Proceedings ArticleDOI
Deep Level Sets for Salient Object Detection
Ping Hu,Bing Shuai,Jun Liu,Gang Wang +3 more
- pp 540-549
Reads0
Chats0
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
More filters
Book ChapterDOI
Combining deep learning and active contours opens the way to robust, automated analysis of brain cytoarchitectonics
Konstantin Thierbach,Pierre-Louis Bazin,Pierre-Louis Bazin,Walter de Back,Filippos Gavriilidis,Evgeniya Kirilina,Evgeniya Kirilina,Carsten Jäger,Markus Morawski,Stefan Geyer,Nikolaus Weiskopf,Nico Scherf +11 more
TL;DR: This work presents a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models and shows that the network can be trained efficiently from simple cell centroid annotations.
Book ChapterDOI
Guided M-Net for High-Resolution Biomedical Image Segmentation with Weak Boundaries
Shihao Zhang,Yuguang Yan,Pengshuai Yin,Zhen Qiu,Wei Zhao,Guiping Cao,Wan Chen,Jin Yuan,Risa Higashita,Qingyao Wu,Mingkui Tan,Jiang Liu +11 more
TL;DR: A guide-based model is proposed, called G-MNet, which seeks to exploit edge information from guided map to guide the corresponding lower resolution outputs of biomedical image segmentation, and will be more robust to noises and blurred object boundaries.
Proceedings ArticleDOI
Study on Small Samples SAR Image Recognition Detection Method Based on Transfer CNN
Shengna Wu,Ke Wang,Yiwen Ouyang +2 more
TL;DR: A transfer-based Convolutional Neural Network (CNN) small samples SAR images ground object recognition detection method, which was fine-tuned by using the transfer of small samples of the target domain SAR images to obtain a new CNN.
Proceedings ArticleDOI
Salient Object Detection by Contextual Refinement
TL;DR: A novel saliency detection framework with a Contextual Refinement Module (CRM) which consists of two sub-networks, Object Relation Unit (ORU) and Scene Context Unit (SCU) which captures complementary contextual information to give a holistic estimation of salient regions.
Journal ArticleDOI
A cross-modal edge-guided salient object detection for RGB-D image
TL;DR: In this paper, a cross-modal edge-guided salient object detection for RGB-D image is proposed, where edge information is extracted from the deep and shallow block of both modalities.
References
More filters
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.
Proceedings Article
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.
Proceedings Article
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.
Book
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.