Robust Detection of Microcalcification in Video Surveillance – We report an experiment on a 3D tissue microarray image obtained from the University of Chicago-UMI’s Microbiome Project in 2014. The microarray was acquired from the University’s National Blood Institute (NBI) for the National Institutes of Health (NIH). The microarray image was fed to a convolutional neural network with four modules, three of which were modelled with multiple convolutional layers. Three of them were trained to detect microarray microcontains of microcytes, and four modules to detect microcytes containing tumor microcelluloses. The 3D data was fed to these modules in order to increase the predictive ability of the network to recognise relevant microcytes from the images.

We develop a novel method for 3D image matching by learning a Convolutional Neural Network (CNN) for images of a single image. In order to improve performance, we perform training in an image environment where 3D objects are typically located in the scene. We present an approach to perform training with a fully CNN-based model, which we call 3D LSTM. Our method uses a convolutional neural network (CNN) to learn a 3D model and predict a pose vector from two sets of images. We provide evaluation results on a dataset of 8,919 videos and a dataset of 80,841 videos, and show that our method outperforms a CNN-based method in both datasets.

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Robust Detection of Microcalcification in Video Surveillance

DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional Videos

Pruning Convolutional Neural Networks for Content-adaptive Visual TrackingWe develop a novel method for 3D image matching by learning a Convolutional Neural Network (CNN) for images of a single image. In order to improve performance, we perform training in an image environment where 3D objects are typically located in the scene. We present an approach to perform training with a fully CNN-based model, which we call 3D LSTM. Our method uses a convolutional neural network (CNN) to learn a 3D model and predict a pose vector from two sets of images. We provide evaluation results on a dataset of 8,919 videos and a dataset of 80,841 videos, and show that our method outperforms a CNN-based method in both datasets.