Texture Analysis

Tüceryan, Mihran; Jain, Anil K.

doi:10.1142/9789812384737_0007

Cited by 273 publications

(219 citation statements)

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“…A very wide set of techniques does exist to address quantitative description of textural features (Tuceryan and Jain, 1998). A first group of techniques relies on feature extraction and so-called blob analysis, whereas another group readily computes spatial statistics (e.g.…”

Section: Grey Level Intercepts For Texture Analysismentioning

confidence: 99%

Particle texture analysis using polarized light imaging and grey level intercepts

Pirard

Lebichot

Krier

2007

International Journal of Mineral Processing

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The usual characterization of particulate ore material with image analysis includes modal analysis and liberation analysis. Both methods include stereological corrections based on intercept length distributions within each phase of interest. The first principle of stereology relies on a simple assumption of uniform random sectioning, whereas the liberation models require all particles to have similar textural properties. However, for the sake of adequate prediction of ore behavior in industrial processes it is becoming more and more important to be able to describe particle populations in terms of texture classes.Among the texture analysis techniques, this paper focuses on mean intercept length (MIL). It brings two important improvements: the first one is to improve grain boundary imaging by using images of bireflectant minerals under plane polarized illumination, the second one is to consider intercept analysis on grey level images instead of binary ones. The method is illustrated on a selection of critical hematite textures found within banded iron formations.

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Section: Grey Level Intercepts For Texture Analysismentioning

confidence: 99%

Particle texture analysis using polarized light imaging and grey level intercepts

Pirard

Lebichot

Krier

2007

International Journal of Mineral Processing

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“…Surveys of existing texture analysis approaches may be found in Refs. [1][2][3][4][5][6][7][8]. Comparative studies of a subset of texture analysis methods can also be found in Refs.…”

Section: Introductionmentioning

confidence: 99%

“…[30 -33]. The importance of texture perception is presented in detail [1] from the viewpoint of human vision and practical machine vision applications.…”

Section: Introductionmentioning

confidence: 99%

“…Existing texture analysis methods are broadly divided into two categories: statistical methods and structural methods in most of the surveys [1][2][3][4][5][6][7][8]. But in the recent literature, many model based methods have been employed in texture analysis [10], including autoregressive model, Gausian Markov random ÿelds, Gibbs random ÿelds, Wold model, wavelet model, multichannel Gabor model and steerable pyramid, etc.…”

Section: Introductionmentioning

confidence: 99%

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Brief review of invariant texture analysis methods

Zhang¹,

Tan

2002

Pattern Recognition

384

171

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This paper considers invariant texture analysis. Texture analysis approaches whose performances are not a ected by translation, rotation, a ne, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classiÿed into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented ÿrst. Each approach is reviewed according to its classiÿcation, and its merits and drawbacks are outlined. The focus of possible future work is also suggested. ?

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“…A wide variety of techniques for describing image texture have been proposed [1]. The methods can be divided into four categories: statistical, geometrical, model-based and signal processing.…”

Section: Introductionmentioning

confidence: 99%

Texture Features in Facial Image Analysis

Hadid

2005

Advances in Biometric Person Authentication

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Abstract. While features used for texture analysis have been successfully used in some biometric applications, only quite few works have considered them in facial image analysis. Texture-based region descriptors can be very useful in recognizing faces and facial expressions, detecting faces and different facial components, and in other face related tasks. This paper demonstrates this issue by considering the local binary pattern (LBP) as an example of texture-based approach and showing its efficiency in facial image analysis.

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Texture Analysis

Cited by 273 publications

References 0 publications

Particle texture analysis using polarized light imaging and grey level intercepts

Particle texture analysis using polarized light imaging and grey level intercepts

Brief review of invariant texture analysis methods

Texture Features in Facial Image Analysis

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