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Improved Modality-Invariant Feature Learning for Degraded Face recognition 11

Improved Modality-Invariant Feature Learning for Degraded Fa...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Yang, Zhifang Song, Zhengxin Wen, Ke Wuhan Inst Technol Sch Elect & Informat Engn Hubei Engn Res Ctr Video Image & HD Project Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636385

In this paper, we investigate the degraded face recognition problem. At checkpoints, it is common that a passenger's photo is digitally taken on the spot and compared with archived images scanned from printed photos. Therefore, the gallery set and the probe set come through two different media. The distortions introduced in the printing and the scanning processes often lead to unsatisfactory identification performance, necessitation further investigations in tackling degraded face recognition. Therefore, we propose an improved modality-invariant feature (IMIF) approach which combines the modality _ invariant features with a discriminative learning procedure to handle the variations in expression, occlusion and degradation. Experiments on the degraded face database show that the proposed IMIF enhances the degraded face recognition performance compared with other methods and validates the effectiveness of the proposed method.

关键词： Degraded face recognition printed and scanned face image per person modality-invariant feature

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Performance evaluation of SIFT under low light contrast 11

Performance evaluation of SIFT under low light contrast

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Wang, Hao Jin, Songzhi Wei, Xiaodan Zhang, Cong Hu, Ruiguang Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing 100854 Peoples R China

ISBN: (纸本)9781510636385

As an excellent method for extracting distinctive invariant features from images, SIFT (scale-invariant feature transform) can effectively resist affine transformation such as translation and rotation of images, and theoretically has better resistance to illumination changes([1]). However, in practical applications the performance of SIFT is always affected by the contrast reduction caused by illumination changes. In this paper, the performance of SIFT under different contrasts is systematically analyzed and evaluated, and a reasonable explanation is given for the reason of SIFT performance change under different illumination conditions. And a SIFT fast matching method based on contrast compression is proposed.

关键词： SIFT image matching low contrast performance evaluation

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Semantic image segmentation network based on deep learning 11

Semantic image segmentation network based on deep learning

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Chen, Bo Zhang, Jiahao Zhou, Jianbang Chen, Zhong Yang, Tian Zhang, Yanna Huazhong Univ Sci & Technol Inst Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China Chinese Acad Sci Inst Aerosp Informat Innovat Beijing Peoples R China Henan Univ Dept Lab & Equipment Management Kaifeng Henan Peoples R China

ISBN: (纸本)9781510636361

Semantic segmentation is one of the basic themes in computer vision. Its purpose is to assign semantic tags to each pixel of an image, which has been applied in many fields such as medical field, intelligent transportation and remote sensing image. In this paper, we use deep learning to solve the task of remote sensing semantic image segmentation. We propose an algorithm for semantic segmentation of the Attention Seg-Net network combined with SegNet and attention gate. Our proposed network can better segment vegetation, buildings, water bodies and roads in the test set of remote sensing images.

关键词： Semantic Segmentation Attention Seg-Net Deep Learning Attention gate

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Hierarchical attention networks for hyperspectral image classification 11

Hierarchical attention networks for hyperspectral image clas...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Li, Zhengtao Xu, Hai Zhang, Yaozong Zhang, Tianxu Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636422

Attention can be interpreted as a method which allocates available computing power to the most informative part of the signal. In deep learning, attention mechanism also helps us to dig out the subtle information. In hyperspectral classification, the discrimination of some land cover types depends on the fine differences of hyperspectral, but most classification methods do not focus on the fine differences between hyperspectral categories. In this paper, a hierarchical group attention classification method is proposed to focus on the differences of categories from coarse to fine, therefore, the fine differences between categories can be obtained to achieve more accurate classification. For comparison and validation, we test the proposed approach with three other classification approaches on Salinas and Indian datasets, and the experiments demonstrate that our proposed approach can distinguish the spectral subtle differences of similar categories more accurately.

关键词： Hyperspectral classification attention mechanism hierarchical attention

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A CNN-based Method for Adaptive Landmark Selection in Remote Sensing image 11

A CNN-based Method for Adaptive Landmark Selection in Remote...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Chen Yongzhan Yang Weidong Cao Yaoxin Liu Chenhua Yang Dong Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China

ISBN: (纸本)9781510636361

The technology of automatic selecting landmark plays a significant role in aircraft navigation and ground information assurance. Compared to the normal object detection, it is quite difficult to describe and quantify the characteristics of a landmark due to its various status and no stable structure. This paper attempts to innovatively combine CNN with the technology of selecting landmark. The algorithm used in this paper uses a structurally stable adaptation region as a learning sample to train the CNN classification model. In the selection phase, remote sensing images were cut into pieces of patches, landmark of which was then recognized through the CNN classification model. Non-maxima suppression was used to filter out the low rate landmark and a correlation peak-based uniqueness analysis (the ratio of primary and secondary peaks and the highest sharpness of peak) was used to ensure landmark with no similarity pattern in the remote sensing image. The results indicate the effectiveness of proposed method for Selecting Remote Sensing image Adaptation Structure.

关键词： landmark Convolutional Neural Networks similarity pattern remote sensing image

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A semantic segmentation method for Satellite image Change Detection 11

A semantic segmentation method for Satellite Image Change De...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Zhang, Jiahao Chen, Bo Zhou, Jianbang Yang, Jingkun Chen, Zhong Yang, Jian Zhang, Yanna Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Inst Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Inst Aerosp Informat Innovat Beijing Peoples R China Henan Univ Dept Lab & Equipment Management Kaifeng Peoples R China

ISBN: (纸本)9781510636361

We apply the semantic segmentation method in deep network to high precision satellite image change detection, and propose a network framework to improve the detection *** directly processed the image after registration, without the steps of radiometric correction, and avoided the tedious steps of manual feature design by traditional *** tried to use Unet and Deeplab v3 model to divide the change area, and added the structure of jumping connection on the basis of Deeplab network, which made the edge of the detection graph more accurate and improved the performance of the *** test results show that this method is effective for detecting the change of high-precision remote sensing images.

关键词： change detection semantic segmentation convolutional neural network Deep learning

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Coherent ISAR imaging based on phase matching processing of complex range profile for procession space target 11

Coherent ISAR imaging based on phase matching processing of ...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Kang, Hongyan Tian, Biao Liu, Yongxiang Natl Univ Def Technol Tech Serv Ctr Vocat Educ Changsha Peoples R China Natl Univ Def Technol Coll Elect Sci & Technol Changsha Peoples R China

ISBN: (纸本)9781510636347

In order to improve the inverse synthetic aperture radar (ISAR) imaging quality of precession space target, an algorithm based on phase matching processing (PMP) of complex range profile envelope (CRPE) is proposed in this paper. By phase matching processing, only the echo components located at the scattering points corresponding to the main body of the warhead can be coherently accumulated. The echoes of other scattering centers without any coherence will cancel each other when they are transformed. Hence, the focusing of scatters centers of the warhead main body is improved and the interference caused by non-spin symmetric components is well suppressed. Simulation results confirmed the effectiveness of the method.

关键词： inverse synthetic aperture radar precession space target phase matching processing complex range profile envelope

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Anti-Interference recognition Using 3D Convolutional Network With Improved Attention Block 11

Anti-Interference Recognition Using 3D Convolutional Network...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Han, Jincheng Zuo, Zhengrong Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781510636385

The discrimination of interferences, especially artificial interferences, such as decoy, is crucial to improving the target's detection performance. The differences in the kinematics characteristics between the target and the decoys are the main foundation to classify targets and kinds of decoys. The kinematics characteristics of the target and decoy usually represented by their behavior patterns. In this paper, learned from the human behavior recognition methods, a method for infrared target and decoy recognition based on the behavior recognition network was proposed. Our method combines detection network (Faster-RCNN), association algorithm (Deep-Sort), Inflated 3D convolutional network (I3D) with long-range attention block to perform target and decoy behavior recognition. Interactions with surrounding objects and other objects contain important information towards understanding the behavior. Improving the non-local attention mechanism by aggregating channel-wise attention and trajectory attention, our proposal method enables the I3D network to efficiently capture relation features on any positions, time and channels, especially trajectory behavior features of the target and decoy, that improve the discriminative ability of the anti-interference behavior recognition network. Experiments show our proposed method has a better performance than the original non-local attention network, achieve a state-of-the-art.

关键词： anti-interference infrared image behavior recognition attention network

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A restoration method for the turbulent degraded images based on the salient edge selection and the L0 norm constraint 11

A restoration method for the turbulent degraded images based...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Feng, Yayuan Shi, Yu Sun, Dianjun Wang, Meixuan Wuhan Inst Technol HuBei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Wuhan Inst Technol HuBei Res Ctr Video Image & High Denit Project Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636347

At present, the restoration of turbulent degraded images is a worldwide problem in the fields of astronomical imaging. Atmospheric turbulence is the reason why images will be blurred, which gravely interferes the object of recognition and the detection of images. This paper presents a restoration method for the turbulent degraded images based on the image saliency edge selection and the L-0 norm constraint, which aims to recover sharp images from the turbulent degraded images. The proposed method imposes the L-0 norm sparse constraint on the latent image, and uses the method of split Bregman to solve the problem of optimization. To avoid the influence of the tiny details on the point spread function (P SF) , we use the image saliency algorithm to build a weighted model to select salient edges from the latent images. Based on the salient edge in the gradient domain, the proposed method establishes an estimation model of the point spread function. The calculation part of the point spread function is solved accurately by using the fast Fourier transform (FFT) in the frequency domain The proposed method uses the multi-scale pyramid strategy to alternatively solve the point spread function and the latent images, which can obtain the final accuracy of the point spread function.

关键词： Turbulent degraded images image saliency the salient edge sparse constraint

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Remote sensing image ship detection based on feature pyramid 11

Remote sensing image ship detection based on feature pyramid

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Zou, Lamei Li, Changfeng Yang, Weidong Zhou, Shiyang Nie, Shiwei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Peoples R China

ISBN: (纸本)9781510636385

We present a two-stage method for remote sensing image ship detection. The proposed approach efficiently detects ships in remote sensing images. Firstly, a light-weight classification network is used to classify different regions. In second stage, we design a detection framework to detect ships in sub-images, which are considered to contain object in the first stage. To solve the scale problems in object detection, our detection network is built on feature pyramid network, but we explicitly assign object into corresponding feature maps based on size. In our proposed framework, instead of using anchors, we predict object center point and the offsets to bounding box. The experiment results show that our proposed method has a good performance in terms of speed and accuracy.

关键词： remote sensing image object detection feature pyramid network feature pyramid network anchor-free

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