Recognition of Rotating Images Using an Automatic Feature Extraction Technique and Neural Networks

This paper presents a new automatic feature extraction technique and a neural network based classification method for recognition of rotating images. The image processing technique extracts global features of an image and converts a large size image into a one-dimensional small vector. A special advantage of the proposed technique is that the extracted features are the same even if the original image is rotated with rotation angles from 5 to 355 or rotated and a little bit distorted. The proposed approach technique is based on simple co-ordinate geometry, fuzzy sets and neural networks. The proposed approach is very easy in implementation and its has been developed in C++ on a Sun workstation. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated and distorted images.

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Recognition of rotating images using an automatic feature extraction technique and neural networks

1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96) ◽

10.1109/cnna.1996.566513 ◽

2002 ◽

Author(s):

B. Verma

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Extraction Technique ◽

Automatic Feature Extraction ◽

Feature Extraction Technique

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Performance Analysis of ApEn as a Feature Extraction Technique and Time Delay Neural Networks, Multi Layer Perceptron as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals

Advances in Intelligent Systems and Computing - Computational Intelligence, Cyber Security and Computational Models ◽

10.1007/978-981-10-0251-9_10 ◽

2015 ◽

pp. 89-97 ◽

Cited By ~ 3

Author(s):

Sunil Kumar Prabhakar ◽

Harikumar Rajaguru

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Time Delay ◽

Performance Analysis ◽

Extraction Technique ◽

Multi Layer Perceptron ◽

Eeg Signals ◽

Risk Levels ◽

Feature Extraction Technique

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Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1243/0959651981539325 ◽

1998 ◽

Vol 212 (2) ◽

pp. 87-97 ◽

Cited By ~ 13

Author(s):

T T Le ◽

J Watton ◽

D T Pham

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Power Systems ◽

Linear Prediction ◽

Large Data ◽

Fault Classification ◽

Extraction Technique ◽

Dynamic Feature ◽

Fluid Power Systems ◽

Feature Extraction Technique

Multilayer perceptron (MLP) type neural networks and dynamic feature extraction techniques, namely linear prediction coding (LPC) and LPC cepstrum, are used to classify leakage type and to predict leakage flowrate magnitude in an electrohydraulic cylinder drive. Both single-leakage and multiple-leakage type faults are considered. A novel feature is that only pressure transient responses are employed as information. In addition, the feature extraction technique used to detect faults can result in a large data dimensionality reduction. The performance of two MLP models, namely serial and parallel, are studied to reflect the importance of the way data are presented to the MLP.

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CORRELATION BASED FACE MATCHING IN COMBINED GLOBAL AND LOCAL PRESERVING FEATURE SPACE

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141000838x ◽

2010 ◽

Vol 24 (08) ◽

pp. 1281-1294

Author(s):

K. RUBA SOUNDAR ◽

K. MURUGESAN

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Measurement Techniques ◽

Local Features ◽

Extraction Technique ◽

Similarity Measurement ◽

Global Features ◽

Linear Discriminant ◽

Feature Based ◽

Feature Extraction Technique

Face recognition plays a vital role in authentication, monitoring, indexing, access control and other surveillance applications. Much research on face recognition with various feature based approaches using global or local features employing a number of similarity measurement techniques have been done earlier. Feature based approaches using global features can effectively preserve only the Euclidean structure of face space, that suffer from lack of local features which may play a major role in some applications. On the other hand, wtih local features only the face subspace that best detects the essential face manifold structure is obtained and it also suffers loss in global features which may also be important in some other applications. Measuring similarity or distance between two feature vectors is also a key step for any pattern matching application. In this work, a new combined approach for recognizing faces that integrates the advantages of the global feature extraction technique by Linear Discriminant Analysis (LDA) and the local feature extraction technique by Locality Preserving Projections (LPP) with correlation based similarity measurement technique has been discussed. This has been validated by performing various experiments and by making a fair comparison with conventional methods.

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