Evolutionary image processing: Difference between revisions

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In evolutionary image processing, genetic programming optimizes the arrangement of different image-processing operators for specific outputs or task performance.<ref name=gpipreview>{{cite journal |last1=Khan |first1=Asifullah |last2=Qureshi |first2=Aqsa Saeed |last3=Wahab |first3=Noorul |last4=Hussain |first4=Mutawarra |last5=Hamza |first5=Muhammad Yousaf |title=A recent survey on the applications of genetic programming in image processing |journal=Computational Intelligence |date=2021 |volume=37 |issue=4 |pages=1745–1778 |doi=10.1111/coin.12459 |url=https://onlinelibrary.wiley.com/doi/abs/10.1111/coin.12459 |language=en |issn=1467-8640}}</ref> In particular, GP has been used for developing accurate classifiers for [[object detection]], classification of medical images, and optical character recognition. GP has multiple advantages in case of image processing.<ref name=gpipreview/> They include:

# The GP output is a program or a collection of programs in the form of mathematical expressions, which are easy to interpret after simplification and conversion to normal notation.

# The GP needs considerable time for evolution of GP based classifiers. HovewerHowever, the the resulting GP tree needs very short execution time in the testing.

# GP fitness function is flexible and can be adapted according to the problem to be solved.

The disadvantages of GP for image processing include: