Geometrical Features Extraction and KNN Based Classification of Handwritten Marathi Characters

2017 ◽  
Author(s):  
Parshuram M. Kamble ◽  
Ravindra S. Hegadi
Keyword(s):  
Features Extraction ◽  
Geometrical Features

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 25 (06) ◽  
pp. 1350058 ◽  
Author(s):  
Pablo F. Diez ◽  
Vicente A. Mut ◽  
Eric Laciar ◽  
Abel Torres ◽  
Enrique M. Avila Perona
Keyword(s):  
Empirical Mode Decomposition ◽  
Communication System ◽  
Brain Activity ◽  
Features Extraction ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Machine Communication ◽  
Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

Color Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium berghei

2019 ◽  
pp. 715-722
Author(s):  
Juan V. Lorenzo-Ginori ◽  
Lyanett Chinea-Valdés ◽  
Yanela IzquierdoTorres ◽  
Rubén Orozco-Morales ◽  
Niurka Mollineda-Diogo ◽  
...  
Keyword(s):  
Plasmodium Berghei ◽  
Digital Images ◽  
Features Extraction ◽  
Color Features

The Corn Seed Image Segmentation and Measurement of the Geometrical Features Based on Image Analysis

2011 ◽  
Vol 66-68 ◽  
pp. 1100-1105
Author(s):  
Min Zhao ◽  
Wen Fu Wu ◽  
Ya Qiu Zhang
Keyword(s):  
Image Analysis ◽  
Conveyor Belt ◽  
Geometric Features ◽  
Quality Inspection ◽  
Corn Kernel ◽  
Corn Seed ◽  
Geometrical Features ◽  
Inspection Process ◽  
Human Inspection

In this article, separation of touching grain kernels and measurement of geometric features in an image are presented. The objective of this work is to discriminate single corn kernel and some broken kernels, which are difficult to achieve on the existing machinery and equipment, especially for the counted number and quality inspection process. The digital image was obtained from flatted conveyor belt; the geometrical features were analyzed using Matlab R2009a software. It was used for recognizing multiple kernels for once. Intact and broken kernels were characterized by the changed geometrical features and combining SVM for the classification of them with accuracy 95%.Which meet the requirement of corn quality inspection comparable to subjective human inspection.

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