Feature Level Fusion of Night Vision Images Based on K-Means Clustering Algorithm

Khan, Asifullah; Kayani, Bushra N.; Gillani, Asif M.

doi:10.1007/978-1-4020-6268-1_14

Cited by 2 publications

(1 citation statement)

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“…The choice of segmentation algorithm used in regionbased image fusion directly affects the fusion result. Segmentation algorithms which have been used in regionbased image fusion algorithms include watershed [30], K-means [31], texture-based [32], pyramidal linking [1], and mean-shift segmentation [33]. In this paper, meanshift segmentation is used for all region-based approaches because of its robustness [34,35].…”

Section: Parameterized Logarithmic Region-based Image Fusionmentioning

confidence: 99%

Multiresolution Decomposition Schemes Using the Parameterized Logarithmic Image Processing Model with Application to Image Fusion

Nercessian

Panetta

Agaian

2010

EURASIP J. Adv. Signal Process.

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New pixel-and region-based multiresolution image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing images. A mathematical analysis shows that the Logarithmic Image Processing (LIP) model and standard mathematical operators are extreme cases of the PLIP model operators. Moreover, the PLIP model operators also have the ability to take on cases in between LIP and standard operators based on the visual requirements of the input images. PLIP-based multiresolution decomposition schemes are developed and thoroughly applied for image fusion as analysis and synthesis methods. The new decomposition schemes and fusion rules yield novel image fusion algorithms which are able to provide visually more pleasing fusion results. LIP-based multiresolution image fusion approaches are consequently formulated due to the generalized nature of the PLIP model. Computer simulations illustrate that the proposed image fusion algorithms using the Parameterized Logarithmic Laplacian Pyramid, Parameterized Logarithmic Discrete Wavelet Transform, and Parameterized Logarithmic Stationary Wavelet Transform outperform their respective traditional approaches by both qualitative and quantitative means. The algorithms were tested over a range of different image classes, including out-of-focus, medical, surveillance, and remote sensing images.

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