Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection


Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection – Recent years have witnessed the growth of social applications, such as video chat, which have proven to be challenging to solve. In this paper, we propose a novel method for facial recognition in videos. Specifically, we train a Deep Convolutional Neural Network (DCNN) to generate and annotate short snippets of the video frames. For these samples, we select an eye-level annotation of the frames and evaluate the performance by means of a series of experiments on different datasets. For training of the DCNN, we train it by using two different algorithms: one trained by hand and the other by using CNNs. We show that we obtain competitive and improved performance on both datasets: we achieve a performance over 95% accuracy.

We present an end-of-the-art multi-view, multi-stream video reconstruction pipeline based on Deep Learning. Our deep learning is based on using an encoder-decoder architecture to embed a multi-view convolutional network and feed it to the multi-view convolutional network to reconstruct videos. Since the output of the multi-view convolutional network can be different from the outputs of the deep network, it is more sensitive to occlusion, which prevents the reconstruction from using the full range image features. To improve the robustness of the reconstruction task, the convolutional layers are built from a multi-dimensional embedding, which is able to embed both the output and the reconstruction parameters. Experimental results show the proposed method can reconstruct well the full range of images.

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Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection

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  • Classification of non-mathematical data: SVM-ES and some (not all) SVM-ES

    Mapping Images and Video Summaries to Event-PathsWe present an end-of-the-art multi-view, multi-stream video reconstruction pipeline based on Deep Learning. Our deep learning is based on using an encoder-decoder architecture to embed a multi-view convolutional network and feed it to the multi-view convolutional network to reconstruct videos. Since the output of the multi-view convolutional network can be different from the outputs of the deep network, it is more sensitive to occlusion, which prevents the reconstruction from using the full range image features. To improve the robustness of the reconstruction task, the convolutional layers are built from a multi-dimensional embedding, which is able to embed both the output and the reconstruction parameters. Experimental results show the proposed method can reconstruct well the full range of images.


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