Evolutionary computation based optimization of image Zernike moments shape feature vector

2008 ◽  
Vol 13 (2) ◽  
pp. 153-158 ◽  
Author(s):  
Maofu Liu ◽  
Hujun Hu ◽  
Ming Zhong ◽  
Yanxiang He ◽  
Fazhi He
Keyword(s):  
Evolutionary Computation ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature

Optimization of Image Zernike Moments Shape Feature Based on Evolutionary Computation

2013 ◽  
pp. 199-216
Author(s):  
Maofu Liu ◽  
Huijun Hu
Keyword(s):  
Image Analysis ◽  
Evolutionary Computation ◽  
High Dimension ◽  
Optimization Algorithm ◽  
Feature Vector ◽  
Zernike Moments ◽  
Shape Feature ◽  
Original Image ◽  
Feature Based ◽  
Image Shape

The image shape feature can be described by the image Zernike moments. In this chapter, the authors point out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. Therefore, the optimization algorithm based on evolutionary computation is designed and implemented in this chapter to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

Feature Selection Based on GA and PNN

2011 ◽  
Vol 217-218 ◽  
pp. 1753-1757 ◽  
Author(s):  
Min Hao ◽  
Shuo Shi Ma ◽  
Xiao Dong Hao ◽  
Li Li Ma ◽  
Li Juan Wang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Fitness Function ◽  
Feature Selection Method ◽  
Selection Method ◽  
Zernike Moments ◽  
Image Feature ◽  
Detection Accuracy ◽  
Shape Feature ◽  
The Impact

A new image feature selection method with the combination of Genetic Algorithm(GA) and Probabilistic Neural Network(PNN) is proposed and applied to potato shape feature selection and classification. The classifier selecting principle is investigated by combining with the genetic algorithm. A new feature selection method based on GA and PNN is put forward firstly. Comprehensively considering the factor of classification accuracy,selected feature number and the impact of the two factors, a new fitness function is proposed. The initial Zernike moments parameters of potatoes are optimized using improved genetic algorithm, and nineteen Zernike moments are extracted to form the shape feature. The shape detection accuracy can reach 93% and 100% respectively for the perfect and malformation potatoes. The tests indicate that the fitness function and feature selection method can be used for searching the best feature combination.

scholarly journals P300 Detection Based on EEG Shape Features

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Montserrat Alvarado-González ◽  
Edgar Garduño ◽  
Ernesto Bribiesca ◽  
Oscar Yáñez-Suárez ◽  
Verónica Medina-Bañuelos
Keyword(s):  
Feature Vector ◽  
Brain Computer Interface ◽  
High Sensitivity ◽  
Computer Interface ◽  
Shape Feature ◽  
Shape Features ◽  
Novel Approach ◽  
Calibration Algorithm ◽  
The One

We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject’s P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA’s performance using our shape-feature vector was93%, that is,10%higher than the one obtained with BCI2000-feature’s vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of0.88. Also, most of the subjects needed less than15trials to have an AUROC superior to0.8. Finally, we found that the electrode C4 also leads to better classification.

scholarly journals Research on Shape Feature Recognition of B-Rep Model Based on Wavelet Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Jihua Wang
Keyword(s):  
Wavelet Transform ◽  
Feature Recognition ◽  
Feature Vector ◽  
3D Structure ◽  
Data File ◽  
Boundary Representation ◽  
Surface Boundary ◽  
Shape Feature ◽  
Product Model ◽  
Model Based

B-Rep (Boundary Representation) CAD model is widely used in the representation of manufactured product in computer, and it is a kind of real 3D structure with invisible part relative to 2.5D mesh model, so the shape feature recognition of B-Rep model is worth of much studying. We present one approach of shape feature recognition of B-Rep model based on the wavelet transform of surface boundary and region; it is inspired by the neuropsychology view that surface is the key visual features and by the systematology method that an object is recognized by decomposing and grouping its similar parts. Surface elements of B-Rep model are extracted from the neutral STEP (Standard for Exchange of Product Model Data) file; the curvatures of surface boundary and region were decomposed by wavelet transform, and then the coefficient statistics of same scale were as the surface feature vector. Similar surfaces of B-Rep model were clustered as a bin with the sum of perimeters and the mean vector, and all bins constituting a histogram are finally as the feature vector of B-Rep model. Thus B-Rep models are compared and retrieved using the EMD (Earth Mover’s Distance) of histogram. Our approach was evaluated by retrieval experiment with NDR (National Design Reservoir), and the result indicated its highly competent performance.

Shape feature descriptor using modified Zernike moments

2009 ◽  
Vol 14 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Z. M. Ma ◽  
Gang Zhang ◽  
Li Yan
Keyword(s):  
Zernike Moments ◽  
Feature Descriptor ◽  
Shape Feature

scholarly journals Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

2021 ◽  
Author(s):  
Norhene Gargouri ◽  
Raouia Mokni ◽  
Alima Damak ◽  
Dorra Sellami ◽  
Riadh Abid
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Zernike Moments ◽  
Rotation Invariance ◽  
Invariance Property ◽  
Shape Feature ◽  
Shape Features ◽  
Shape Feature Extraction

Abstract Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the lesions based on mammographic images is playing an essential role to assist experts. A novel Computer-Aided Diagnosis (CADx) scheme of breast lesion classification is proposed in this paper based on an optimized combination of texture and shape features using machine and deep learning algorithms for mass classification as benign-malignant namely C(M-ZMs)*. The main advantage of using Zernike moments for shape feature extraction is their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our case. We implemented for texture feature extraction the Monogenic-Local Binary Pattern taking the advantage of lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. Therefore, we used Zernike moments for shape feature extraction due to their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our proposed system. The proposed system proves its performance on some challenging breast cancer cases where the lesions exist in dense breast tissues. Validation has been undertaken on 520 mammograms from the Digital Database for Screening Mammography Database (DDSM), yielding an accuracy rate of 99.5\%.

Robust Shape-Feature-Vector-Based Face Recognition System

2011 ◽  
Vol 60 (12) ◽  
pp. 3781-3791 ◽  
Author(s):  
Zhengya Xu ◽  
Hong Ren Wu ◽  
Xinghuo Yu ◽  
Kathryn Horadam ◽  
Bin Qiu
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
Recognition System ◽  
Shape Feature ◽  
Face Recognition System

scholarly journals Microfilariae Classification Using Multiple Classifiers for Color and Shape Features

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Faroq AL-Tam ◽  
António dos Anjos ◽  
Sébastien Pion ◽  
Michel Boussinesq ◽  
Hamid Reza Shahbazkia
Keyword(s):  
Feature Vector ◽  
Shape Descriptor ◽  
Experimental Results ◽  
Shape Feature ◽  
Shape Features ◽  
Posterior Probabilities ◽  
Shape Information ◽  
Color Feature ◽  
Feature Vectors ◽  
Multiple Classifiers

Abstract This paper presents a multi-classifier approach for classifying microfilariae in 2-D images. A shape descriptor based on the quench function is described. This descriptor is represented as a feature vector that encodes the shape information. The color feature vector is calculated as a histogram. Two classifiers were used to train both color and shape feature vectors, one for each vector. The posterior probabilities calculated from the scores of each classifier are then used to calculate the final classification decision. The experimental results show that, although the proposed approach is simple, it is efficient when compared to various approaches.

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