John F. Haddon

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In this paper we apply artificial neural networks for classifying texture data of various natural objects found in FLIR images. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object(More)
It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours are found for more than one training class (model-1). We also(More)
Texture can be interpreted as a measure of the 'edginess' about a pixel and can thus be described by edge co-occurrence matrices. The matrix can be decomposed using 2-dimensional orthogonal Hermite functions, the coefficients of which provide a low order feature vector which is characteristic of the texture. The Hermite coefficients for 240 hand-segmented(More)
The detection of image segmented objects in video sequences is constrained by the a priori information available with a classifier. An object recogniser labels image regions based on texture and shape information about objects for which historical data is available. The introduction of a new object would culminate in its misclassification as the closest(More)