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With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scal-able static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition(More)
We present a biologically-motivated system for the recognition of actions from video sequences. The approach builds on recent work on object recognition based on hierarchical feedforward architectures [25, 16, 20] and extends a neurobiological model of motion processing in the visual cortex [10]. The system consists of a hierarchy of spatio-temporal feature(More)
Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using(More)
Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our(More)
BACKGROUND Ankyrin 3 (ANK3) has been strongly implicated as a risk gene for bipolar disorder (BD) by recent genome-wide association studies of patient populations. However, the genetic variants of ANK3 contributing to BD risk and their pathological function are unknown. METHODS To gain insight into the potential disease relevance of ANK3, we examined the(More)
We describe a fully trainable computer vision system enabling the automated analysis of complex mouse behaviors. Our system computes a sequence of feature descriptors for each video sequence and a classifier is used to learn a mapping from these features to behaviors of interest. We collected a very large manually annotated video database of mouse behaviors(More)
We present a biologically-motivated system for the recognition of actions from video sequences. The approach builds on recent work on object recognition based on hierarchical feedforward architectures and extends a neurobiological model of motion processing in the visual cortex. The system consists of a hierarchy of spatio-temporal feature detectors of(More)