John E. Ball

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—We present a semi-automated supervised hyper-spectral image segmentation algorithm based on the level set methodology. In the proposed procedure, seed pixels are automatically selected by their similarity to the training signatures, and speed functions that control the level set propagation are created based on pixel similarity to the seed signature and(More)
—This paper presents a semi-automated supervised level set hyperspectral image segmentation algorithm. The proposed method uses near-optimal speed functions (which control the level set segmentation) that are composed of a spectral similarity term and a stopping term. The spectral similarity term is used to compare pixels to class training signatures and is(More)
—We present a supervised hyperspectral classification procedure consisting of an initial distance-based segmentation method that uses best band analysis (BBA), followed by a level set enhancement that forces localized region homogeneity. The proposed method is tested on two hyperspectral images of an urban and rural nature. The proposed method is compared(More)
Outlier Detection is a critical and cardinal research task due its array of applications in variety of domains ranging from data mining, clustering, statistical analysis, fraud detection, network intrusion detection and diagnosis of diseases etc. Over the last few decades, distance-based outlier detection algorithms have gained significant reputation as a(More)
OBJECTIVE Monocortical screws are commonly employed in locking plate fixation, but specific recommendations for their placement are lacking and use of short monocortical screws in metaphyseal bone may be contraindicated. Objectives of this study were to evaluate axial pullout strength of two different lengths of monocortical screws placed in various regions(More)