A Statistical Theory for Shape Analysis of Curves and Surfaces with Applications in Image Analysis, Biometrics, Bioinformatics and Medical Diagnostics

2010 ◽  
Author(s):  
Anuj Srivastava
Keyword(s):  
Image Analysis ◽  
Statistical Theory ◽  
Shape Analysis ◽  
Medical Diagnostics ◽  
Curves And Surfaces

Joint Registration and Shape Analysis of Curves and Surfaces

2013 ◽  
pp. 213-224
Author(s):  
Jingyong Su ◽  
Sebastian Kurtek ◽  
Anuj Srivastava
Keyword(s):  
Shape Analysis ◽  
Curves And Surfaces

scholarly journals Rapid Shape Analysis of Crushed Stone Using Image Analysis

1996 ◽  
Vol 14 (0) ◽  
pp. 130-137 ◽  
Author(s):  
David A. Broyles ◽  
Hugh W. Rimmer ◽  
Gregory T. Adel
Keyword(s):  
Image Analysis ◽  
Shape Analysis ◽  
Crushed Stone

Hyperspectral fluorescence image analysis for use in medical diagnostics

2005 ◽  
Author(s):  
Seong G. Kong ◽  
Zheng Du ◽  
Matthew Martin ◽  
Tuan Vo-Dinh
Keyword(s):  
Image Analysis ◽  
Medical Diagnostics ◽  
Fluorescence Image

Automated image analysis in histopathology: a valuable tool in medical diagnostics

2008 ◽  
Vol 8 (6) ◽  
pp. 707-725 ◽  
Author(s):  
Laoighse Mulrane ◽  
Elton Rexhepaj ◽  
Steve Penney ◽  
John J Callanan ◽  
William M Gallagher
Keyword(s):  
Image Analysis ◽  
Medical Diagnostics ◽  
Automated Image Analysis

scholarly journals Evaluation of a method based on image analysis to obtain shape parameters in crushed sand grains

2015 ◽  
Vol 8 (5) ◽  
pp. 577-590 ◽  
Author(s):  
A. G. Goldoni ◽  
L. M. Pandolfo ◽  
A. P. Gomes ◽  
D. Folle ◽  
M. S. Martins ◽  
...  
Keyword(s):  
Image Analysis ◽  
Shape Analysis ◽  
Shape Factor ◽  
Image Acquisition ◽  
Shape Parameters ◽  
The City

ABSTRACTThe objective of this paper is to evaluate a method based on image analysis to obtain shape parameters in crushed sand grains. There is no consensus about standards and rules for testing aggregates, the lack of methodology to prepare and conduct tests may produce incorrect results, which do not satisfactorily represent the aggregate characteristics. One way to perform these analyzes is the use of images obtained with magnifying glasses or similar equipment. To contribute to this, three experiments were prepared with samples of crushed sand from the city of Passo Fundo. The fixed and evaluated parameters were: samples preparation, zoom used for image acquisition and number of grains representative of the shape parameters. The results were statistically analyzed and significant differences were obtained to the shape factor regarding the fixed parameters, except for the number of grains needed to characterize it, which differs from the currently literature used by academic studies. According to this work it is possible to realize that it is necessary to standardize the tests for shape analysis to eliminate errors generated by the interpretation of incorrect results, which may have been generated by changes in the methodology for conducting the tests.

Size and Shape Analysis of Sedimentary Grains by Automated Dynamic Image Analysis

2006 ◽  
Vol 23 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Daisy Tysmans ◽  
Philippe Claeys ◽  
Luc Deriemaeker ◽  
Dominique Maes ◽  
Robert Finsy ◽  
...  
Keyword(s):  
Image Analysis ◽  
Shape Analysis ◽  
Dynamic Image ◽  
Size And Shape ◽  
Dynamic Image Analysis

scholarly journals Studies on the imbibition of triticale kernels with a different degree of sprouting, using digital shape analysis

2011 ◽  
Vol 52 (No, 7) ◽  
pp. 328-334 ◽  
Author(s):  
M. Wiwart ◽  
M. Moś ◽  
T. Wójtowicz
Keyword(s):  
Image Analysis ◽  
Shape Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Kernel Weight ◽  
Shape Descriptors ◽  
Germination Capacity ◽  
Shape Factors ◽  
Image Area ◽  
Weight Method

The imbibition of triticale kernels with a different degree of sprouting and with no visible sprouting symptoms was studied using digital image analysis and the classical weight method. Measurements were performed at two-hour intervals for the first 14 hours of imbibition, and at 24 and 26 hours. The area, perimeter, length and width of kernel images, and ten different shape factors were determined along with the weight of imbibing kernels. The germination capacity was determined on the seeds used for measurements. Kernels showing the lowest degree of sprouting were characterized by the highest rate of water uptake in comparison with the control. Most of shape descriptors were significantly correlated with kernel weight, especially image area (r = 0.688) and two shape factors (calculated on the basis of image perimeter and area) of S<sub>9</sub> and S<sub>10</sub> (r = 0.742 and 0.958, respectively), which makes them the best descriptors of image of imbibing seeds.

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