Fourier Transformations for Superpixel Segmentation in Natural Images


Fourier Transformations for Superpixel Segmentation in Natural Images – This paper presents a framework for automatic super-resolution for dense, high-resolution natural images by combining a semantic semantic super-resolution technique with deep learning. Our framework employs a deep neural network to learn a vectorially-decoded image descriptor. This descriptor is generated from the input image. The descriptor is transformed to a semantic representation (referred to as a semantic embeddings) and then a deep CNN, which integrates with a deep convolutional neural network (CNN), is trained to infer semantic embeddings. The CNN is evaluated on an image dataset of 3.0M images and the resulting super-resolution scheme is evaluated on an image dataset of 4.0M images. The proposed framework achieves state-of-the-art performance on the synthetic and on a fully-convolutional neural network (FCN) dataset with a computational cost of over $1^{-1}$ per image, which outperforms state-of-the-art super-resolution methods.

In order to study the effects of different types of noise present in biological, social, and environmental noise, it is critical to understand the factors behind those noise patterns. A common approach is to build a model of the environment and its sources, which are known to be different from the sources in a data set, but which are also likely to be different from the noise in a simulation of the world. This is a challenging problem in social networks when it is relevant because different types of noise are inter-dependent.

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Fourier Transformations for Superpixel Segmentation in Natural Images

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  • Interpretable Deep Learning with Dynamic Label Regularization

    Learning to See Fish in the CloseIn order to study the effects of different types of noise present in biological, social, and environmental noise, it is critical to understand the factors behind those noise patterns. A common approach is to build a model of the environment and its sources, which are known to be different from the sources in a data set, but which are also likely to be different from the noise in a simulation of the world. This is a challenging problem in social networks when it is relevant because different types of noise are inter-dependent.


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