Image Segmentation using Sparsity-based Densely Connected Convolutional Neural Networks with Outliers


Image Segmentation using Sparsity-based Densely Connected Convolutional Neural Networks with Outliers – The most popular segmentation models for image classification are based on unsupervised deep architectures. Recently, it has been shown that the traditional deep architecture models have not been well suited to real-world tasks such as human evaluation of images. In this paper, we propose a new deep architecture called PNS (Deep Network for Image Prediction). PNS is a combination of two different learning approaches. First, it learns to predict image segments accurately while the network learns to estimate an image’s shape. The network is trained to learn the optimal pose, and then it is able to predict each segment in a supervised manner. We test PNS on two datasets and show that it outperforms most existing approaches for human evaluation of image segmentation.

The segmentation of human skin is an active and challenging task in biomedicine. A great deal of effort is devoted to understanding the relationship between skin texture and its physical structure and consequently determining the shape of the segments. Despite the considerable amount of research work on segmentation of skin to address these two questions, there is very little progress on skin segmentation algorithms in general. In this paper, we extend the work on skin segmentation and develop a new methodology based on deep neural network models on the problem of segmentation. To our best knowledge, our method is the first successful segmentation algorithm for a broad class of skin texture and physical structure. The method also demonstrates the superiority of our algorithm over existing segmentation algorithms that do not focus on skin texture and physical structure. We evaluate our approach on a set of challenging Skin texture segmentations and report an absolute improvement of 4% compared to our existing segmentation based method. The approach was validated on a dataset of over 200,000 face images with high level skin textures as well as a subset of skin image types with different shapes and characteristics.

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Image Segmentation using Sparsity-based Densely Connected Convolutional Neural Networks with Outliers

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  • A Unified Approach to Learning with Structured Priors

    Robust Multidimensional Segmentation based on Edge PredictionThe segmentation of human skin is an active and challenging task in biomedicine. A great deal of effort is devoted to understanding the relationship between skin texture and its physical structure and consequently determining the shape of the segments. Despite the considerable amount of research work on segmentation of skin to address these two questions, there is very little progress on skin segmentation algorithms in general. In this paper, we extend the work on skin segmentation and develop a new methodology based on deep neural network models on the problem of segmentation. To our best knowledge, our method is the first successful segmentation algorithm for a broad class of skin texture and physical structure. The method also demonstrates the superiority of our algorithm over existing segmentation algorithms that do not focus on skin texture and physical structure. We evaluate our approach on a set of challenging Skin texture segmentations and report an absolute improvement of 4% compared to our existing segmentation based method. The approach was validated on a dataset of over 200,000 face images with high level skin textures as well as a subset of skin image types with different shapes and characteristics.


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