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Dim target detection method based on salient graph fusion 10

Dim target detection method based on salient graph fusion

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Hu Ruo-lan Shen Yi-yan Jiang Jun Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

关键词： dim target detection salient graph image fusion

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Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images 10

Gaussian mixture models-based ship target recognition algori...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Yao, Shoukui Qin, Xiaojuan Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.

关键词： Ship target recognition Gaussian mixture model invariant moment remote sensing infrared image

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image Compression Based on Orthogonal Balanced Multiwavelets with Symmetry/antisymmetry 10

Image Compression Based on Orthogonal Balanced Multiwavelets...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Zhang Liping Zhao Yi Changjiang Polytech Wuhan Hubei Peoples R China HuBei Presch Teacher Coll Wuhan Hubei Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Multiwavelets have orthogonality, compacted support and symmetry simultaneously, these properties are very important for signal processing. However, most of Multiwavelets require related prefilters. An approach to construction of symmetry/antisymmetry orthogonal filter is proposed and its corresponding balanced filter is constructed, no any prefilter is necessary. Experimental results prove its performance is superior to DGHM and CL multiwavelets, higher than Bi9/7.

关键词： image Compression Multiwavelet Balancing Coefficients Rearrangement

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Feature Extraction based on Extended Multi-Attribute Profiles and Sparse Autoencoder for Remote Sensing image Classification 10

Feature Extraction based on Extended Multi-Attribute Profile...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Teffahi, Hanane Yao, Hongxun Belabid, Nasreddine Chaib, Souleyman Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Algerian Space Agcy Algiers Algeria Beijing Univ Aeronaut & Astronaut Space Technol Applicat Sch Instrument Sci & Technol Beijing Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become a challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper proposes a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral images "KSC" and "Pavia university" datasets and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers, and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

关键词： Hyperspectral image multispectral image spatial feature extraction dimensionality reduction image classification deep learning (DL) sparse autoencoder (SAE) extend multi-attribute profiles (EMAP) support vector machine (SVM)

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Complex Background Suppression Using Global-local Registration Strategy for the Detection of Small-Moving Target On Moving Platform 10

Complex Background Suppression Using Global-local Registrati...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

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

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

Target detection is a very important and basic problem of computer vision and image processing. The most often case we meet in real world is a detection task for a moving-small target on moving platform. The commonly used methods, such as Registration-based suppression, can hardly achieve a desired result. To crack this hard nut, we introduce a Global-local registration based suppression method. Differ from the traditional ones, the proposed Global-local Registration Strategy consider both the global consistency and the local diversity of the background, obtain a better performance than normal background suppression methods. In this paper, we first discussed the features about the small-moving target detection on unstable platform. Then we introduced a new strategy and conducted an experiment to confirm its noisy stability. In the end, we confirmed the background suppression method based on global-local registration strategy has a better perform in moving target detection on moving platform.

关键词： image registration global optimize local rematch

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Infrared and visible images fusion based on visual saliency map and NSCT 10

Infrared and visible images fusion based on visual saliency ...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Chen, Yanfei Cao, Min Li, Wenxiang Wuhan Inst Technol Sch Elect & Informat Engn Hubei Key Lab Intelligent Robot Wuhan 430073 Hubei Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

A fusion algorithm of infrared and visible images based on visual saliency map (VSM) and nonsubsampled contourlet transform (NSCT) was proposed. Usually, the visual salient region of infrared image is directed towards the targets which interpret the most important information in the image. For the given registered infrared and visible images, firstly, the frequency-tuned (FT) saliency detection algorithm is used to calculate the visual saliency map of infrared and visible images. Then the size of each salient region is obtained by maximizing entropy. In order to capture the details of the infrared and visible images, the low and high frequency fusion coefficients of nonsubsampled contourlet transform (NSCT) are selected based on region saliency, region energy (RE) and region sharpness (RS). Four different data sets from TNO, Human Factors are employed, and experimental results indicate that the proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

关键词： image fusion visual saliency map (VSM) nonsubsampled contourlet transform (NSCT) infrared image

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Nano-focusing effect simulation of metal film with subwavelength pattern structures 10

Nano-focusing effect simulation of metal film with subwavele...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Wei, Dong Yu, Yue Li, Dapeng Wu, Yong Xin, Zhaowei Xie, Xingwang Zhang, Xinyu Wang, Haiwei Xie, Changsheng Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China CASC Shanghai Aerosp Control Technol Inst Infrared Detect Technol Res & Dev Ctr Shanghai 201100 Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

Surface plasmon polarizations (SPP) is a nano-scale photon control technology which can converge the spread of oscillation electron driven by incident light. In recent years, SPP has become an advanced research hotspot and has been studied more and more widely. The convergence effect of SPP has extensive applications, such as Schottky barrier detector in which the higher power hotspot, the lower signal-to-noise ratio. In this paper, studies have been done about the interaction of light and matter. Different geometric shapes have been simulated, which were obtained by graphic clipping. Via comparing the power of the hot spot and the minimum location on the transmittance line, we concluded the relationship of the interaction and the structure. It's found that every absorption peak corresponds a mode of LSPP spread. Therefore, we can design figure to control the spread of the SPP, and achieve fantastic goal. Finally, a typical figure with high power hotspot was given.

关键词： Surface plasmon polarizations Nano-focusing near-field electromagnetic

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Joint image Restoration and Location in Visual Navigation System 10

Joint Image Restoration and Location in Visual Navigation Sy...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Wu, Yuefeng Sang, Nong Lin, Wei Shao, Yuanjie Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

image location methods are the key technologies of visual navigation, most previous image location methods simply assume the ideal inputs without taking into account the real-world degradations (e.g. low resolution and blur). In view of such degradations, the conventional image location methods first perform image restoration and then match the restored image on the reference image. However, the defective output of the image restoration can affect the result of localization, by dealing with the restoration and location separately. In this paper, we present a joint image restoration and location (JRL) method, which utilizes the sparse representation prior to handle the challenging problem of low-quality image location. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the dictionary constructed from the reference image. By iteratively solving the image restoration in pursuit of the sparest representation, our method can achieve simultaneous restoration and location. Based on such a sparse representation prior, we demonstrate that the image restoration task and the location task can benefit greatly from each other. Extensive experiments on real scene images with Gaussian blur are carried out and our joint model outperforms the conventional methods of treating the two tasks independently.

关键词： image restoration image location sparse representation

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A Method of Distributed Avionics Data processing Based on SVM Classifier 10

A Method of Distributed Avionics Data Processing Based on SV...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Guo Hangyu Wang Jinyan Kang Minyang Xu Guojing Sci & Technol Avion Integrat Lab Shanghai 200233 Peoples R China China Natl Aeronaut Radio Elect Res Inst Shanghai 200233 Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Under the environment of system combat, in order to solve the problem on management and analysis of the massive heterogeneous data on multi-platform avionics system, this paper proposes a management solution which called avionics "resource cloud" based on big data technology, and designs an aided decision classifier based on SVM algorithm. We design an experiment with STK simulation, the result shows that this method has a high accuracy and a broad application prospect.

关键词： distributed avionics big data data processing assistant decision-making

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Different Focus image Fusion Based on Multi-level Scaling 10

Different Focus Image Fusion Based on Multi-level Scaling

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Qian, Ruiyan Lu, Zhenkun Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Based on the idea of iteration, a simple and effective method for multi-focus image fusion is proposed. Firstly, two images are processed by Gaussian convolution, and the blurred images are obtained. Then, the area decision of image blocks are obtained through the original image subtracting the blurred images at different blocks and different levels. According to the principle of consistency, the subjection of image blocks is obtained by the area decision of image blocks. The area partition map is obtained by the subjection map at different levels. Finally, the fused image is obtained through the method of coefficient weighting. The experimental results show that the image quality obtained by the proposed method is superior to the traditional fusion method.

关键词： image fusion Block iteration multi-focus

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