Multi-Channel Multi-Resolution RGB-D Light Field Video with Convolutional Neural Networks


Multi-Channel Multi-Resolution RGB-D Light Field Video with Convolutional Neural Networks – Constraint-based image segmentation is a key challenge for many computer vision problems. Most existing methods either use an RGB-D image as a pre-processing step, or directly feed the RGB image into a convolutional neural network (CNN). Previous work has explored the idea of adapting CNN’s structure to make use of the features of the input image. This work is based on learning a CNN model of the input image. In this paper, to overcome these two shortcomings, we propose a novel deep learning-based method to segment the input image with a CNN. Using the deep CNN model, we extend the existing CNN segmentation approach to the task of fine-tuning the image features. Results demonstrate that our proposed CNN model achieves a better performance on our segmentation task than the existing CNN model with respect to the performance of other existing deep learning-based CNN models.

We present a general algorithm for identifying human gestures using word embeddings on image data. In particular, a word embeddings is an effective descriptor for recognizing gestures that are consistent with a given visual description. Our model is based on the notion of a semantic semantic similarity. The semantic similarity determines which regions correspond to the desired gestures. We show that a semantic-semantic similarity could be used to discriminate people with gestures. By contrast, our model is formulated as a feature extraction model. We further provide a simple computational model for the semantic-semantic similarity that we use to demonstrate the approach. Finally, we experiment the approach on the task of recognizing gestures using text descriptions of people.

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Multi-Channel Multi-Resolution RGB-D Light Field Video with Convolutional Neural Networks

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  • Modeling Content, Response Variation and Response Popularity within Blogs for Classification

    Reconstructing images of traffic video with word embeddings: a multi-dimensional frameworkWe present a general algorithm for identifying human gestures using word embeddings on image data. In particular, a word embeddings is an effective descriptor for recognizing gestures that are consistent with a given visual description. Our model is based on the notion of a semantic semantic similarity. The semantic similarity determines which regions correspond to the desired gestures. We show that a semantic-semantic similarity could be used to discriminate people with gestures. By contrast, our model is formulated as a feature extraction model. We further provide a simple computational model for the semantic-semantic similarity that we use to demonstrate the approach. Finally, we experiment the approach on the task of recognizing gestures using text descriptions of people.


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