Evaluations of fractal geometry and invariant moments for shape classification of corn germplasm

1998 ◽  
Vol 20 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Suranjan Panigrahi ◽  
Manjit K Misra ◽  
Stephen Willson
Keyword(s):  
Fractal Geometry ◽  
Shape Classification ◽  
Invariant Moments

scholarly journals Database Classification of MPEG7 Shape Using Rotation Moments and Generated Stationary Transformed Features

2019 ◽  
pp. 1-5
Author(s):  
Arash Kalami ◽  
Tohid Sedghi
Keyword(s):  
Graph Matching ◽  
Shape Classification ◽  
Rotation Invariant ◽  
Graph Node ◽  
Invariant Moments ◽  
Final Score ◽  
As Graph ◽  
The Difference

After the main trellis processing, the final score will contain all matching information. The graph matching shape classification is also used, where the Extracted Stationary Transformed Features is used to describe the Gabor graph for signal Rotation Invariant Moments object. The difference between our work and theirs is that we use feature point’s as graph node values and they use Shape Database Classification Gabor jet. Another difference is that and they didn’t use to do the graph matching.

scholarly journals Quantitative Shape-Classification of Misfitting Precipitates during Cubic to Tetragonal Transformations: Phase-Field Simulations and Experiments

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1373
Author(s):  
Yueh-Yu Lin ◽  
Felix Schleifer ◽  
Markus Holzinger ◽  
Na Ta ◽  
Birgit Skrotzki ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Phase Field ◽  
Phase Field Model ◽  
Invariant Moments ◽  
Plane Normal ◽  
Plane Shape ◽  
Formation And Evolution ◽  
The Given ◽  
Good Agreement

The effectiveness of the mechanism of precipitation strengthening in metallic alloys depends on the shapes of the precipitates. Two different material systems are considered: tetragonal γ′′ precipitates in Ni-based alloys and tetragonal θ′ precipitates in Al-Cu-alloys. The shape formation and evolution of the tetragonally misfitting precipitates was investigated by means of experiments and phase-field simulations. We employed the method of invariant moments for the consistent shape quantification of precipitates obtained from the simulation as well as those obtained from the experiment. Two well-defined shape-quantities are proposed: (i) a generalized measure for the particles aspect ratio and (ii) the normalized λ2, as a measure for shape deviations from an ideal ellipse of the given aspect ratio. Considering the size dependence of the aspect ratio of γ′′ precipitates, we find good agreement between the simulation results and the experiment. Further, the precipitates’ in-plane shape is defined as the central 2D cut through the 3D particle in a plane normal to the tetragonal c-axes of the precipitate. The experimentally observed in-plane shapes of γ′′-precipitates can be quantitatively reproduced by the phase-field model.

scholarly journals The shape classification of Torus-like and (n-sphere)-like continua

1974 ◽  
Vol 4 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Carl Eberhart ◽  
G.R. Gordh ◽  
John Mack
Keyword(s):  
Shape Classification

scholarly journals Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

PLoS ONE ◽  
2008 ◽  
Vol 3 (4) ◽  
pp. e1997 ◽  
Author(s):  
Alfredo Rodriguez ◽  
Douglas B. Ehlenberger ◽  
Dara L. Dickstein ◽  
Patrick R. Hof ◽  
Susan L. Wearne
Keyword(s):  
Fluorescence Microscopy ◽  
Dendritic Spines ◽  
Three Dimensional ◽  
Shape Classification ◽  
Microscopy Images

Feature Extraction and Classification of Images Based on Corner Invariant Moments

2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Image Feature ◽  
Support Vector ◽  
System Management ◽  
Image Feature Extraction ◽  
Invariant Moments ◽  
Harris Corner ◽  
Classification Of Images

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

Sign in / Sign up

Export Citation Format

Share Document