Multidimensional Co-occurrence Matrices for Object Recognition and Matching

Kovalev, Vassili; Πέτρου, Μαρία

doi:10.1006/gmip.1996.0016

Cited by 75 publications

(34 citation statements)

References 6 publications

(19 reference statements)

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“…They used a supervised parametric method based on a distribution to guide a forward sequential search algorithm in the object detection phase. Kovalev and Petrou [44] used multidimensional GLCM to perform classification of various images of CT brain scans, several types of microscope images, and photographs of signatures. Haddon and Boyce [32], [33] proposed one interesting application of cooccurrence matrices in which the matrices were used to detect edges and estimate optic flow field.…”

Section: A Applicationsmentioning

confidence: 99%

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Soh

Tsatsoulis

1999

IEEE Trans. Geosci. Remote Sensing

1,035

492

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Abstract-This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. We conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures. We showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, we developed three GLCM implementations and evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured. These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM.

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Section: A Applicationsmentioning

confidence: 99%

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Soh

Tsatsoulis

1999

IEEE Trans. Geosci. Remote Sensing

1,035

492

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“…The general form can be limited to different orders to define alternative texture descriptors. For example, co-occurrence matrices (Haralick et al, 1973a;Kovalev and Petrou, 1996;Strand and Taxt, 1994) are defined by considering only two points. That is, f ðc 1 ; c 2 ;sÞ for s ¼ jx 2 À x 1 j ð 1Þ for values of s defining neighbourhoods of 3 · 3 or 4 · 4 pixels.…”

Section: Statistical Characterisation Of Texturesmentioning

confidence: 99%

Texture classification via conditional histograms

Montiel

Aguado

Nixon

2005

Pattern Recognition Letters

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This paper presents a non-parametric discrimination strategy based on texture features characterised by one-dimensional conditional histograms. Our characterisation extends previous co-occurrence matrix encoding schemes by considering a mixture of colour and contextual information obtained from binary images. We compute joint distributions that define regions that represent pixels with similar intensity or colour properties. The main motivation is to obtain a compact characterisation suitable for applications requiring on-line training. Experimental results show that our approach can provide accurate discrimination. We use the classification to implement a segmentation application based on a hierarchical subdivision. The segmentation handles mixture problems at the boundary of regions by considering windows of different sizes. Examples show that the segmentation can accurately delineate image regions.

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“…The commonly known histograms of Local Binary Patterns (Pietikäinen, M., 2011) as well as 2D version of extended cooccurrence matrices (Kovalev, V., 1996;Kovalev, V., 2001) which fuse the intensity, gradient magnitude, and anisotropy image properties were used as image descriptors. In addition, we calculated also the commonly known Histograms of Oriented Gradients and Banks of Filters.…”

Section: Conventional Methodsmentioning

confidence: 99%

Lesion Detection in Ct Images Using Deep Learning Semantic Segmentation Technique

Kalinovsky

Liauchuk

Tarasau

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

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Multidimensional Co-occurrence Matrices for Object Recognition and Matching

Cited by 75 publications

References 6 publications

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

Texture classification via conditional histograms

Lesion Detection in Ct Images Using Deep Learning Semantic Segmentation Technique

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