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Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the(More)
We address the problem of fine-grained action localization from temporally untrimmed web videos. We assume that only weak video-level annotations are available for training. The goal is to use these weak labels to identify temporal segments corresponding to the actions, and learn models that generalize to unconstrained web videos. We find that web images(More)
We present a novel scale adaptive, nonparametric approach to clustering point patterns. Clusters are detected by moving all points to their cluster cores using shift vectors. First, we propose a novel scale selection criterion based on local density isotropy which determines the neighborhoods over which the shift vectors are computed. We then construct a(More)
The purpose of this in vitro study was to analyze the effectiveness of 2.5% sodium hypochlorite (NaOCl) in preventing inoculation of periapical tissue with contaminated patency files. Fifty single-rooted teeth with single canals were used in the study. They were randomly divided into five groups of which two were experimental groups, two positive controls,(More)
INTRODUCTION The present in vitro study was undertaken to evaluate and compare the sealing ability of hybrid composite, glass ionomer cement type II, silver amalgam and Ketac molar as permanent filling material in root canal treated teeth. METHODOLOGY Hundred maxillary central incisors were selected for the study. After cleaning all the teeth, root canal(More)
Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving(More)
Evolutionary computation is used for improved information extraction from high-resolution satellite imagery. The utilization of evolutionary computation is based on stochastic selection of input parameters often defined in a trial-and-error approach. However, exploration of optimal input parameters can yield improved candidate solutions while requiring(More)
This thesis develops an algorithm for shape representation and matching. The algorithm is an object centered, boundary-based method for shape recognition. Global features of the shape are utilized to define a frame of reference relative to which local shape features are characterized. The curvature of the boundary at a point is the local feature used.(More)
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