A novel image-processing based method for the automatic detection, extraction and characterization of marine mammal tonal calls

2009 ◽  
Vol 90 (8) ◽  
pp. 1667-1684 ◽  
Author(s):  
Antonio Sánchez-García ◽  
Patricio Muñoz-Esparza ◽  
José Luis Sancho-Gomez
Keyword(s):  
Image Processing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Marine Mammal ◽  
Automatic Detection ◽  
Fixed Number ◽  
Automatic Method ◽  
Contour Extraction ◽  
Time Frequency

A novel, automatic method for the detection, extraction and characterization of marine mammal tonal calls is presented. Signals are automatically detected from the spectrogram, isolated using region-based segmentation, extracted and finally characterized by means of a fixed number of radial basis function (RBF) coefficients. A total of sixteen RBF coefficients are sufficient to accurately capture the time–frequency information contained in the calls. These coefficients can be later used to classify signals based on their characteristics. New specific functions for contour extraction and cross-resolution have been developed. The performance of the method has been extensively tested using simulated signals and a set of recordings covering a significant range of situations that can be encountered at sea.

RECOGNITION OF FACIAL EXPRESSION BY IMAGE PROCESSING AND RADIAL BASIS FUNCTION NETWORK

2016 ◽  
Author(s):  
Olímpio Murilo Capeli ◽  
Euvaldo Ferreira Cabral Junior ◽  
Sadao Isotani ◽  
Antonio Roberto Pereira Leite de Albuquerque
Keyword(s):  
Image Processing ◽  
Facial Expression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Radial Basis ◽  
Function Network

Product Identification Using Image Processing and Radial Basis Function Neural Networks

2015 ◽  
Vol 761 ◽  
pp. 120-124
Author(s):  
K.A.A. Aziz ◽  
Abdul Kadir ◽  
Rostam Affendi Hamzah ◽  
Amat Amir Basari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Architecture ◽  
Rbf Neural Networks ◽  
Specific Product ◽  
Product Identification ◽  
Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

EIT Image Processing Based on 2-D Empirical Mode Decomposition

2012 ◽  
Vol 268-270 ◽  
pp. 1906-1909
Author(s):  
Qing He ◽  
Xiao Yan Chen
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Empirical Mode Decomposition ◽  
Basis Function ◽  
Small Volume ◽  
Impedance Tomography ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Adaptive Time

Electrical impedance tomography (EIT) imaging is a new kind of imaging technology that has developed in recent years. This technology has many advantages such as noninvasive, small volume and so on, but there are also many shortcomings such as imaging quality is not high, in order to improve the image quality, image processing methods is used in this paper. Significant different with the traditional method, empirical mode decomposition method does not require any prior basis function. It is an adaptive time-frequency analysis method. The two-dimensional empirical mode decomposition algorithm is adopted to process EIT images in this paper, and the compactly supported radial basis function interpolation algorithm is used for the post-processing of three organic glass rod EIT imaging. The image is decomposed into six IMFs in the experiment, some IMFs contain useful information, and the rest of them contains a lot of noise. The experimental results show that this method can significantly improve the EIT image quality.

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