A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames


A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames – This paper addresses the problem of using a video capture system to capture a 3D shape of an object in real-time. Using video frames from the same object, there is a large amount of information about the object and its physical motion. We propose a video recognition framework, in which it is possible to directly extract the objects location and the motion within video frames, through the use of a deep network, to make an efficient reconstruction of the video frames. In addition, we propose an iterative method for the recognition of object location, motion and object oriented parts of video frames on the basis of the 3D features. We validate the performance of our approach by utilizing object-oriented parts and pose of objects.

We present a new method of multi-view classification based on multi-view convolutional neural networks for object segmentation. The proposed network consists of a group of deep convolutional neural networks trained to predict the next pose of the object over the same training set. Each convolutional neural network has an output that predicts a set of labeled pose updates for each frame, which can be considered as a multi-view classification problem. The proposed model can be described as a multi-view CNN (multi-view CNN) for multi-view object segmentation, which can be solved efficiently by exploiting multi-view convolutional networks for object segmentation. The proposed model will be used as a pre-processing step which makes a small error correction that minimizes the expected error rate. We evaluate the method on the large-scale object segmentation datasets such as the Flickr RGB dataset and the GifuNet dataset; it outperforms the state-of-the-art CNN for segmentation.

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A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames

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    Flexible Two-Row Recurrent Neural Network for ClassificationWe present a new method of multi-view classification based on multi-view convolutional neural networks for object segmentation. The proposed network consists of a group of deep convolutional neural networks trained to predict the next pose of the object over the same training set. Each convolutional neural network has an output that predicts a set of labeled pose updates for each frame, which can be considered as a multi-view classification problem. The proposed model can be described as a multi-view CNN (multi-view CNN) for multi-view object segmentation, which can be solved efficiently by exploiting multi-view convolutional networks for object segmentation. The proposed model will be used as a pre-processing step which makes a small error correction that minimizes the expected error rate. We evaluate the method on the large-scale object segmentation datasets such as the Flickr RGB dataset and the GifuNet dataset; it outperforms the state-of-the-art CNN for segmentation.


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