Proceedings ArticleDOI
Deep Level Sets for Salient Object Detection
Ping Hu,Bing Shuai,Jun Liu,Gang Wang +3 more
- pp 540-549
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TLDR
This work proposes a deep Level Set network to produce compact and uniform saliency maps and drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency.Abstract:
Deep learning has been applied to saliency detection in recent years. The superior performance has proved that deep networks can model the semantic properties of salient objects. Yet it is difficult for a deep network to discriminate pixels belonging to similar receptive fields around the object boundaries, thus deep networks may output maps with blurred saliency and inaccurate boundaries. To tackle such an issue, in this work, we propose a deep Level Set network to produce compact and uniform saliency maps. Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency. Besides, to propagate saliency information among pixels and recover full resolution saliency map, we extend a superpixel-based guided filter to be a layer in the network. The proposed network has a simple structure and is trained end-to-end. During testing, the network can produce saliency maps by efficiently feedforwarding testing images at a speed over 12FPS on GPUs. Evaluations on benchmark datasets show that the proposed method achieves state-of-the-art performance.read more
Citations
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Journal ArticleDOI
Focal Boundary Guided Salient Object Detection
TL;DR: This work proposes a novel deep model—Focal Boundary Guided (Focal-BG) network, designed to jointly learn to segment salient object masks and detect salient object boundaries and demonstrates that the joint modeling of salient object boundary and mask helps to better capture the shape details, especially in the vicinity of object boundaries.
Proceedings ArticleDOI
Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network
TL;DR: This work presents a novel deep learning method for unsupervised segmentation of blood vessels, inspired by the field of active contours and introduces a new loss term, which is based on the morphological Active Contours Without Edges (ACWE) optimization method.
Journal ArticleDOI
Saliency-guided level set model for automatic object segmentation
TL;DR: A new saliency-guided level set model (SLSM), which can automatically segment objects in color images guided by visual saliency, is proposed, which outperforms many state-of-the-art level set models and saliency detecting methods in accuracy and robustness.
Book ChapterDOI
Deep Active Lesion Segmentation
Ali Hatamizadeh,Assaf Hoogi,Debleena Sengupta,Wuyue Lu,Brian Wilcox,Daniel L. Rubin,Demetri Terzopoulos +6 more
TL;DR: Deep Active Lesion Segmentation (DALS) is introduced, a fully automated segmentation framework that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs).
Journal ArticleDOI
IoT-based 3D convolution for video salient object detection
TL;DR: This work proposes an end-to-end video SOD algorithm that contains a 3D convolution-based X-shape structure that directly represents the motion information in successive video frames efficiently, and 2D densely connected convolutional neural networks (DenseNet) with pyramid structure to extract the rich spatial contrast information in a single video frame.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
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Partial Differential Equations
TL;DR: In this paper, the authors present a theory for linear PDEs: Sobolev spaces Second-order elliptic equations Linear evolution equations, Hamilton-Jacobi equations and systems of conservation laws.