Optimal depth estimation by combining focus measures using genetic programming

Mahmood, Muhammad Tariq; Majid, Abdul; Choi, Tae‐Sun

doi:10.1016/j.ins.2010.11.039

Cited by 46 publications

(23 citation statements)

References 41 publications

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“…Unfortunately this may also cause smoothing of important image structures such that the resulting images appear blurred and not sharp everywhere. Hence, researchers came up with the idea of not applying the smoothness constraint on the resulting image itself, but on the per-pixel decision of the in-focus areas: In [13,14,15,16,17,18] the authors determine an initial decision map by means of a specific sharpness criterion. Subsequently they segment these maps into regions that belong to the same input frames.…”

Section: Related Workmentioning

confidence: 99%

“…In [16], Shim and Choi introduce a novel iterative algorithm to reconstruct the 3-D shape. Mahmood et al [17] propose a combination of different focus measures for constructing the optimal decision map through genetic programming. Mahmood and Choi [18] employ 3-D anisotropic diffusion to enhance the input images, and in turn, to obtain an accurate decision map.…”

Section: Related Workmentioning

confidence: 99%

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A focus fusion framework with anisotropic depth map smoothing

Boshtayeva

Hafner

Weickert

2015

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A focus fusion framework with anisotropic depth map smoothing

Boshtayeva

Hafner

Weickert

2015

Pattern Recognition

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“…The gradient method ( Tenengrad ) was investigated in the early seventies (Tenenbaum, ) and during the eighties (Schlag et al ., ; Krotkov, ). Since that time various authors have implemented and used this method within focusing computations (Valdecasas et al ., ; Thelen et al ., ; Mahmood et al ., ). The method employs the Sobel operators to approximate gradients in the x‐ and y‐directions.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional reconstructions of solid surfaces using conventional microscopes

Ficker

Martišek

2015

Scanning

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Summary:The three-dimensional digital replicas of solid surfaces are subject of interest of different branches of science and technology. The present paper in its introductory parts brings an overview of the various microscopic reconstructive techniques based on optical sectioning. The main attention is devoted to conventional reconstruction methods and especially to that one employing the Fourier transform. The threedimensional replicas of this special reconstructive frequency method are compared graphically and numerically with the three-dimensional replicas of the confocal method. Based on the comparative study it has been concluded that the quality of the conventional replicas of surfaces possessing textures of intermediate height irregularities is acceptable and almost comparable with the quality of confocal replicas. This study is relevant both for identifying a convenient technique that provides good qualities of three-dimensional replicas and for selecting the hardware whose price is affordable even for small research groups studying rougher surface textures. SCANNING 38: 21-35, 2016.

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“…GP approach works on the principles of natural selection and recombination to search the space for possible solutions and widely used in the applications of image processing, pattern recognition, and computer vision [11]. In proposed scheme, an Optimal Composite Morphological Supervised Filter (F OCMSF ) is evolved through a certain number of generations by combining the gray-scale MM operators under a particular fitness criterion.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Based Composite Filter for Rician Noise Reduction

Sharif

Jaffar

Mahmood

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Composite filters based on Mathematical Morphological (MM) operators are getting considerable attraction in denoising Magnetic Resonance (MR) images. However, most of the approaches depend on pre-fixed combination of MM operators. In this paper, we propose a genetic programming (GP) based approach for denoising MR images. An Optimal Composite Morphological Supervised Filter FOCMSF is developed through a certain number of generations by combining the gray-scale MM operators under a fitness criterion. The proposed method does not need any prior information about the noise variance. The improved performance of the developed filter is investigated using the standard MRI data sets and its performance is compared with previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed GP based scheme over the existing approaches.

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Optimal depth estimation by combining focus measures using genetic programming

Cited by 46 publications

References 41 publications

A focus fusion framework with anisotropic depth map smoothing

A focus fusion framework with anisotropic depth map smoothing

Three‐dimensional reconstructions of solid surfaces using conventional microscopes

Genetic Programming Based Composite Filter for Rician Noise Reduction

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