Title: A Recent Survey on the Applications of Genetic Programming in Image Processing,
Abstract: During the last two decades, Genetic Programming (GP) has been largely used
to tackle optimization, classification, and automatic features selection
related tasks. The widespread use of GP is mainly due to its flexible and
comprehensible tree-type structure. Similarly, research is also gaining
momentum in the field of Image Processing (IP) because of its promising results
over wide areas of applications ranging from medical IP to multispectral
imaging. IP is mainly involved in applications such as computer vision, pattern
recognition, image compression, storage and transmission, and medical
diagnostics. This prevailing 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 IP since its inception. Many interesting GP
techniques have been developed and employed in the field of IP. To give the
research community an extensive view of these techniques, this paper presents
the diverse applications of GP in IP and provides useful resources for further
research. Also, comparison of different parameters used in ten different
applications of IP are summarized in tabular form. Moreover, analysis of
different parameters used in IP related tasks is carried-out to save the time
needed in future for evaluating the parameters of GP. As more advancement is
made in GP methodologies, its success in solving complex tasks not only related
to IP but also in other fields will increase. Additionally, guidelines are
provided for applying GP in IP related tasks, pros and cons of GP techniques
are discussed, and some future directions are also set.