2014 IEEE Symposium on Computational Intelligence for Human-Like Intelligence (CIHLI) 2014
DOI: 10.1109/cihli.2014.7013395
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Simplified firefly algorithm for 2D image key-points search

Abstract: Abstract-In order to identify an object, human eyes firstly search the field of view for points or areas which have particular properties. These properties are used to recognise an image or an object. Then this process could be taken as a model to develop computer algorithms for images identification. This paper proposes the idea of applying the simplified firefly algorithm to search for key-areas in 2D images. For a set of input test images the proposed version of firefly algorithm has been examined. Research… Show more

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Cited by 22 publications
(15 citation statements)
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References 51 publications
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“…Firefly Algorithm was used in image compression [11], gray-scale image watermarking [14] and Key-Point classification [27], [17]. Cuckoo Search Algorithm has efficient application in intelligent video target racking [22], satellite image segmentation [2] and recognition [29].…”
Section: Related Workmentioning
confidence: 99%
“…Firefly Algorithm was used in image compression [11], gray-scale image watermarking [14] and Key-Point classification [27], [17]. Cuckoo Search Algorithm has efficient application in intelligent video target racking [22], satellite image segmentation [2] and recognition [29].…”
Section: Related Workmentioning
confidence: 99%
“…This paper describes an application of CI methods combined with a simplified sobel filter, as a dedicated solution that creates a 2D input image recognition system. The Firefly Algorithm (FA), originally proposed in [13], was efficiently applied for various purposes: to solve the travelling salesman problem [14], image compression [15], threshold selection [16], benchmark tests [17] and feature extraction [18], [19], [20], [21]. Therefore, we present a novel approach to feature extraction for future object classification that is based on the application of FA with a simplified sobel filter.…”
Section: Introductionmentioning
confidence: 99%
“…Several methods based on more specific image acquisition are reported in [7], [8], [9], [10], [11], [12], [13]. Wen and Tao developed a nearinfrared vision system for automatic apple defect inspection, see [14], While some recent advances in feature extraction for images and biometrics are reported in [15], [16], [17], [18], [19]. Zion et al introduced a computerised method to detect the bruises of Jonathan, Golden Delicious, and Hermon apples from magnetic resonance images by threshold technique.…”
Section: Introductionmentioning
confidence: 99%