Combining fuzzy theory and a genetic algorithm for satellite image edge detection

Jubai, A.; Bai, Jing; Yang, Jingjing

doi:10.1080/01431160600554371

Cited by 25 publications

(15 citation statements)

References 2 publications

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“…Then the matrix is enhanced to highlight the edge information. Finally, the processed membership matrix is inversely transformed into enhanced image and the edge is extracted [18]. There are some drawbacks involved in the Pal and King algorithm: (1) some gray information is lost after fuzzy-image enhancement because the range of the membership function is not strictly from 0 to 1; (2) the membership function depends on the reciprocal fuzzy factor F d and the exponential fuzzy factor F e ; however, these parameters are adjustable and there is no reasonable criterion to decide their optimal values for different images; (3) there is a lot of floating point arithmetic in the mapping of the membership matrix and the inverse transformation, which consumes much time and deteriorates a real-time property of the algorithm; and (4) the edge is enhanced via iterating many time moments, but when the times elapses, the details of the edge disappear.…”

Section: Edge Extraction For the Input Image Based On Modified Fuzzy mentioning

confidence: 99%

Measurements of image shifts using an edge-extracted joint transform correlator

Fan¹

2018

Ukr. J. Phys. Opt.

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Abstract. To measure accurately sub-pixel image shifts for the space cameras with high resolutions, an improved joint-transform correlation (JTC) algorithm is put forward, which combines fuzzy-edge detection and high-boost filtering. First we explain the principles of measurements of the image shifts based on the JTC. Considering that most of the important information is kept in and described by the edges of image, a modified fuzzy-edge detection algorithm by Pal and King is then used to extract the edge information of the input image. The joint power spectrum is subjected to high-boost filtering to enhance detail-containing image components and eliminate low-frequency noises. Finally, an experimental platform is built and the performance of image-shift measurements based on the improved JTC is studied. The results testify that the measurement errors are normally distributed, with a zero mean and mean-square deviation less than ~ 0.1 pixel, whenever the shift between two correlated images changes from 0 to 25 pixel. As a result, our JTC technique facilitates highly accurate measurements of sub-pixel image shifts.

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Section: Edge Extraction For the Input Image Based On Modified Fuzzy mentioning

confidence: 99%

Measurements of image shifts using an edge-extracted joint transform correlator

Fan¹

2018

Ukr. J. Phys. Opt.

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“…A. JUBAI et.al [31] put forward a novel algorithm which integrates uncertainty principles of fuzzy with evolutionary concepts of genetic algorithms for detection of oil spilled on sea. Authors have tried to improve Palking fuzzy edge detection method with the help of genetic algorithms.…”

Section: B Fuzzy-genetic Edge Detection Algorithmmentioning

confidence: 99%

Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid

Kaushal¹,

Akashdeep²

2013

JETWI

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Soft Computing is a multifaceted technique comprising of Fuzzy Logic, Neural Network, Genetic algorithms and other Evolutionary computation. These paradigms have found wide variety of applications in the field of image processing. One of the most vital applications of image segmentation is edge detection where edge refers to the boundary between two consistent regions and edge detection is the process of detecting and finding abrupt discontinuities in an image. This paper summarizes hybrid and non-hybrid approaches for edge detection. The objective of this paper is to survey the core issues for soft computing based approaches for edge detection.

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“…Fuzzy logic and genetic programming (GP) were used to tackle the image de-noising problem of gray level images degraded with Gaussian white noise in spatial domain, by Chaudhry et al [10]. Jubai et al [32] combined an improved fuzzy theory and a GA for the detection of oil spilled on the sea by remote sensing. Ghosh et al developed an efficient data clustering scheme based on the property of aggregation pheromone found in ants in [21].…”

Section: Introductionmentioning

confidence: 99%