Corrigendum to ‘Discrimination of bark from wood chips through texture analysis by image processing’ [Comput. Electron. Agric. 79 (2011) 13–19]

2013 ◽  
Vol 99 ◽  
pp. 123
Author(s):  
James R. Wooten ◽  
S.D. Filip To ◽  
C. Igathinathane ◽  
L.O. Pordesimo
Keyword(s):  
Image Processing ◽  
Texture Analysis ◽  
Wood Chips ◽  
Comput Electron

Discrimination of bark from wood chips through texture analysis by image processing

2011 ◽  
Vol 79 (1) ◽  
pp. 13-19 ◽  
Author(s):  
James R. Wooten ◽  
S.D. Filip To ◽  
C. Igathinathane ◽  
L.O. Pordesimo
Keyword(s):  
Image Processing ◽  
Texture Analysis ◽  
Wood Chips

Classification of Wood Chips Using Image Processing Texture Analysis

2008 ◽  
Author(s):  
James R Wooten ◽  
Lester O Pordesimo ◽  
Cannayen Igathinathane ◽  
Eugene P Columbus
Keyword(s):  
Image Processing ◽  
Texture Analysis ◽  
Wood Chips

Brain Tumour Texture Analysis-A Method

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Samir Kumar Bandyopadhyay
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Texture Analysis ◽  
Brain Tumour ◽  
Tumor Segmentation ◽  
Biomedical Image Processing ◽  
Biomedical Analysis ◽  
Brain Tumor Segmentation ◽  
Human Brain Tumor ◽  
Biomedical Image

Computer aided technology is used in biomedical image processing. In biomedical analysis features are extracted and then the proposed method will detect any abnormalities present or not in the system to be considered. In recent days the detection of brain tumour through image processing is made in medical diagnosis. The separation of tumor is made by the process of segmentation. Brain in human is the most complicated and delicate anatomical structure. There are various brain ailments in human but the indication of cancer in brain tumour may be fatal for the human. Brain tumor can be malignant or benign. The neurologist or neurosurgeon wants to know the exact location, size, shape and texture of tumor from Magnetic Resonance Imaging (MRI) of brain before going to the operation of the brain tumour or decided whether operation of removing brain tumour is at all necessary or not. The disease is analyzed since operation may cause death to the patient. Initially they took a chance by prescribing medicines to see whether there is any improvement of the condition of the patient. If the result is not satisfactory then there is no option other than operation of the tumor. Doctors also take an attempt to find the texture of the tumor since it may help them to know the progress of the tumour. In addition to Brain tumor segmentation, the detection of surface of the texture of brain tumor is required for proper treatment. The chapter proposed methods for detection of the progressive nature of the texture in the tumor presence in brain. For this process segmentation of tumor from other parts of brain is essential. In the chapter segmentation techniques are presented before the texture analysis process is given. Finally, comparisons of the proposed method with other methods are analyzed.

scholarly journals New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Safia Abdelmounaime ◽  
He Dong-Chen
Keyword(s):  
Image Processing ◽  
Texture Analysis ◽  
Image Databases ◽  
Spectral Information ◽  
Spectral Content ◽  
New Class ◽  
Texture Information ◽  
Basic Image ◽  
Informative Content ◽  
Texture Characterization

Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

scholarly journals APPLYING IMAGE PROCESSING TOOLS TO ANALYZE THE SURFACE CHARACTERISTICS OF NANO ALUMINA (NANOAL2O3) AND NANO TITANIUM (NANOTIO2)

2018 ◽  
Vol 80 (4) ◽  
Author(s):  
Abdul-Adheem Zaily Hameed ◽  
Muzhir Shaban Al-Ani ◽  
Faik Hammad Anter
Keyword(s):  
Image Processing ◽  
Composite Material ◽  
Texture Analysis ◽  
Surface Texture ◽  
Quantitative Description ◽  
Surface Characteristics ◽  
Material Surface ◽  
Mean Values ◽  
Gray Image ◽  
Nano Alumina

Composite material is a material constructed of two or more materials that leads with different physical or chemical characteristics. Nano Alumina (NANO AL2O3) and Nano Titanium (NANO TIO2) are normally used to construct the composite material. The fundamental of texture analysis seeks to derive a general efficient and compact quantitative description of textures so that various mathematical operations can be used to achieve, compare and transform of texture characteristics. Many mechanical and physical methods are used to measure the surface characteristics. Some of these methods suffered from accurate description of material surface. In addition, the details of material surface are not clear via applying the traditional methods for surface analyzing. This work is concentrated on combining many functions and steps of image processing method to understand and analyze the surface characteristics of the composite material (Nano Alumina and Nano Titanium). The implemented approach including many steps: image enhancement, texture analysis, edge detection and contour analysis. This approach leads to explain, extract, analyze and evaluate the characteristics of surface texture of the composite material via measuring of mean values for original gray image, adjusted gray image, equalized gray image and adapted gray image. The average mean values of Nano Alumina are 103, 110, 128 and 134 for the applied method respectively. The average mean values of Nano titanium are 120, 123, 125 and 129 respectively. As a conclusion the implemented approach of surface texture analysis indicated that there is a significant improvement at the surface characteristics for both equalization and adaptive methods compared with the adjustment method.

Estimation of qth-Order Fractal Dimensions

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 257-269 ◽  
Author(s):  
Stefano Fioravanti ◽  
Daniele D. Giusto
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Texture Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
State Of The Art ◽  
Fractal Dimensions ◽  
Sar Image ◽  
Spectrum Function ◽  
Novel Algorithm

The paper deals with the theory of qth-order fractal dimensions and its application to texture analysis. In particular, the state-of-the-art regarding the fractal dimension estimation for characterizing textures is presented. After, the insufficiency of the single fractal dimension is proven and the qth order fractal dimensions are introduced to overcome such drawback. The multifractality spectrum function D(q) is described, a novel algorithm for estimating such dimensions is then proposed, and its use in digital-image processing is addressed. Results on real SAR image textures are reported and discussed.

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