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Multi-focus image fusion based on fully convolutional networks

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Frontiers of Information Technology & Electronic Engineering 2020年第7期21卷 1019-1033页

作者： Rui GUO Xuan-jing SHEN Xiao-yu DONG Xiao-li ZHANG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China

We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.

关键词： Multi-focus image fusion fully convolutional networks Skip layer Performance evaluation

维普期刊数据库

Preserving details in semantics-aware context for scene parsing

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Science China(Information Sciences) 2020年第2期63卷 79-92页

作者： Shuai MA Yanwei PANG Jing PAN Ling SHAO Tianjin Key Laboratory of Brain-Inspired Intelligence Technology School of Electrical and Information EngineeringTianjin University School of Electronic Engineering Tianjin University of Technology and Education

Great success of scene parsing(also known as, semantic segmentation) has been achieved with the pipeline of fully convolutional networks(FCNs). Nevertheless, there are a lot of segmentation failures caused by large similarities between local appearances. To alleviate the problem, most of existing methods attempt to improve the global view of FCNs by introducing different contextual modules. Though the reconstructed high resolution output of these methods is of rich semantics, it cannot faithfully recover the fine image details owing to lack of desired precise low-level information. To overcome the problem, we propose to improve the spatial decoding process through embedding possibly lost low-level information in a principled way. To this end, we make the following three contributions. First, we propose a semantics conformity module to make low-level features variations agnostic. Second, we introduce semantics into the conformed low level features through guidance from semantically aware features. Finally, we institute the availability of various possible contextual features at feature fusion to enrich context information. The proposed approach demonstrates competitive performance on challenging PASCAL VOC 2012, Cityscapes,and ADE20K benchmarks in comparison to the state-of-the-art methods.

关键词： fully convolutional networks semantic segmentation cityscapes semantic-aware context

Feature context learning for human parsing

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Science China(Information Sciences) 2019年第12期62卷 6-19页

作者： Tengteng HUANG Yongchao XU Song BAI Yongpan WANG Xiang BAI School of Electronic Information and Communications Huazhong University of Science and Technology Alibaba Group

Parsing inconsistency, referring to the scatters and speckles in the parsing results as well as imprecise contours, is a long-standing problem in human parsing. It results from the fact that the pixel-wise classification loss independently considers each pixel. To address the inconsistency issue, we propose in this paper an end-to-end trainable, highly flexible and generic module called feature context module(FCM).FCM explores the correlation of adjacent pixels and aggregates the contextual information embedded in the real topology of the human body. Therefore, the feature representations are enhanced and thus quite robust in distinguishing semantically related parts. Extensive experiments are done with three different backbone models and four benchmark datasets, suggesting that FCM can be an effective and efficient plug-in to consistently improve the performance of existing algorithms without sacrificing the inference speed too much.

关键词： human parsing context learning fully convolutional networks graph convolutional network semantic segmentation

CGNet:cross-guidance network for semantic segmentation

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Science China(Information Sciences) 2020年第2期63卷 49-64页

作者： Zhijie ZHANG Yanwei PANG School of Electrical and Information Engineering Tianjin University

Semantic segmentation is a fundamental task in image analysis. The issue of semantic segmentation is to extract discriminative features for distinguishing different objects and recognizing hard examples.However, most existing methods have limitations on resolving this problem. To tackle this problem, we identify the contributions of the edge and saliency information for segmentation and present a novel end-to-end network, termed cross-guidance network(CGNet) to leverage them to benefit the semantic segmentation. The edge and saliency detection network are unified into the CGNet, and model the intrinsic information among them, guiding the process of extracting discriminative features. Specifically, the CGNet attempts to extract segmentation, edge, and salient features, simultaneously. Then it transfers them into the cross-guidance module(CGM) to generate the pre-knowledge features based on the modeled information, optimizing the context feature extraction process. The proposed approach is extensively evaluated on PASCAL VOC 2012,PASCAL-Person-Part, and Cityscapes, and achieves state-of-the-art performance, demonstrating the superiority of the proposed approach.

关键词： semantic segmentation fully convolutional networks pyramid network edge detection saliency detection cross-guidance

A New Hand Segmentation Method Based on fully convolutional Network

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A New Hand Segmentation Method Based on Fully Convolutional ...

Deep Semantic Segmentation Neural networks of Railway Scene

第30届中国控制与决策会议

作者： Shiyu Zhao Wankou Yang Yangang Wang Southeast University Central building

Hand segmentation has several important applications such as human-machine interaction, person behaviors identification and etc. However, traditional hand segmentation methods cannot be widely used due to the complexities of hand motion and environment. With the development of deep learning, convolutional neural networks are demonstrated as powerful in many vision tasks. In this paper, we present a hand segmentation method based on fully convolutional networks（FCNs）. We transfer the FCN-8 s architecture of VGG 16-layer net（VGG16） into a hand segmentation network. Through fine-tuning the version of VGG16 model in ILSVRC-2014 competition, we obtain a professional hand segmentation model. Experiments show that our method achieves a 91.0% mean IU on our hand dataset and gives a great performance on hand segmentation.

关键词： Hand detection Hand Segmentation fully convolutional networks

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Deep Semantic Segmentation Neural Networks of Railway Scene