Flotation froth image texture extraction method based on deterministic tourist walks

As known, there always exist severely degradation problems in digital radiography. How we can extract necessary textures from degraded radiographic images is the post-processing key. Local binary pattern (LBP) is a well-known method, which is widely used in fast image texture extraction. However, for noisy images, LBP can’t work well due to its sensitivity to details. On the other hand, as one of the important shock filters developed in recent years, complex shock filter possesses excellent capabilities in textural image processing. Here, by combining complex shock filter with LBP, a novel fast and efficient method, C-LBP is presented for texture extraction of degraded radiographic images. Experimental results show that comparing with traditional LBP, C-LBP not only distinguishes between noise and details in radiographic images, but also extracts image textures efficiently and rapidly, which plays an important role in developing nondestructive detection technique by low-dose ray radiography.

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Color co-occurrence matrix based froth image texture extraction for mineral flotation

Minerals Engineering ◽

10.1016/j.mineng.2013.03.024 ◽

2013 ◽

Vol 46-47 ◽

pp. 60-67 ◽

Cited By ~ 32

Author(s):

Weihua Gui ◽

Jinping Liu ◽

Chunhua Yang ◽

Ning Chen ◽

Xi Liao

Keyword(s):

Image Texture ◽

Mineral Flotation ◽

Froth Image ◽

Occurrence Matrix ◽

Texture Extraction

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Adaptive CSLBP compressed image hashing

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2982-2992 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2982

Author(s):

Varsha Patil ◽

Tanuja Sarode

Keyword(s):

Hamming Distance ◽

Color Space ◽

Image Authentication ◽

Image Texture ◽

Weight Factor ◽

Image Hashing ◽

Discrimination Capability ◽

Content Change ◽

Smooth Region ◽

Texture Extraction

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.

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Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

International Journal of Virtual Reality ◽

10.20870/ijvr.2011.10.3.2823 ◽

2011 ◽

Vol 10 (3) ◽

pp. 73-79 ◽

Cited By ~ 1

Author(s):

Jian Yang ◽

Jingfeng Guo

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Texture Feature ◽

Local Area ◽

Image Texture ◽

Gray Level ◽

Texture Feature Extraction ◽

Feature Extraction Method ◽

Level Difference ◽

Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

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Research on the Wood Cell Contour Extraction Method Based on Image Texture and Gray-scale Information

International Journal of Signal Processing Image Processing and Pattern Recognition ◽

10.14257/ijsip.2014.7.6.06 ◽

2014 ◽

Vol 7 (6) ◽

pp. 65-74

Author(s):

Lei Zhao ◽

Jianhua Wang ◽

Xiaofeng Li

Keyword(s):

Extraction Method ◽

Image Texture ◽

Contour Extraction ◽

Gray Scale ◽

Cell Contour

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Infrared polarization image texture extraction via variational decomposition algorithm

10.1117/12.2208242 ◽

2015 ◽

Author(s):

Weiguo Zhang ◽

Jingjing Zhang ◽

Lina Xun ◽

Feng Wang ◽

Hongwu Yuan ◽

...

Keyword(s):

Decomposition Algorithm ◽

Image Texture ◽

Polarization Image ◽

Texture Extraction

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Volume texture extraction for 3D seismic visualization and interpretation

Geophysics ◽

10.1190/1.1598122 ◽

2003 ◽

Vol 68 (4) ◽

pp. 1294-1302 ◽

Cited By ~ 101

Author(s):

Dengliang Gao

Keyword(s):

Extraction Method ◽

Large Scale ◽

Quantitative Information ◽

Hydrocarbon Exploration ◽

3D Seismic ◽

Image Domain ◽

Seismic Image ◽

Occurrence Matrix ◽

Seismic Visualization ◽

Texture Extraction

Visual inspection of poststack seismic image patterns is effective in recognizing large‐scale seismic features; however, it is not effective in extracting quantitative information to visualize, detect, and map seismic features in an automatic and objective manner. Although conventional seismic attributes have significantly enhanced interpreters' ability to quantify seismic visualization and interpretation, very few attributes are published to characterize both intratrace and intertrace relationships of amplitudes from a three‐dimensional (3D) perspective. These relationships are fundamental to the characterization and identification of certain geological features. Here, I present a volume texture extraction method to overcome these limitations. In a two‐dimensional (2D) image domain where data samples are visualized by pixels (picture elements), a texture has been typically characterized based on a planar texel (textural element) using a gray level co‐occurrence matrix. I extend the concepts to a 3D seismic domain, where reflection amplitudes are visualized by voxels (volume picture elements). By evaluating a voxel co‐occurrence matrix (VCM) based on a cubic texel at each of the voxel locations, the algorithm extracts a plurality of volume textural attributes that are difficult to obtain using conventional seismic attribute extraction algorithms. Case studies indicate that the VCM texture extraction method helps visualize and detect major structural and stratigraphic features that are fundamental to robust seismic interpretation and successful hydrocarbon exploration.

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