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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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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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A Method of Airborne Infrared and Visible image Matching Based on HOG Feature 10

A Method of Airborne Infrared and Visible Image Matching Bas...

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

作者： Wang, Xue Zhou, Qing Liu, Qiang Qi, Shengxiang Sci & Technol Avion Integrat Lab Shanghai 200233 Peoples R China China Natl Aeronaut Radio Elect Res Inst Shanghai 200233 Peoples R China

ISBN: (纸本)9781510617223;9781510617216

In the all-time matching and navigation task, the aircraft applies real-time infrared images acquired by infrared imagery sensor to match the referenced visible image provided by the satellite for accurate location. However, the large difference between the infrared image and the visible image makes the task challenging. In this paper, for the sake of engineering application in the avionics system, we obtain real-time infrared images according to the flight trajectory, and then use them to match the referenced visible images. Furthermore, the HOG features are extracted respectively from real-time infrared images and referenced visible images to describe their feature similarity, for the purpose of accurate matching and localization. Experimental results demonstrate that our proposed method can not only realize the matching between airborne infrared and visible images, but also achieve high location accuracy, which shows good performance and robustness.

关键词： infrared image visible image image matching HOG

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Threshold-adaptive Canny Operator Based on Cross-zero Points 10

Threshold-adaptive Canny Operator Based on Cross-zero Points

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

作者： Liu, Boqi Zhang, Xiuhua Guo, Shengwei Xu, Wei Wuhan Inst Technol Lab Image Proc & Intelligent Control Wuhan 430205 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Canny edge detectionw is a technique to extract useful structural information from different vision objects and dramatically reduce the amount of data to be processed. It has been widely applied in various computer vision systems. There are two thresholds have to be settled before the edge is segregated from background. Usually, by the experience of developers, two static values are set as the thresholds([2]). In this paper, a novel automatic thresholding method is proposed. The relation between the thresholds and Cross-zero Points is analyzed, and an interpolation function is deduced to determine the thresholds. Comprehensive experimental results demonstrate the effectiveness of proposed method and advantageous for stable edge detection at changing illumination.

关键词： Canny operator threshold-adaptive edge detection image process

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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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Action recognition in depth video from RGB perspective: A knowledge transfer manner 10

Action recognition in depth video from RGB perspective: A kn...

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

作者： Chen, Jun Xiao, Yang Cao, Zhiguo Fang, Zhiwen Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Sch Automat Wuhan Hubei Peoples R China Hunan Univ Humanities Sci & Technol Sch Energy & Mech Elect Engn Loudi Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.

关键词： domain adaptation dynamic image action recognition deep learning

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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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A Novel Approach for Fire recognition Using Hybrid Features and Manifold Learning-Based Classifier 10

A Novel Approach for Fire Recognition Using Hybrid Features ...

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

作者： Zhu Rong Hu Xueying Tang Jiajun Hu Sheng Jiaxing Univ Sch Math & Informat Engn Jiaxing 314001 Peoples R China Henan Polytech Univ Sch Software Engn Jiaozuo Peoples R China Kunming Univ Sci & Technol Sch Comp Sci & Technol Kunming 650093 Yunnan Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several non overlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

关键词： Fire recognition flame/smoke detection block processing color model hybrid feature manifold learning-based classifier image manifold distance measurement

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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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