00000nam 2200000zi 4500 9.893010 CaOODSP 20221107173328 m o d f cr bn||||||||| 201029e199702##oncad ob f000 0 eng d CaOODSP eng rda CaOODSP eng fre Co24-3/7-97-002E-PDF Sala, Kenneth Leonard Charles‏, author. Image classification by neural networks using moment invariant feature vectors / by Kenneth L. Sala. Ottawa : Communication Research Centre, Industry Canada, February 1997. 1 online resource (1 volume (various pagings)) : illustrations, graphs. text txt rdacontent computer c rdamedia online resource cr rdacarrier CRC report ; no. 97-002 Digitized edition from print [produced by Innovation, Science and Economic Development Canada]. "The work described in this document was sponsored by the Department of National Defence under Task 5BB14." Includes bibliographical references (pages REF-1-REF-12). "An image classification system based upon the extraction of moment invariant feature vectors and an artificial neural network classifier is described. The moment invariant feature vectors are derived from the test images using series of orthogonal basis functions. Six different basis functions are studied which include four types of Zernike functions and two types of Walsh functions. Four different schemes for the normalization of the feature vectors are also investigated. The images used in the study possess random scales, lateral positions, and angles of orientation in the image plane. In addition, random noise with different signal-to-noise ratios is superimposed upon the images. The feature vector extraction technique employs the concept of moment invariants so that the feature vector components are independent of the image's scale, lateral position, and orientation. The neural network employed for the classification task is a multilayer perceptron network which is trained with the backpropagation algorithm. The performance of the overall classification system is determined by measuring the classification accuracy as a function of the signal-to-noise ratio of the test imagery. The work and the results presented in this study form the basis for a neural network based, image recognition system which will be employed in the classification of military, synthetic aperture radar (SAR) imagery of land targets"--Abstract, page iii. Includes abstract in French. Synthetic aperture radar Image quality. Vector processing (Computer science) Radar à synthèse d'ouverture Qualité de l'image. Traitement vectoriel. Canada. Industry Canada, issuing body. Communications Research Centre (Canada), issuing body. CRC report ; no. 97-002. (CaOODSP)9.882492 PDF 13.39 MB https://publications.gc.ca/collections/collection_2020/isde-ised/Co24/Co24-3-7-97-002-eng.pdf