31ZengMDPI AGJournal of Imaging2313-433X2021-11-0171124510.3390/jimaging7110245Colour and Texture Descriptors for Visual Recognition: A Historical OverviewFrancesco Bianconi0Antonio Fernández1Fabrizio Smeraldi2Giulia Pascoletti3Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06135 Perugia, ItalySchool of Industrial Engineering, Universidade de Vigo, Rúa Maxwell s/n, 36310 Vigo, SpainSchool of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London E1 4NS, UKDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyColour 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.https://www.mdpi.com/2313-433X/7/11/245texturecolourvisual recognitiondeep learning |
| spellingShingle | Francesco Bianconi Antonio Fernández Fabrizio Smeraldi Giulia Pascoletti Colour and Texture Descriptors for Visual Recognition: A Historical Overview Journal of Imaging texture colour visual recognition deep learning |
| title | 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_short | Colour and Texture Descriptors for Visual Recognition: A Historical Overview |
| title_sort | colour and texture descriptors for visual recognition a historical overview |
| topic | texture colour visual recognition deep learning |
| url | https://www.mdpi.com/2313-433X/7/11/245 |
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