Texture Classification through Multiscale Orientation Histogram Analysis

Alemán‐Flores, Miguel; Álvarez‐León, Luis

doi:10.1007/3-540-44935-3_33

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…(ii) Generation of an orientation map (planar image projection of a hemisphere) for a single mCT voxel. To this end, for a hemisphere pixel the standard deviation (SD) from all grey values along the maximum straight distance through the sphere was computed (Alemán-Flores and Álvarez-León, 2003). This was done for each hemisphere pixel and resulted in 1609 tracks for a given sphere radius of 16 voxel ðA hemisphere ¼ 2nπnð16Þ 2 Þ.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Reconstruction of muscle fascicle architecture from iodine-enhanced microCT images: A combined texture mapping and streamline approach

Kupczik

Stark

Mundry

et al. 2015

Journal of Theoretical Biology

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Section: Pattern Recognitionmentioning

confidence: 99%

Reconstruction of muscle fascicle architecture from iodine-enhanced microCT images: A combined texture mapping and streamline approach

Kupczik

Stark

Mundry

et al. 2015

Journal of Theoretical Biology

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“…In this paper, we present a method for video segmentation based on the distribution of the orientation of the edges. We use the results of the multiscale texture analysis described in [1] and study the behavior of natural textures in order to find the transitions between the different video scenes. To this end, we estimate the gradient in every point of the region and build an orientation histogram to describe it.…”

Section: Introductionmentioning

confidence: 99%

Video Segmentation Through Multiscale Texture Analysis

Alemán‐Flores

Álvarez‐León

2004

Lecture Notes in Computer Science

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Segmenting a video sequence into different coherent scenes requires analyzing those aspects which allow finding the changes where a transition is to be found. Textures are an important feature when we try to identify or classify elements in a scene and, therefore, can be very helpful to find those frames where there is a transition. Furthermore, analyzing the textures in a given environment at different scales provides more information than considering the features which can be extracted from a single one. A standard multiscale texture analysis would require an adjustment of the scales in the comparison of the textures. However, when analyzing video sequences, this process can be simplified by assuming that the frames have been acquired at the same resolution. In this paper, we present a multiscale approach for segmenting video scenes by comparing the textures which are present in their frames.

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Multi-resolution Texture Classification Based on Local Image Orientation

Ghita

Whelan

Ilea

Lecture Notes in Computer Science

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Abstract. The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases.

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Texture Classification through Multiscale Orientation Histogram Analysis

Cited by 4 publications

References 6 publications

Reconstruction of muscle fascicle architecture from iodine-enhanced microCT images: A combined texture mapping and streamline approach

Reconstruction of muscle fascicle architecture from iodine-enhanced microCT images: A combined texture mapping and streamline approach

Video Segmentation Through Multiscale Texture Analysis

Multi-resolution Texture Classification Based on Local Image Orientation

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