Q-statistics in dynamic speckle pattern analysis

Rabal, H.; Cap, Nelly; Trivi, Marcelo; Guzmán, Marcelo Nicolás

doi:10.1016/j.optlaseng.2012.01.010

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Ansari et al [ 31 ] reported about Q-statistic concept to improve the performance of generalized differences algorithm based on intensity histogram for imaging functional blood vessel structures in a rodent window chamber of a mice. Rabal et al [ 32 ] introduced Q-statistic concepts to improve the performance of some methods based on the histogram to estimate dynamic speckle activity.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Han

2018

Sensors

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Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Han

2018

Sensors

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“…As described by Rabal et al [11], the statistical techniques are indicated for data with random nature and with time evolution, which is the case with dynamic laser speckle. As described by Rabal et al [11], the statistical techniques are indicated for data with random nature and with time evolution, which is the case with dynamic laser speckle.…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis in the spectral analysis of the dynamic laser speckle patterns

Ribeiro

Braga

Horgan

et al. 2014

J. Eur. Opt. Soc.-Rapid Publ.

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Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

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Advances in Speckle Metrology

Joenathan

Sirohi

Bernal

2015

The Optics Encyclopedia

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The field of speckle metrology has seen a surge in development in the past 10 years owing to advancements in digital cameras and computing power. For instance, a variety of digital speckle correlation (DSC) processes can now be performed in almost real time and therefore speckle techniques has found applications in nondestructive testing (NDT), biology, and medicine. New techniques in terms of optical arrangements and in extracting phase data have been introduced for measuring out‐of‐plane and in‐plane motions in digital speckle interferometry (DSPI) and digital speckle shear interferometry (DSSPI). DSC, DSPI, and DSSPI have also been combined to extend the range of measurements. In addition, quantitative analysis in speckle interferometry has seen a plethora of new software developments for conducting real‐time, accurate, complicated, fast, and repeatable measurements with ease. In addition, different methods to process speckle fringes have made analysis equivalent to that of classical interferometry, yet can be applied to all types of real‐world specimen.

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Q-statistics in dynamic speckle pattern analysis

Cited by 7 publications

References 9 publications

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Principal component analysis in the spectral analysis of the dynamic laser speckle patterns

Advances in Speckle Metrology

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