11 Declaration 13 Copyright 14 Dedication 15 Acknowledgements 16

A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally m...

background and objective: identifying the details of the injurious effects of alcohol seems to be decisive in nowadays with growing abuse of the substance. this study mainly aimed to assess the adverse effects of ethanol on the recalling of information in the wistar rats. materials and methods: male wistar rats (pasteur institute of iran) were evaluated using the novelty seeking behavior based ...

In analogy to animal research, where behavioral and internal neural dynamics are simultaneously analysed, this paper suggests a method for emergent behaviors arising in interaction with the underlying neural mechanism. This way an attempt to go beyond the indeterministic nature of the emergent behaviors of robots is made. The neural dynamics is represented as an interaction of memories of exper...

We develop novelty detection techniques for the analysis of data from a large-vehicle engine turbocharger in order to illustrate how abnormal events of operational significance may be identified with respect to a model of normality. Results are validated using polynomial function modelling and reduced dimensionality visualisation techniques to show that system operation can be automatically cla...

Novelty detection is the identification of abnormal system behaviour, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Given the complexity of su...

We propose a holistic approach to the problem of re-identification in an environment of distributed smart cameras. We model the re-identification process in a distributed camera network as a distributed multi-class classifier, composed of spatially distributed binary classifiers. We treat the problem of re-identification as an open-world problem, and address novelty detection and forgetting. As...