Improvements in the Visualization of Segmented Areas of Patterns of Dynamic Laser Speckle

Passoni, Lucía Isabel; Pra, Ana Lucía Dai; Ballarin, Virginia L.; Meschino, Gustavo J.; Weber, Christian; Guzmán, Marcelo Nicolás; Rabal, H.; Trivi, Marcelo

doi:10.1007/978-3-642-35230-0_17

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…The proposed method to improve the image quality has been utilised in astronomy since the middle of the last century when the CCD was first developed [30]. Therefore, this method is often called Shift-and-Add (SAA).…”

Section: Shift-and-add Speckle Imaging Algorithmsmentioning

confidence: 99%

Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging

Świłło,

Cacko

2023

Adv. Sci. Technol. Res. J.

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The sheet metal surface crack detection during manufacturing is an essential issue because of both the product quality and process productivity. Development of solutions to eliminate defective products during the metal forming process is crucial for the smooth production and for developing an appropriate tool geometry in the initial phase of the process. Currently, the methods of surface crack detection used in the industry are mostly related to visual inspection. These are methods that require operators of industrial facilities considerable attention and effort to capture emerging discontinuities on the sheet metal surface. Also, this situation results increase in the duration of the specific operations of stamping and significantly reduces productivity. Therefore, an industrial application of a non-contact laser technique that simultaneously provides the results of the speckle imaging is presented. The authors demonstrate a specially designed machine vision system along with experimental tools for the stamping operation. Proposed solution uses the phenomenon of speckle pattern that appears in the image of the investigated sheet surface produced by the laser beam emission. In this method, coherent laser light is emitted to the surface, where a speckle pattern is generated due to scatter reflection from the sheet metal surface and then, shift-and-add technique and image processing is applied. The proposed measurement technique consists, initially, of making a sequence of images of the tested object for the moving surface of the sheet. Secondly, the object's displacement quantity in each image is determined, and the position is corrected. The test object in each image is moved to the starting position, and all images are superimposed. It allows to obtain a high-quality image with visible surface defects. Finally, the dynamically changing speckle pattern intensity is evaluated using Gaussian-of-Laplacian edge detection to investigate a surface crack location due to the surface discontinues and light scattering. This process is recommended for machine vision imaging of distant objects, which works well in industrial conditions as well as online analysis. Also, from the speckle size measurement, an experimental procedure is employed to verify the best condition for vision system resolution.

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Section: Shift-and-add Speckle Imaging Algorithmsmentioning

confidence: 99%

Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging

Świłło,

Cacko

2023

Adv. Sci. Technol. Res. J.

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“…The coding is such that topological nearby cells will have similar colours and those far away according to this criterion will have distinct colours. Using this coloured map, a colour can be assigned to each input data according their BMUs (Passoni et al 2013b).…”

Section: Pseudo Colouring By Self-organizing Mapsmentioning

confidence: 99%

“…We propose a method for performing the visualization of sample regions using image sequences of laser speckle patterns coming from different situations: a) activity test in a simplified situation using a paint drying experiment, b) inhomogeneous illuminated surface of a wet painted sample, and c) an active biologic specimen (fruit). Previously, bacteria (Guzmán et al 2010) and seeds (Passoni et al 2013b) were also tested and preliminary results were presented.…”

Section: Introductionmentioning

confidence: 99%

Dynamic speckle image segmentation using self-organizing maps

Pra

Meschino

Guzmán

et al. 2016

J. Opt.

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The aim of this work is to build a computational model able to automatically identify, after training, dynamic speckle pattern regions with similar properties. The process is carried out using a set of descriptors applied to the intensity variations with time in every pixel of a speckle image sequence. An image obtained by projecting a self-organized map is converted into regions of similar activity that can be easily distinguished. We propose a general procedure that could be applied to numerous situations. As examples we show different situations: (a) an activity test in a simplified situation; (b) a non-biological example and (c) biological active specimens. The results obtained are encouraging; they significantly improve upon those obtained using a single descriptor and will eventually permit automatic quantitative assessment.

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“…Artificial intelligence techniques have been incorporated, along with learning algorithms (supervised and nonsupervised), to segment and identify regions of interest within data. 2,3 However, there is not a unique strategy for analyzing dynamic speckle images applicable to a wide variety of situations.…”

Section: Introductionmentioning

confidence: 99%

Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis

et al. 2016

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Abstract. Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial-temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.

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Improvements in the Visualization of Segmented Areas of Patterns of Dynamic Laser Speckle

Cited by 4 publications

References 14 publications

Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging

Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging

Dynamic speckle image segmentation using self-organizing maps

Characterization of spatial–temporal patterns in dynamic speckle sequences using principal component analysis

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