Automatic Instrument Tracking in Video Based on Optical Flow (PoOL) for Planar Targets with Planar Spatial Structure


Automatic Instrument Tracking in Video Based on Optical Flow (PoOL) for Planar Targets with Planar Spatial Structure – Probabilistic models of action are computationally tractable and are often useful for tasks involving large-scale data (e.g., prediction of the trajectory of a taxi driver). We show how learning to recognize action from a probabilistic model of action is useful both in theory and practice. We consider the task of predicting a human driver in a video capturing scenario from a moving, semi-perpetuating and stationary camera. We show that in some cases the human driver may be moving, but the video frame captures a very large range of motion. In some cases, it is impossible to accurately predict the driver in the video capture because the driver will continuously move. In this paper, we propose a probabilistic model for the driver in this setting, where a human driver is spatially stationary and moving. The learner can predict the trajectory from the video frames using a probabilistic model of the driver. It can also predict the human driver’s location using a spatial tracking model. We illustrate that such a probabilistic model can be used to create novel action prediction techniques.

Inference of conversational language from spoken utterances is a challenge in spoken dialogue, which has been discussed in numerous works. This paper investigates the task of inferring a conversational phrase from language utterances. We formulate this task as a dialogue system where the system learns a translation vector and then a natural language translation. To the best of our knowledge, this paper shows that our approach can accurately infer a conversational phrase from speech utterances, and not from language. We present an application of this approach to a conversational dialogue system by using a text-to-speech system.

On the validity of the Sigmoid transformation for binary logistic regression models

Decide-and-Constrain: Learning to Compose Adaptively for Task-Oriented Reinforcement Learning

Automatic Instrument Tracking in Video Based on Optical Flow (PoOL) for Planar Targets with Planar Spatial Structure

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  • On the Performance of Convolutional Neural Networks in Real-Time Resource Sharing Problems using Global Mean Field Theory

    Modeling Conversational Systems with a Spoken Dialogue ModelInference of conversational language from spoken utterances is a challenge in spoken dialogue, which has been discussed in numerous works. This paper investigates the task of inferring a conversational phrase from language utterances. We formulate this task as a dialogue system where the system learns a translation vector and then a natural language translation. To the best of our knowledge, this paper shows that our approach can accurately infer a conversational phrase from speech utterances, and not from language. We present an application of this approach to a conversational dialogue system by using a text-to-speech system.


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