A recent survey on the applications of genetic programming in image processing

Autor: Aqsa Saeed Qureshi, Asifullah Khan, Mutawarra Hussain, M. Y. Hamza, Noorul Wahab
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Computer Science - Artificial Intelligence
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Multispectral image
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Genetic programming
Feature selection
Image processing
Computational intelligence
02 engineering and technology
Machine learning
computer.software_genre
Field (computer science)
Artificial Intelligence
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
business.industry
Computational Mathematics
Artificial Intelligence (cs.AI)
Pattern recognition (psychology)
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Image compression
Zdroj: Computational Intelligence. 37:1745-1778
ISSN: 1467-8640
0824-7935
Popis: Genetic Programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree-type structure. Similarly, research is also gaining momentum in the field of Image Processing, because of its promising results over vast areas of applications ranging from medical Image Processing to multispectral imaging. Image Processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, i.e., complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for Image Processing since its inception. Many interesting GP techniques have been developed and employed in the field of Image Processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in Image Processing and provides useful resources for further research. Also, the comparison of different parameters used in different applications of Image Processing is summarized in tabular form. Moreover, analysis of the different parameters used in Image Processing related tasks is carried-out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in Image Processing but also in other fields, may increase. Additionally, guidelines are provided for applying GP in Image Processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.
31 pages, 12 figures, and 1 table
Databáze: OpenAIRE
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