【论文推荐】最新5篇图像分割(Image Segmentation)相关论文—多重假设、超像素分割、自监督、图、生成对抗网络

2018 年 2 月 7 日 专知 专知内容组(编)
【论文推荐】最新5篇图像分割(Image Segmentation)相关论文—多重假设、超像素分割、自监督、图、生成对抗网络

【导读】专知内容组整理了最近五篇图像分割(Image Segmentation)相关文章,为大家进行介绍,欢迎查看!

1. Improved Image Segmentation via Cost Minimization of Multiple Hypotheses通过多重假设最小化损失改进图像分割性能




作者Marc Bosch,Christopher M. Gifford,Austin G. Dress,Clare W. Lau,Jeffrey G. Skibo,Gordon A. Christie

摘要Image segmentation is an important component of many image understanding systems. It aims to group pixels in a spatially and perceptually coherent manner. Typically, these algorithms have a collection of parameters that control the degree of over-segmentation produced. It still remains a challenge to properly select such parameters for human-like perceptual grouping. In this work, we exploit the diversity of segments produced by different choices of parameters. We scan the segmentation parameter space and generate a collection of image segmentation hypotheses (from highly over-segmented to under-segmented). These are fed into a cost minimization framework that produces the final segmentation by selecting segments that: (1) better describe the natural contours of the image, and (2) are more stable and persistent among all the segmentation hypotheses. We compare our algorithm's performance with state-of-the-art algorithms, showing that we can achieve improved results. We also show that our framework is robust to the choice of segmentation kernel that produces the initial set of hypotheses.

期刊:arXiv, 2018年2月1日

网址

http://www.zhuanzhi.ai/document/dd2ca32cb74a374d653d1e8833bb0177

2. An Iterative Spanning Forest Framework for Superpixel Segmentation一个用于超像素分割的迭代生成森林框架




作者John E. Vargas-Muñoz,Ananda S. Chowdhury,Eduardo B. Alexandre,Felipe L. Galvão,Paulo A. Vechiatto Miranda,Alexandre X. Falcão

摘要Superpixel segmentation has become an important research problem in image processing. In this paper, we propose an Iterative Spanning Forest (ISF) framework, based on sequences of Image Foresting Transforms, where one can choose i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure to generate improved sets of connected superpixels (supervoxels in 3D) per iteration. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF methods to illustrate different choices of its components. These methods are compared with approaches from the state-of-the-art in effectiveness and efficiency. The experiments involve 2D and 3D datasets with distinct characteristics, and a high level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show that some of its methods are competitive with or superior to the best baselines in effectiveness and efficiency.

期刊:arXiv, 2018年1月30日

网址

http://www.zhuanzhi.ai/document/84d288b969678cd6a42661ad0ffb0a03

3. Mix-and-Match Tuning for Self-Supervised Semantic Segmentation自监督语义分割的混合匹配优化




作者Xiaohang Zhan,Ziwei Liu,Ping Luo,Xiaoou Tang,Chen Change Loy

摘要Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train a network without any human-provided labels. The key of this new form of learning is to design a proxy task (e.g. image colorization), from which a discriminative loss can be formulated on unlabeled data. Many proxy tasks, however, lack the critical supervision signals that could induce discriminative representation for the target image segmentation task. Thus self-supervision's performance is still far from that of supervised pre-training. In this study, we overcome this limitation by incorporating a "mix-and-match" (M&M) tuning stage in the self-supervision pipeline. The proposed approach is readily pluggable to many self-supervision methods and does not use more annotated samples than the original process. Yet, it is capable of boosting the performance of target image segmentation task to surpass fully-supervised pre-trained counterpart. The improvement is made possible by better harnessing the limited pixel-wise annotations in the target dataset. Specifically, we first introduce the "mix" stage, which sparsely samples and mixes patches from the target set to reflect rich and diverse local patch statistics of target images. A "match" stage then forms a class-wise connected graph, which can be used to derive a strong triplet-based discriminative loss for fine-tuning the network. Our paradigm follows the standard practice in existing self-supervised studies and no extra data or label is required. With the proposed M&M approach, for the first time, a self-supervision method can achieve comparable or even better performance compared to its ImageNet pre-trained counterpart on both PASCAL VOC2012 dataset and CityScapes dataset.

期刊:arXiv, 2018年1月30日

网址

http://www.zhuanzhi.ai/document/42991861993f309f7e7bc7fcad635556

4. Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scansDeep LOGISMOS: CT扫描中胰腺肿瘤的深度学习图3D分割方法




作者Zhihui Guo,Ling Zhang,Le Lu,Mohammadhadi Bagheri,Ronald M. Summers,Milan Sonka,Jianhua Yao

摘要This paper reports Deep LOGISMOS approach to 3D tumor segmentation by incorporating boundary information derived from deep contextual learning to LOGISMOS - layered optimal graph image segmentation of multiple objects and surfaces. Accurate and reliable tumor segmentation is essential to tumor growth analysis and treatment selection. A fully convolutional network (FCN), UNet, is first trained using three adjacent 2D patches centered at the tumor, providing contextual UNet segmentation and probability map for each 2D patch. The UNet segmentation is then refined by Gaussian Mixture Model (GMM) and morphological operations. The refined UNet segmentation is used to provide the initial shape boundary to build a segmentation graph. The cost for each node of the graph is determined by the UNet probability maps. Finally, a max-flow algorithm is employed to find the globally optimal solution thus obtaining the final segmentation. For evaluation, we applied the method to pancreatic tumor segmentation on a dataset of 51 CT scans, among which 30 scans were used for training and 21 for testing. With Deep LOGISMOS, DICE Similarity Coefficient (DSC) and Relative Volume Difference (RVD) reached 83.2+-7.8% and 18.6+-17.4% respectively, both are significantly improved (p<0.05) compared with contextual UNet and/or LOGISMOS alone.

期刊:arXiv, 2018年1月26日

网址

http://www.zhuanzhi.ai/document/deab2fa98863fa8b5011c2df13f6ec31

5. Fluorescence Microscopy Image Segmentation Using Convolutional Neural Network With Generative Adversarial Networks(基于卷积神经网络与生成对抗网络的荧光显微图像分割




作者Chichen Fu,Soonam Lee,David Joon Ho,Shuo Han,Paul Salama,Kenneth W. Dunn,Edward J. Delp

摘要Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images. 3D segmentation using deep learning has achieved promising results in microscopy images. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for microscopy volumes. This paper describes a 3D nuclei segmentation method using 3D convolutional neural networks. A set of synthetic volumes and the corresponding groundtruth volumes are generated automatically using a generative adversarial network. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully in 3D for various data sets.

期刊:arXiv, 2018年1月23日

网址

http://www.zhuanzhi.ai/document/9fdb6962bd4223b7ad1e6d818a0a304c

-END-

专 · 知

人工智能领域主题知识资料查看获取【专知荟萃】人工智能领域26个主题知识资料全集(入门/进阶/论文/综述/视频/专家等)

同时欢迎各位用户进行专知投稿,详情请点击

诚邀】专知诚挚邀请各位专业者加入AI创作者计划了解使用专知!

请PC登录www.zhuanzhi.ai或者点击阅读原文,注册登录专知,获取更多AI知识资料

请扫一扫如下二维码关注我们的公众号,获取人工智能的专业知识!

请加专知小助手微信(Rancho_Fang),加入专知主题人工智能群交流!

点击“阅读原文”,使用专知

登录查看更多
26

相关内容

图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程。它是由图像处理到图像分析的关键步骤。 所谓图像分割指的是根据灰度、颜色、纹理和形状等特征把图像划分成若干互不交迭的区域,并使这些特征在同一区域内呈现出相似性,而在不同区域间呈现出明显的差异性。

知识荟萃

精品入门和进阶教程、论文和代码整理等

更多

查看相关VIP内容、论文、资讯等

The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation of the U-Net to novel problems, however, comprises several degrees of freedom regarding the exact architecture, preprocessing, training and inference. These choices are not independent of each other and substantially impact the overall performance. The present paper introduces the nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on the basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away superfluous bells and whistles of many proposed network designs and instead focus on the remaining aspects that make out the performance and generalizability of a method. We evaluate the nnU-Net in the context of the Medical Segmentation Decathlon challenge, which measures segmentation performance in ten disciplines comprising distinct entities, image modalities, image geometries and dataset sizes, with no manual adjustments between datasets allowed. At the time of manuscript submission, nnU-Net achieves the highest mean dice scores across all classes and seven phase 1 tasks (except class 1 in BrainTumour) in the online leaderboard of the challenge.

0
10
下载
预览

Image segmentation is an important component of many image understanding systems. It aims to group pixels in a spatially and perceptually coherent manner. Typically, these algorithms have a collection of parameters that control the degree of over-segmentation produced. It still remains a challenge to properly select such parameters for human-like perceptual grouping. In this work, we exploit the diversity of segments produced by different choices of parameters. We scan the segmentation parameter space and generate a collection of image segmentation hypotheses (from highly over-segmented to under-segmented). These are fed into a cost minimization framework that produces the final segmentation by selecting segments that: (1) better describe the natural contours of the image, and (2) are more stable and persistent among all the segmentation hypotheses. We compare our algorithm's performance with state-of-the-art algorithms, showing that we can achieve improved results. We also show that our framework is robust to the choice of segmentation kernel that produces the initial set of hypotheses.

0
14
下载
预览

We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled intermediate images, the FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike existing methods, which requires full supervision with paired conditions and images, the FusedGAN can effectively leverage more abundant images without corresponding conditions in training, to produce more diverse samples with high fidelity. We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation. We demonstrate the efficacy of the FusedGAN in fine grained image generation tasks such as text-to-image, and attribute-to-face generation.

0
8
下载
预览
小贴士
相关资讯
相关VIP内容
相关论文
Deep Co-Training for Semi-Supervised Image Segmentation
Jizong Peng,Guillermo Estrada,Marco Pedersoli,Christian Desrosiers
4+阅读 · 2019年10月30日
Jungbeom Lee,Eunji Kim,Sungmin Lee,Jangho Lee,Sungroh Yoon
4+阅读 · 2019年2月27日
Fabian Isensee,Jens Petersen,Andre Klein,David Zimmerer,Paul F. Jaeger,Simon Kohl,Jakob Wasserthal,Gregor Koehler,Tobias Norajitra,Sebastian Wirkert,Klaus H. Maier-Hein
10+阅读 · 2018年9月27日
Cristina Mata,Guy Ben-Yosef,Boris Katz
7+阅读 · 2018年5月24日
Marvin T. T. Teichmann,Roberto Cipolla
7+阅读 · 2018年5月15日
Nathan D. Cahill,Tyler L. Hayes,Renee T. Meinhold,John F. Hamilton
4+阅读 · 2018年3月27日
Holger R. Roth,Chen Shen,Hirohisa Oda,Masahiro Oda,Yuichiro Hayashi,Kazunari Misawa,Kensaku Mori
5+阅读 · 2018年3月23日
Xiaowei Xu,Qing Lu,Yu Hu,Lin Yang,Sharon Hu,Danny Chen,Yiyu Shi
5+阅读 · 2018年3月13日
Marc Bosch,Christopher M. Gifford,Austin G. Dress,Clare W. Lau,Jeffrey G. Skibo,Gordon A. Christie
14+阅读 · 2018年1月31日
Navaneeth Bodla,Gang Hua,Rama Chellappa
8+阅读 · 2018年1月17日
Top