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Stacked U-Nets with Multi-Output for Road Extraction 31

Stacked U-Nets with Multi-Output for Road Extraction

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sun, Tao Chen, Zehui Yang, Wenxiang Wang, Yin Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.

关键词： Roads Decoding computer vision Encoding Feature extraction Neural networks Satellites

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Road detection with EOSResUNet and post vectorizing algorithm 31

Road detection with EOSResUNet and post vectorizing algorith...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Filin, Oleksandr Zapara, Anton Panchenko, Serhii EOS Data Analyt Menlo Pk CA 94027 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge.

关键词： Roads Neural networks Task analysis Satellites computer vision Earth Observing System Data analysis

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A Compact Deep Learning Model for Robust Facial Expression recognition 31

A Compact Deep Learning Model for Robust Facial Expression R...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kuo, Chieh-Ming Lai, Shang-Hong Sarkis, Michel Natl Tsing Hua Univ Hsinchu Taiwan Qualcomm Technol Inc San Diego CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we propose a compact frame-based facial expression recognition framework for facial expression recognition which achieves very competitive performance with respect to state-of-the-art methods while using much less parameters. The proposed framework is extended to a frame-to-sequence approach by exploiting temporal information with gated recurrent units. In addition, we develop an illumination augmentation scheme to alleviate the over-fitting problem when training the deep networks with hybrid data sources. Finally, we demonstrate the performance improvement by using the proposed technique on some public datasets.

关键词： Databases Training Face recognition Hidden Markov models Face Image recognition Image sequences

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Comparison of deep transfer learning strategies for digital pathology 31

Comparison of deep transfer learning strategies for digital ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mormont, Romain Geurts, Pierre Maree, Raphael Univ Liege Liege Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we study deep transfer learning as a way of overcoming object recognition challenges encountered in the field of digital pathology. Through several experiments, we investigate various uses of pre-trained neural network architectures and different combination schemes with random forests for feature selection. Our experiments on eight classification datasets show that densely connected and residual networks consistently yield best performances across strategies. It also appears that network fine-tuning and using inner layers features are the best performing strategies, with the former yielding slightly superior results.

关键词： Feature extraction Task analysis Biomedical imaging Training Support vector machines computer vision

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Word Spotting in Scene Images based on Character recognition 31

Word Spotting in Scene Images based on Character Recognition

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bazazian, Dena Karatzas, Dimosthenis Bagdanov, Andrew D. Univ Autonoma Barcelona CVC Barcelona Spain Univ Florence MICC I-50121 Florence Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.

关键词： Proposals Histograms Character recognition Text recognition Detectors Heating systems Image recognition

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Rotated Rectangles for Symbolized Building Footprint Extraction 31

Rotated Rectangles for Symbolized Building Footprint Extract...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dickenson, Matt Gueguen, Lionel UBER Technol Inc San Francisco CA 94103 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Building footprints (BFP) provide useful visual context for users of digital maps when navigating in space. This paper proposes a method for extracting and symbolizing building footprints from satellite imagery using a convolutional neural network (CNN). The CNN architecture outputs rotated rectangles, providing a symbolized approximation that works well for small buildings. Experiments are conducted on the four cities in the DeepGlobe Challenge dataset (Las Vegas, Paris, Shanghai, Khartoum). Our method performs best on suburbs consisting of individual houses. These experiments show that either large buildings or buildings without clear delineation produce weaker results in terms of precision and recall.

关键词： Buildings computer architecture Microprocessors Architecture Urban areas computer vision Satellites

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Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss 31

Land Cover Classification from Satellite Imagery With U-Net ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rakhlin, Alexander Davydow, Alex Nikolenko, Sergey Neuromat OU EE-10111 Tallinn Estonia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The land cover classification task of the DeepGlohe Challenge presents significant obstacles even to state of the art segmentation models due to a small amount of data, incomplete and sometimes incorrect labeling, and highly imbalanced classes. In this work, we show an approach based on the U-Net architecture with the Lovcisz-Softmax loss that successfully alleviates these problems: we compare several different convolutional architectures for U-Net encoders.

关键词： Image segmentation Satellites computer architecture Training Task analysis computer vision Stochastic processes

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Human Action Adverb recognition: ADHA Dataset and A Three-Stream Hybrid Model 31

Human Action Adverb Recognition: ADHA Dataset and A Three-St...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pang, Bo Zha, Kaiwen Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We introduce the first benchmark for a new problem - recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labelling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results reveal that such methods are unsatisfactory. Furthermore, we propose a novel three-stream hybrid model to tackle the HAA problem, which achieves better performances and receives relatively promising results.

关键词： Image recognition Mood Emotion recognition Sports Benchmark testing Face pattern recognition

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Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery 31

Stacked U-Nets for Ground Material Segmentation in Remote Se...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ghosh, Arthita Ehrlich, Max Shah, Sohil Davis, Larry Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

关键词： Semantics Image segmentation Remote sensing Convolution Training Satellites computer architecture

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Deep Convolutional Neural Networks with Integrated Quadratic Correlation Filters for Automatic Target recognition 31

Deep Convolutional Neural Networks with Integrated Quadratic...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Millikan, Brian Foroosh, Hassan Sun, Qiyu Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA Univ Cent Florida Dept Math Orlando FL 32816 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Automatic target recognition involves detecting and recognizing potential targets automatically, which is widely used in civilian and military applications today. Quadratic correlation filters were introduced as two-class recognition classifiers for quickly detecting targets in cluttered scene environments. In this paper, we introduce two methods that integrate the discrimination capability of quadratic correlation filters with the multi-class recognition ability of multilayer neural networks. For mid-wave infrared imagery, the proposed methods are demonstrated to be multi-class target recognition classifiers with very high accuracy.

关键词： Target recognition Correlation Training Object detection Standards Convolutional neural networks

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