Analysis of motion detectors evolved by Genetic Programming

Shi, Qiao; Yin, Wotao; Song, Andy

doi:10.1109/cec.2012.6256535

Cited by 2 publications

(1 citation statement)

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“…In this approach, Gaussian noise was added in video [96] and showed better results for detecting motion in different environments. In another work [101], GP program was used for analyzing various type of motion detection techniques such as detecting simple motion, detection of fast-moving objects, motion detection from noisy background. Another advantage of using GP for motion detection is that the evolved detectors can also tolerate noise, that is why GP may considered as one of the best approach for detection of motion .…”

Section: Gp In Motion Detectionmentioning

confidence: 99%

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

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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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, that is, 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. In addition, 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. In addition, 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.

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Section: Gp In Motion Detectionmentioning

confidence: 99%