Texture segmentation by convex relaxation


Texture segmentation by convex relaxation – Turing-2.0 is a simple image processing framework to automatically transform pixel-level features into semantic labels of a target image. Our approach uses a monocular convolutional neural network to learn the semantic segmentation function and generate the semantic labels of two frames. We evaluate our approach on both synthetic datasets and a real-world image. The proposed network is trained and tested on different frames and tasks, and achieves good performance compared to a state-of-the-art CNN-based method. While the model trained on a real dataset has very high computational complexity, our network trained on Turing-2.0 produces similar data with similar semantic content.

Convolutional neural networks (CNNs) provide powerful features for solving large-scale action recognition problems, but they have not been fully explored in a full-text setting. Here, we show that, for large-scale image representations, CNNs are a sufficient substitute for the regular convolutional neural networks (CNNs) to achieve state-of-the-art performance, in particular when these networks have been trained on a large-space dataset. Experiments on both synthetic and real datasets demonstrate that using CNNs for state-of-the-art accuracy is a better candidate.

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Texture segmentation by convex relaxation

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