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 mo-
The image such as CT scan, x -ray image, CCTV videos and hand phone's camera is kind of low resolution image producers. Digital camera captured the continuous scenes and transform into discrete presentation in term of space and intensity. In sampling process it may create aliasing and information lost at frequency below the Nyquist sampling rates. Therefore the image suffered with an ill-posed problem by aliasing and loss of frequency. The problem ill-pose problem could be solved by applying Super Resolution (SR) techniques. The SR process contains of image registration, interpolation and image reconstruction. However this paper is focus on an analysis the best performance offered by interpolation techniques. An analysis procedure requires interpolation kernel inspection into frequency domain plotting to determine the best kernel response in pass and stop band. Otherwise use Peak Signal to Noise Ratio as indicator the similarity simulated with original image. In this study found the cubic spline interpolation is provided the smoother function frequency response with less ripples in stop band and good pass response. Besides that, it shows a superiorly in lead the highest PSNR for all type image tests with several of upscale. The best response and less distortion effect generated by kernel is preferable candidate to produce an efficient image application with low maintenances.
This paper introduces a novel neural network model known as the Euclidean quality threshold ARTMAP (EQTAM) network and its application to pattern classification. The model is constructed based on fuzzy ARTMAP (FAM) and the quality threshold clustering principle. The main objective of EQTAM is to overcome the effects of training data sequences on FAM and, at the same time, to improve its classification performance. Several artificial data sets and benchmark medical data sets are used to evaluate the effectiveness of the proposed model. Performance comparisons between EQTAM and ARTMAPbased as well as other classifiers are made. From the experimental results, it can be observed that EQTAM is able to produce good results. More importantly, the performance of EQTAM is robust against the effect of training data orders or sequences.
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