Visual-Inertial Character Recognition with Learned Deep Convolutional Sparse Representation

Visual-Inertial Character Recognition with Learned Deep Convolutional Sparse Representation – The paper was submitted to the 2017 Workshop on Deep Neural Network Systems and Machine Learning. The paper was submitted to the 2017 Workshop on Neural Network Systems and Machine Learning.

The object detection framework for multi-target tracking has received a lot of attention in the past years. One of the applications that has been adopted in this work is multi-object tracking, which relies on a large number of target locations. However, most of the existing multi-object tracking methods treat the object locations as a feature descriptor of the target locations. In this work, we consider the task where each point with an object is seen as having a similar pose to those with a different pose. The pose of each region has to be known beforehand to be used for tracking. We propose a deep learning framework that uses a recurrent neural network (RNN) to jointly learn to learn a pose and target location descriptors. We provide two benchmark datasets, namely, a real-world database and an online and real-world dataset for the state-of-the-art and demonstrate that the network learned correctly on both datasets. The approach is evaluated in the COCO database and our method performs favorably compared to state-of-the-art systems even though our approach is very expensive, especially for the same pose.

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Visual-Inertial Character Recognition with Learned Deep Convolutional Sparse Representation

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  • An Integrated Learning Environment for Two-Dimensional 3D Histological Image Reconstruction

    Multi-target tracking without line complementationThe object detection framework for multi-target tracking has received a lot of attention in the past years. One of the applications that has been adopted in this work is multi-object tracking, which relies on a large number of target locations. However, most of the existing multi-object tracking methods treat the object locations as a feature descriptor of the target locations. In this work, we consider the task where each point with an object is seen as having a similar pose to those with a different pose. The pose of each region has to be known beforehand to be used for tracking. We propose a deep learning framework that uses a recurrent neural network (RNN) to jointly learn to learn a pose and target location descriptors. We provide two benchmark datasets, namely, a real-world database and an online and real-world dataset for the state-of-the-art and demonstrate that the network learned correctly on both datasets. The approach is evaluated in the COCO database and our method performs favorably compared to state-of-the-art systems even though our approach is very expensive, especially for the same pose.


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