IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences: an International Journal
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Affine-Invariant Geometric Shape Priors for Region-Based Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatic species identification of live moths
Knowledge-Based Systems
Image Watermarking Using Krawtchouk Moments
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Automated Insect Identification through Concatenated Histograms of Local Appearance Features
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
EURASIP Journal on Applied Signal Processing
A Novel Feature Extraction Technique for Face Recognition
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Automated Motion Tracking of Insects Using Invariant Moments in Image Sequence
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 03
Moment-Based Techniques for Image Retrieval
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
A Video Zero-Watermark Algorithm against RST Attacks
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
IITA '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 02
Vehicle-logo Recognition Method Based on Tchebichef Moment Invariants and SVM
WCSE '09 Proceedings of the 2009 WRI World Congress on Software Engineering - Volume 03
A novel Euclidean quality threshold ARTMAP network and its application to pattern classification
Neural Computing and Applications - Special Issue - KES2008
Blurred image recognition by Legendre moment invariants
IEEE Transactions on Image Processing
A self-organizing neural network for supervised learning, recognition, and prediction
IEEE Communications Magazine
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Constructive feedforward ART clustering networks. I
IEEE Transactions on Neural Networks
Constructive feedforward ART clustering networks. II
IEEE Transactions on Neural Networks
Fuzzy ARTMAP with input relevances
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
Skin cancer extraction with optimum fuzzy thresholding technique
Applied Intelligence
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The main objective of this paper is to investigate the use of Quality Threshold ARTMAP (QTAM) neural network in classifying the feature vectors generated by moment invariant for the insect recognition task. In this work, six different types of moment invariant technique are adopted to extract the shape features of the insect images. These moment techniques are Geometrical Moment Invariant (GMI), United Moment Invariant (UMI), Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI), Tchebichef Moment Invariant (TMI) and Krawtchouk Moment Invariant (KMI). All the moment techniques are analyzed using the concept of intraclass and interclass analysis. In intraclass analysis, several computation methods are introduced in order to examine the invariance properties of adopted moment techniques for the same insect object. Meanwhile, the classification accuracy of neural networks is adopted to measure the interclass characteristic and the effectiveness of moment technique in extracting the shape features of insect images. Other types of neural networks are also utilized in this research work. This includes novel enhancement technique based on the Gaussian and Mahalanobis function that design to increase its prediction accuracy. All the other networks used to classify the feature vectors are based on the Fuzzy ARTMAP (FAM) neural network. The experimental results indicated that the Krawtchouk Moment Invariant technique generated the highest classification accuracy for most of the networks used and generated the smallest error for the intraclass analysis. Using different normalization technique, the Quality Threshold ARTMAP and Mahalanobis distance function (QTAM-m) network gave the highest insect recognition results when compared to other networks.