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In this paper, we consider two inter-dependent deep networks, where one network taps into the other, to perform two challenging cognitive vision tasks - scene classification and object recognition jointly. Recently, convolutional neural networks have shown promising results in each of these tasks. However, as scene and objects are interrelated, the performance of both of these recognition tasks can...
In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error. The current best-performing detectors are based on the technique of finding region proposals in order to localize objects. Despite having very good performance, these techniques are computationally expensive due to having large number of proposed regions...
Motion and image analysis are both important for robust solutions to video search of activities; the physics-based, data-driven Hamiltonian Monte Carlo (HMC), a Markov chain Monte Carlo variant that is efficient in searching large dimensional spaces, simultaneously examines the combined motion and image space. In this paper, we generalize the data-driven HMC to no longer depend upon ad hoc Guide Hamiltonians...
In this work, we consider a camera network where processing is distributed across the cameras. Our goal is to recognize actions of multiple targets consistently observed over the entire network. To obtain consistent and better results we need to properly fuse the action scores from multiple cameras. There have been multiple works on distributed tracking and distributed data association for multiple...
The Neurobiological model of motion recognition posits a Motion Energy Pathway and a Form Pathway but leaves the mechanism for Integration open. In this paper, we present a stochastic Integration methodology, based on the Hamiltonian Monte Carlo, which explores both the Motion and Form space by creating data-driven proposals in the image/form space which are then confirmed in the motion space. We...
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