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Selecting good regions to deblur via relative total variation 10

Selecting good regions to deblur via relative total variatio...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Li, Lerenhan Yan, Hao Fan, Zhihua Zheng, Hanqing Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China Shanghai Inst Space Control Technol Infrared Detect Technol Res & Dev Ctr CASC Shanghai 200233 Peoples R China

ISBN: (纸本)9781510617223;9781510617216

image deblurring is to estimate the blur kernel and to restore the latent image. It is usually divided into two stage, including kernel estimation and image restoration. In kernel estimation, selecting a good region that contains structure information is helpful to the accuracy of estimated kernel. Good region to deblur is usually expert-chosen or in a trial-and error way. In this paper, we apply a metric named relative total variation (RTV) to discriminate the structure regions from smooth and texture. Given a blurry image, we first calculate the RTV of each pixel to determine whether it is the pixel in structure region, after which, we sample the image in an overlapping way. At last, the sampled region that contains the most structure pixels is the best region to deblur. Both qualitative and quantitative experiments show that our proposed method can help to estimate the kernel accurately.

关键词： image Deblurring Blind Deconvolution Region Selection Relative Total Variation

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Method of Segmenting River from Remote Sensing image 10

Method of Segmenting River from Remote Sensing Image

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Tang, Qingyun Zhang, Jun Zhang, Daimeng Liu, Xiaomao Tian, Jinwen HUST Sch Automat State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Hubei Peoples R China Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA HUST Sch Math & Stat Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

this paper presents a method of segment the river area in remote sensing images. the spectral distribution of the river area in the image is relatively uniform, and the overall gray level is dark, And the spectrum is evenly distributed regardless of direction, but land area spectral information is very messy, most of the land in the regional spectral distribution is not uniform, maybe some land area spectral distribution is more uniform, but has a certain direction, this paper according to these characteristics, using the cross-type template, the regional variance is used as the regional texture characteristic to obtain the adaptive threshold to obtain the adaptive binary graph the river is usually a connected water, only a large enough area to determine the river, so the use of binary image marking algorithm to obtain the largest connected area, marked as a river. this paper presents the method of river segmentation. Experiments show that the river segmentation is suitable for remote sensing images with relatively large river regions.

关键词： River extraction Remote Sensing image Adaptive threshold Binary image region labeling algorithm

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Multi-Modal image Registration via Depth information based on Point Set Matching 10

Multi-Modal Image Registration via Depth information based o...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Sun, Bin Yang, Qi Hu, Kai Bai, Honglin Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781510617223;9781510617216

image registration is an important pre-processing operation to perform multi-modal joint analysis correctly. However, registration of images captured by different sensors is a very challenging problem due to the apparent differences of scenes. Traditional Coherent Point Drift method (CPD) is a global registration approach, which strongly relies on the extracted features. In the case of infrared and visible images, registration methods based on edges or points are inappropriate since those features might be significantly different. Fortunately, depth information is more robust feature for multi-modal image pairs. In this paper, we propose an algorithm based on Canny to extract edge of objects. And the regions of interest (ROI) is obtained by depth maps of image pairs in which common features usually successfully implemented by point set registration. Experimental results on real world data demonstrate the effectiveness of the proposed approach, which is superior to the traditional CPD algorithm for multi-modal image registration.

关键词： image registration point set matching depth information

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Learning deep features with adaptive triplet loss for person reidentification 10

Learning deep features with adaptive triplet loss for person...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Li, Zhiqiang Sang, Nong Chen, Kezhou Gao, Changxin Wang, Ruolin Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Wuhan Univ Sch Civil Engn Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. the experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

关键词： Deep learning part-based person reidentification (re-id)

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A Framework for Farmland Parcels Extraction Based on image Classification 10

A Framework for Farmland Parcels Extraction Based on Image C...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Liu, Guoying Ge, Wenying Song, Xu Zhao, Hongdan Anyang Normal Univ Dept Comp & Informat Engn 436 Xiange Rd Anyang 455000 Peoples R China

ISBN: (纸本)9781510617223;9781510617216

It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.

关键词： image classification Gabor filter semantic link farmland parcel extraction

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A fast non-local means algorithm based on integral image and reconstructed similar kernel 10

A fast non-local means algorithm based on integral image and...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Lin, Zheng Song, Enmin Huazhong Univ Sci & Technol Sch Comp & Technol Wuhan Hubei Peoples R China Wenzhou Med Univ Sch Informat & Engn Wenzhou Peoples R China

ISBN: (纸本)9781510617223;9781510617216

image denoising is one of the essential methods in digital image processing. the non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to-Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.

关键词： image denoising non-local means reconstruction of similar kernel integral image

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Supervised Guiding Long-Short Term Memory for image Caption Generation based on Object Classes 10

Supervised Guiding Long-Short Term Memory for Image Caption ...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Wang, Jian Cao, Zhiguo Xiao, Yang Qi, Xinyuan Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

the present models of image caption generation have the problems of image visual semantic information attenuation and errors in guidance information. In order to solve these problems, we propose a supervised guiding Long Short Term Memory model based on object classes, named S-gLSTM for short. It uses the object detection results from R-FCN as supervisory information with high confidence, and updates the guidance word set by judging whether the last output matches the supervisory information. S-gLSTM learns how to extract the current interested information from the image visual se-mantic information based on guidance word set. the interested information is fed into the S-gLSTM at each iteration as guidance information, to guide the caption generation. To acquire the text-related visual semantic information, the S-gLSTM fine-tunes the weights of the network through the back-propagation of the guiding loss. Complementing guidance information at each iteration solves the problem of visual semantic information attenuation in the traditional LSTM model. Besides, the supervised guidance information in our model can reduce the impact of the mismatched words on the caption generation. We test our model on MSCOCO2014 dataset, and obtain better performance than the state-ofthe-art models.

关键词： image caption generation Supervised guiding LSTM Object detection Text-related

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Smoke regions extraction based on two steps segmentation and motion detection in early fire 10

Smoke regions extraction based on two steps segmentation and...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Jian, Wenlin Wu, Kaizhi Yu, Zirong Chen, Lijuan Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. the Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. the proposed method was tested on 6 test videos with smoke. the experimental results show the effectiveness of our proposed method over visual observation.

关键词： Video-based Smoke Detection image enhancement Two steps segmentation ViBe motion detection

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Deep learning based hand gesture recognition in complex scenes 10

Deep learning based hand gesture recognition in complex scen...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Ni, Zihan Sang, Nong Tan, Cheng Huazhong Univ Sci & Technol Minist Educ Image Proc & Intelligent Control Key Lab Sch Automat Wuhan 430074 Hubei Peoples R China ContextLogic Inc San Francisco CA 94104 USA

ISBN: (纸本)9781510617223;9781510617216

Recently, region-based convolutional neural networks(R-CNNs) have achieved significant success in the field of object detection, but their accuracy is not too high for small objects and similar objects, such as the gestures. To solve this problem, we present an online hard example testing(OHET) technology to evaluate the confidence of the R-CNNs' outputs, and regard those outputs with low confidence as hard examples. In this paper, we proposed a cascaded networks to recognize the gestures. Firstly, we use the region-based fully convolutional neural network(R-FCN), which is capable of the detection for small object, to detect the gestures, and then use the OHET to select the hard examples. To enhance the accuracy of the gesture recognition, we re-classify the hard examples through VGG-19 classification network to obtain the final output of the gesture recognition system. through the contrast experiments with other methods, we can see that the cascaded networks combined with the OHET reached to the state-of-the-art results of 99.3% mAP on small and similar gestures in complex scenes.

关键词： Gesture recognition Object Detection Region-Based Fully Convolutional Neural Networks

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Weighted Least Square Method for Epipolar Rectification in Semi-calibrated image 10

Weighted Least Square Method for Epipolar Rectification in S...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - pattern recognition and Computer Vision

作者： Zhu, Guojia Zhou, Huabing Tao, Yiwei Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Hubei Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

the traditional method for dealing with the problem of epipolar rectification in the semi-calibrated case is to use RANdom SAmple Consensus(RANSAC), which could not get a correct parameter when exist serious mismatch points. So the weighted least square method is proposed to solve this problem. First, extracting Scale Invariant Feature Transform(SIFT) and conducting initial feature matching for image pairs. Next, according to the internal geometric relations of corresponding points, transforming the problem into a maximum likelihood estimate problem. And then, each pair of corresponding points is given weight, and the weight is regarded as a latent variable to stand for the precision of correct matching. Finally, weighted least square method and Expectation Maximization(EM) algorithm are used to estimate the latent variable and uncalibrated parameters. Experimental results show that propo-sed method could not only keep rectified precision high, but also has slighter image morphing and faster rectified velocity than state-of-the-art algorithms.

关键词： Epipolar rectification semi-calibrated latent variable weighted least square method Expectation Maximization(EM) algorithm

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