Invariant feature extraction for 3D texture analysis using the autocorrelation function

Brochard, Jacques; Khoudeir, Majdi; Augereau, Bertrand

doi:10.1016/s0167-8655(01)00015-0

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…Certainly, the autocorrelation has already been used in image and video processing (Brochard et al, 2001;Popovici and Thiran, 2001;Horikawa, 2004b;Toyoda and Hasegawa, 2007;Kameyama and Phan, 2013;Yi and Pavlovic, 2013;Haouas et al, 2016). Brochard et al (2001) present a method for feature extraction from texture images. The method is invariant to affine transformations, this being achieved by transforming the autocorrelation function (ACF) and then by determining an invariant criterion which is the sum of the coefficients of the discrete correlation matrix.…”

Section: Autocorrelation In Image Analysismentioning

confidence: 99%

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

Ionescu

Mothe

et al. 2016

Artif Intell Rev

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Section: Autocorrelation In Image Analysismentioning

confidence: 99%

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

Ionescu

Mothe

et al. 2016

Artif Intell Rev

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“…As shown by Brochard et al [37], analyses of ACF surfaces can reveal a great deal of information pertaining to the spatial arrangement of the surface features. Unfortunately, the boundary conditions used in the ACF calculation have a strong influence on the results.…”

Section: Autocorrelation Function Approachmentioning

confidence: 99%

Fundamental relationships between deformation-induced surface roughness, critical strain localisation and failure in AA5754-O

Stoudt¹,

Hubbard²

2009

Philosophical Magazine

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This research employs two approaches to characterise the apparent structure observed in localised strain maps constructed from surface topography data acquired from AA5754-O sheet stock that was deformed in three in-plane stretching modes. The first uses a conventional two-point autocorrelation function (ACF), while the second uses the combination of the eigenvalue spectrum associated with each map and information theory. The results from the ACF analysis are inconclusive, implying that this technique lacks the sensitivity necessary to quantify the relationships between multi-point clustering and strain localisation. The information theory-based approach reveals that the relative spectral entropy increases monotonically, attains a maximum and then decreases sharply to the failure strain. This behaviour occurs in all three strain modes and results from two competing processes: one where the formation of structure is favourable and one where it is not. The crossover point is a clear indicator of the onset of critical strain localisation and, therefore, can be regarded as a precursor to failure because once the dominant process shifts, additional strain results in the precipitate formation of a critical strain localisation event.

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“…Computation of invariant features is mostly done either by using moments, through harmonic analysis, or by using the autocorrelation function (ACF). 22 In contrast, a feature normalization technique is applied. 23 To reduce the influence of the variances of the amplitudes and offsets, before computing the exponential component parameters, the energy feature curves in the four directions are normalized by…”

Section: Surface Roughness Extraction From Texture Featuresmentioning

confidence: 99%

Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture

Tian

Gledhill

et al. 2006

Appl. Opt.

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Surface speckle pattern intensity distribution resulting from laser light scattering from a rough surface contains various information about the surface geometrical and physical properties. A surface roughness measurement technique based on the texture analysis of surface speckle pattern texture images is put forward. In the surface roughness measurement technique, the speckle pattern texture images are taken by a simple setup configuration consisting of a laser and a CCD camera. Our experimental results show that the surface roughness contained in the surface speckle pattern texture images has a good monotonic relationship with their energy feature of the gray-level co-occurrence matrices. After the measurement system is calibrated by a standard surface roughness specimen, the surface roughness of the object surface composed of the same material and machined by the same method as the standard specimen surface can be evaluated from a single speckle pattern texture image. The robustness of the characterization of speckle pattern texture for surface roughness is also discussed. Thus the surface roughness measurement technique can be used for an in-process surface measurement.

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Invariant feature extraction for 3D texture analysis using the autocorrelation function

Cited by 13 publications

References 17 publications

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

Fundamental relationships between deformation-induced surface roughness, critical strain localisation and failure in AA5754-O

Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture

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