ArticleJournal of imaging2021
Colour and Texture Descriptors for Visual Recognition: A Historical Overview.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Three-dimensional reconstruction of densely planted rice seedlings based on MultiView images.Plant phenomics (Washington, D.C.) · 2025Article
- The untapped potential of dosomics for theranostics: shaping the future of personalized medicine.European journal of nuclear medicine and molecular imaging · 2025Article
- Convolutional Fine-Tuned Threshold Adaboost approach for effectual content-based image retrieval.Scientific reports · 2025Article
- Analysis of coat texture characteristics of bread wheat grains obtained from digital images.Frontiers in plant science · 2025Article
- Deep Learning and Entropy-Based Texture Features for Color Image Classification.Entropy (Basel, Switzerland) · 2022Article
- On the Quantification of Visual Texture Complexity.Journal of imaging · 2022Article
- Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification.Journal of imaging · 2022Article
- Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Article
- Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.