scholarly journals A Proposed Mechanism for Texture Property of Woody Breast in Broilers

2019 ◽  
Vol 3 (2) ◽  
pp. 179-179
Author(s):  
A. Welter ◽  
W. J. Wu ◽  
T. O’Quinn ◽  
T. Houser ◽  
E. Boyle ◽  
...  
Keyword(s):  
Texture Property

scholarly journals Texture-Property Relationships in the High Temperature Superconductors

1991 ◽  
Vol 14 ◽  
pp. 355-362 ◽  
Author(s):  
A. D. Rollett ◽  
H.-R. Wenk ◽  
F. Heidelbach ◽  
T. G. Schofield ◽  
R. E. Muenschausen ◽  
...  
Keyword(s):  
High Temperature ◽  
High Temperature Superconductors ◽  
Texture Property

Prediction of Handsheet Tensile Strength by Computational Simulation of Structure

TAPPI Journal ◽  
2010 ◽  
Vol 9 (1) ◽  
pp. 15-19
Author(s):  
RÉMI VINCENT ◽  
MARTINE RUEFF ◽  
CHRISTIAN VOILLOT
Keyword(s):  
Tensile Strength ◽  
Breaking Strength ◽  
Computational Simulation ◽  
Physical Property ◽  
Size Distributions ◽  
Fiber Morphology ◽  
Paper Properties ◽  
Texture Property ◽  
Paper Structure ◽  
Fiber Breaks

To better understand the influence of fiber morphology on paper properties, we developed a novel 3-D computational simulator of paper structure, which was validated through experimental work. This simulator creates virtual pieces of handsheets using the size distributions of the fibers as the main inputs. Once the structure is generated, physical properties can be assessed. The main principles of the simulation and the results for one global texture property, the apparent density, were presented in a previous paper. In this paper, we focus on the prediction of the tensile breaking strength, the most commonly used physical property for paper characterization. The model is based on the model developed by Shallhorn and Karnis, which was adapted to take into account the fiber morphological distributions. It was successfully applied in the absence of fiber breaks during the test and validated with the 10 pulps used in the first part of the study.

MULTI-RESOLUTION TEXTURE ANALYSIS OF MAMMOGRAMS USING NEAREST NEIGHBOR CLASSIFICATION TECHNIQUES

2010 ◽  
Vol 07 (02) ◽  
pp. 109-118 ◽  
Author(s):  
B. N. PRATHIBHA ◽  
V. SADASIVAM
Keyword(s):  
Roc Curve ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Performance Measure ◽  
Support Vector ◽  
Roc Curve Analysis ◽  
Classification Techniques ◽  
Cost Effective Method ◽  
Texture Property

Mammograms are the most reliable and cost effective method for showing tissues abnormalities of breast. The proposed method classifies the breast tissues by extracting multi texture properties with multi scale wavelet transformations on regions of interest (ROI). The ability of each texture property in discriminating normal and abnormal ROI is analyzed individually and collectively. The method is tested on 217 mammogram images from the mini-MIAS database. Results indicate that multi-resolution image parametrization becomes inevitable when improvement of classification accuracy in textural domains is required. Further, this paper finds the classification accuracy of the nearest neighbor (NN) classification techniques which exploits the underlying density structure of dataset. The study reveals that the variants of the nearest neighbor classifier perform well individually and they significantly enhance the classification accuracy when combined. Finally, the performance of the proposed statistical classifier is compared with radial basis classifier Support Vector Machines (SVM). The receiver operating characteristic (ROC) curve analysis is used as the performance measure to justify the result. It yields an area under the ROC curve (AZ) of 0.946 for proposed scheme, against 0.924 of SVM.

Nanoscale Composition-Texture-Property Relation in Calcium-Silicate-Hydrates

2020 ◽  
pp. 1761-1792
Author(s):  
Mohammad Javad Abdolhosseini Qomi ◽  
Mathieu Bauchy ◽  
Roland J.-M. Pellenq
Keyword(s):  
Calcium Silicate ◽  
Calcium Silicate Hydrates ◽  
Texture Property ◽  
Property Relation

Preferred Orientation Analysis in Textured Materials

1991 ◽  
Vol 35 (A) ◽  
pp. 263-275
Author(s):  
H.J. Bunge
Keyword(s):  
Solid State ◽  
Powder Diffraction ◽  
Preferred Orientation ◽  
Polycrystalline Materials ◽  
Texture Formation ◽  
Orientation Analysis ◽  
Texture Property ◽  
Powder Samples ◽  
Diffraction Peaks ◽  
Textured Materials

AbstractPolycrystalline materials may either be compact such as metals, ceramics, or some polymers or they may be powder samples. In both cases preferred orientation of the crystallites must be taken into account. In compact materials, texture formation by solid state processes and the texture-property relationship are the main purposes for texture investigations. In powder diffraction, texture correction can increase the accuracy of the results. Also strongly textured samples may be used in order to seperate diffraction peaks which are coincident in random powders.

Effect of Carbon Pretreatment on Cerium Loaded Activated Carbon as Desulfurization Adsorbents

2012 ◽  
Vol 557-559 ◽  
pp. 1584-1587
Author(s):  
Lin Xiong ◽  
Xue Min Yan ◽  
Yuan Zhu Mi
Keyword(s):  
Activated Carbon ◽  
Surface Chemistry ◽  
Adsorption Capacity ◽  
Adsorption Isotherms ◽  
Metal Species ◽  
Model Fuel ◽  
Texture Property ◽  
The One

Activated carbon (AC) was pretreated by H2O2 or HNO3 and then loaded with cerium to obtain Ce/AC composites as desulfurization adsorbents. The adsorption isotherms of as-prepared Ce/AC composites for dibenzothiophene were measured in static batch desulfurization of model fuel and compared with the one prepared without AC pretreatment. It was found that both H2O2 and HNO3 pretreatment could enhance the adsorption capacity of Ce/AC composites. The results were discussed in terms of surface chemistry and texture property. The improvement could be related to increased surface acidic groups and better dispersion of loaded metal species bought about by the two pretreatment methods

Clustering-Based Color Image Segmentation Using Local Maxima

2018 ◽  
Vol 14 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Kalaivani Anbarasan ◽  
S. Chitrakala
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Automatic Segmentation ◽  
Input Image ◽  
Color Image Segmentation ◽  
Local Maxima ◽  
User Input ◽  
Texture Property ◽  
Multiple Regions

Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, with pixels spatially clustered into identical regions. A novel Clustering-based Image Segmentation using Local Maxima (CBIS-LM) method is presented. Our proposed approach generates reliable, accurate and non-overlapping multiple regions for the given user input image. The segmented regions can be automatically annotated with distinct labels which, in turn, help retrieve relevant images based on image semantics.

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