Study of GP representations for motion detection with unstable background

Song, Andy; Pinto, Brian

doi:10.1109/cec.2010.5586334

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…It was observed that generally, the GP based evolved programs outperformed manually coded programs. To tackle the unstable background (such as rainy background, moving background due to a moving camera) in motion detection, GP was employed in [100], where classification accuracy based on motion and non-motion was used as a fitness measure. Another difficult task in case of motion detection is to detect motion from noisy scene, when there is no information about the noise.…”

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%

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

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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“…Wagner and Michalewicz proposed GP with adaptive windowing for forecasting in a dynamic environment [7]. Song and Pinto applied GP video frames for motion detection problems [5]. This study was based on pixel values where the evolved GP detectors operated on points rather than regions.…”

Section: Background and Related Workmentioning

confidence: 99%

Event detection in time series by genetic programming

Xie

Song

Ciesielski

2012

2012 IEEE Congress on Evolutionary Computation

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The aim of event detection in time series is to identify particular occurrences of user-interest in one or more time lines, such as finding an anomaly in electrocardiograms or reporting a sudden variation of voltage in a power supply. Current methods are not adequate for detecting certain kinds of events without any domain knowledge. Therefore, we propose a Genetic Programming (GP) based event detection methodology in which solutions can be built from raw time series data. The framework is applied to five synthetic data sets and one real world application. The experimental results show that working on raw data even with a dimensionality as high as 140 × 80, genetic programming can achieve superior performance to conventional methods operating on pre-defined features. Furthermore, analysis of the evolved event detectors shows that they have captured the regularities inserted into the synthetic data sets and some individuals can be readily understood by humans. Index Terms-genetic programming; feature extraction; event detection; time series.

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