Detecting motion from noisy scenes using Genetic Programming

Pinto, Brian; Song, Andy

doi:10.1109/ivcnz.2009.5378389

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…images, which helped identify edges of objects and changes in colour and illumination. We used GP to evolve motion detection programs to recognize interesting motions from unstable background [4].…”

Section: Introduction and Methodologymentioning

confidence: 99%

Evolving automatic frame splitters

Song

Xie

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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This paper extends the application of Genetic Programming into a new area, automatically splitting video frames based on the content. Compared with human written video splitting programs, GP generated splitters are more accurate. Moreover these video splitting programs have high tolerance to noises. They can still achieve reasonable performance even when the noisy videos are not easily recognizable by human eyes.

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Section: Introduction and Methodologymentioning

confidence: 99%

Evolving automatic frame splitters

Song

Xie

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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“…In past, many modeling and background subtraction related techniques have been designed for motion detection. Moreover, to avoid manually coded motion detection system, different researchers used GP based automatically evolved systems [94][95][96][97][98][99]. It was observed that generally, the GP based evolved programs outperformed manually coded programs.…”

Section: Gp In Motion Detectionmentioning

confidence: 99%

“…Another difficult task in case of motion detection is to detect motion from noisy scene, when there is no information about the noise. Pinto et al [96] tackled this problem by using GP based approach in which motion detectors were generated during the testing phase on the basis of fitness function. In this approach, Gaussian noise was added in video [96] and showed better results for detecting motion in different environments.…”

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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“…One of the advantages of GP is creativity as GP often finds excellent solutions which have never been thought by human experts. The adaptation of GP in image related tasks has been also successful, including image segmentation [17], edge detection [5], texture analysis [21], motion detection [16] and finding interest points [15]. On these tasks GP could achieve better or at least comparable performance without much domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

Extracting image features for classification by two-tier genetic programming

Al-Sahaf¹,

Song²,

Neshatian³

et al. 2020

Preprint

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Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

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Detecting motion from noisy scenes using Genetic Programming

Cited by 15 publications

References 12 publications

Evolving automatic frame splitters

Evolving automatic frame splitters

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

Extracting image features for classification by two-tier genetic programming

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