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An image-sequence compressing algorithm based on homography transformation for unmanned aerial vehicle

An image-sequence compressing algorithm based on homography ...

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International Symposium on Intelligence information processing and Trusted Computing

作者： Gong, Junbin Zheng, Chenlin Tian, Jinwen Wu, Dingxue Institute for Pattern Recognition and Artificial Intelligence National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology Wuhan 430074 China College of Computer Science and Technology Huanggang Normal University Huanggang 438000 China

ISBN: (纸本)9780769541969

Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorithm based on homography transformation was proposed. The image sequences were dynamically divided into framegroups according the data from airborne inertial navigation systems, and the intermediate frames in the same frame-group was bi-directionally predicted by the first-frame and the end-frame with homography transformation. The homography matrix was got approximately by the airborne inertial navigation systems firstly and then was accurately computed by fast multiple sub-areas template matching. At the end the first frame and the residual images of the intermediate frames of the same frame-group was merged into a big image and coded by JPEG2000 to generate fixed-size code streams. The experiment results show that the proposed algorithm was with high compression performance, low computational complexity and excellent capacity for code-size control and will has good prospect in engineer. © 2010 IEEE.

关键词： Image compression

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Design and implementation of knowledge base for target recognition in remote sensing images

Design and implementation of knowledge base for target recog...

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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Shaobin Cai, Chao Li, Erlang Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology State Key Laboratory for Multi-spectral Information Processing Technologies Wuhan 430074 China School of Life Science and Technology of Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424459346

In this paper, the knowledge modeling, architecture design and detailed implementation of an ontology-based knowledge base for target recognition in remote sensing images is presented. Knowledge base is a critical component of a large ground target recognition system which is a hybrid system that combines knowledge base with remote sensing image processing module. The knowledge base provides necessary knowledge for remote sensing image processing module, and remote sensing image processing module can perform multiple tasks with the support of the knowledge base. The existence of the knowledge base makes the whole recognition system more flexible and more generic than those systems without knowledge base. The effectiveness of the presented knowledge base shows the good prospect of the application of knowledge base in remote sensing image processing domain. ©2010 IEEE.

关键词： Knowledge based systems

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Sub-manifold distance based object recognition in clutter

Sub-manifold distance based object recognition in clutter

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International Conference on Natural Computation

作者： Zhou, Hua Cai, Chao Ding, Mingyue National Lab for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Imaging Processing and Intelligence Control Key Laboratory of Education Ministry School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424459612

Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manifold learning algorithms are developed for discovering intrinsical features. Selective visual attention mechanism provides tools to reduce computation cost and avoid the influence of clutter background. In this paper, we described a new object recognition method, the submanifold distance (SMD) algorithm, which is induced by the visual attention mechanism to provide complex object recognition. Experiments with airport remote sensing images illustrated that our proposed algorithm can recognize complex objects accurately, robustly and quickly. ©2010 IEEE.

关键词： Object recognition

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Thresholding Using Two-Dimensional Histogram Based on Local Entropy

Thresholding Using Two-Dimensional Histogram Based on Local ...

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International Asia Conference on Informatics in Control, Automation, and Robotics

作者： Zhao Cheng Tianxu Zhang Luxin Yan State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

ISBN: (纸本)9781424451920;9781424451937

Image Segmentation is a basal research in image processing domain. The outcome of segmentation has an immense effect on the posterior image processing. Focusing on gray-level image adaptive segmentation, in this paper, a new gray-level image segmentation algorithm using 2-D histogram thresholding based on local entropy (GLLE) is proposed. Some comparative experiments using typical image are implemented and the results are shown at the end of this paper. The results of comparative experiment demonstrate that the GLLE method can segment the target area much more intactly than the classical methods. Furthermore, the GLLE method also has the ability of anti-illumination.

关键词： Image Segmentation Local Entropy 2-D Histogram

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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International Conference on Mechatronics and Automation

作者： Wang, Yuanyu Li, Yuanzong Zhang, Tianxu Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan CO 030024 China College of Mechanical Engineering TaiYuan University of Technology TaiYuan CO 030024 China Institute for Pattern Recognition and A.I. State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology WuHan CO 430074 China

ISBN: (纸本)9781424451418

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments. © 2010 IEEE.

关键词： Dust

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A self-adaptive unscented Kalman filtering for underwater gravity aided navigation

A self-adaptive unscented Kalman filtering for underwater gr...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Lin Wu Jie Ma Jinwen Tian State Key Laboratory of Multi-spectral Information Processing Technologies Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424450367;9781424450374

In this paper, a self-adaptive unscented Kalman filtering for underwater gravity aided navigation is constructed. It is more accurate and far easier to implement than an extended Kalman filter. Then the novel navigation algorithm based on the self-adaptive unscented Kalman filter is explored. With this method submerged position fixes for autonomous underwater vehicle can be obtained from comparing gravity fields' measurements with gravity maps. Specifically, simulation results show that navigation errors can be reduced more effectively and efficiently by the presented algorithm.

关键词： Kalman filters Gravity Error correction Equations information filtering information filters Underwater vehicles Inertial navigation Remotely operated vehicles Laboratories

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Efficient systolic implementation of DFT using only the first-order moments

Efficient systolic implementation of DFT using only the firs...

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International Conference on intelligent Control and information processing (ICICIP)

作者： Jianguo Liu Zhenbing Liu Chao Pan State Key Laboratory for Multi-spectral Information Processing Technology Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

The Discrete Fourier transform (DFT) is an important tool in digital signal processing. In this paper, a novel approach to DFT is proposed. The computation of DFT is transformed to the computation of the first-order moments by the simple mathematical deductions. It is well known that the first-order moments can be computed efficiently using only additions. An efficient systolic array is designed to implement DFT. The comparison with the existing methods shows the advantages of our method. The approach is also applicable to DFT inverses.

关键词： Discrete Fourier transforms Arrays Clocks Algorithm design and analysis Complexity theory Signal processing algorithms

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I2T: Image parsing to text description

I2T: Image parsing to text description

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作者： Yao, Benjamin Z. Yang, Xiong Lin, Liang Lee, Mun Wai Zhu, Song-Chun Lotus Hill Institute Ezhou 436000 China Los Angeles CA 90095 United States Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Laboratory of Intelligent Information Processing Sun Yat-Sen University Guangzhou 510275 China ObjectVideo Inc. Reston VA 20191-1410 United States

In this paper, we present an image parsing to text description (I2T) framework that generates text descriptions of image and video content based on image understanding. The proposed I2T framework follows three steps: 1) input images (or video frames) are decomposed into their constituent visual patterns by an image parsing engine, in a spirit similar to parsing sentences in natural language;2) the image parsing results are converted into semantic representation in the form of Web ontology language (OWL), which enables seamless integration with general knowledge bases;and 3) a text generation engine converts the results from previous steps into semantically meaningful, human readable, and query-able text reports. The centerpiece of the I2T framework is an andor graph (AoG) visual knowledge representation, which provides a graphical representation serving as prior knowledge for representing diverse visual patterns and provides topdown hypotheses during the image parsing. The AoG embodies vocabularies of visual elements including primitives, parts, objects, scenes as well as a stochastic image grammar that specifies syntactic relations (i.e., compositional) and semantic relations (e.g., categorical, spatial, temporal, and functional) between these visual elements. Therefore, the AoG is a unified model of both categorical and symbolic representations of visual knowledge. The proposed I2T framework has two objectives. First, we use semiautomatic method to parse images from the Internet in order to build an AoG for visual knowledge representation. Our goal is to make the parsing process more and more automatic using the learned AoG model. Second, we use automatic methods to parse image/video in specific domains and generate text reports that are useful for real-world applications. In the case studies at the end of this paper, we demonstrate two automatic I2T systems: a maritime and urban scene video surveillance system and a real-time automatic driving scene understanding

关键词： Syntactics

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Second order morphology algorithm for IR small target enhancement in sea clutter background

Second order morphology algorithm for IR small target enhanc...

引用

International Conference on Optics Photonics and Energy Engineering (OPEE)

作者： Zhou Xiao Mou xingang Zhang guilin National Key Laboratory of Science and Technology On Multi-spectral Information Processing Technologies Institute For Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424452347;9781424452354;9781424452361

The framework of a second order morphology algorithm is proposed to enhance the dim small infrared target in sea clutter background with strong detector noise. First, a morphological filters bank is given. Each filter in the bank is designed to suppress its corresponding type texture. The second order process can be accomplished in two steps. The original infrared image is first filtered using each filter separately, and then the first order result is obtained by the combination of the filtered result images. The other step is essentially the same as the first one except the combination manner of the filtered result images. Second, the traditional SNR gain based evaluation of different small target enhancement algorithms is examined and a more reasonable method based on the distance between the target and the maximal background (DTMB) is described in this paper. Third, using the new evaluation method, the comparison is given by experiment through a sequence of infrared images with a dim small target. Experimental results demonstrate that the new proposed algorithm performs better in sea clutter background with strong detector noise than the traditional ones.

关键词： Morphology Filter bank Infrared detectors Nonlinear filters Background noise Infrared imaging Shape Working environment noise Optical filters Photonics

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Fast Marine Route Planning for UAV Using Improved Sparse A* Algorithm

Fast Marine Route Planning for UAV Using Improved Sparse A* ...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Xin Yang Ming-yue Ding Cheng-ping Zhou Institute for Pattern Recognition and Artificial Intelligence (IPRAI) Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China School of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Institute for Pattern Recognition and Artificial Intelligence (IPRAI) State Key Laboratory for Multispectral Information Processing Technologies Huazhong University of Science and Technology Wuhan Hubei China

This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.

关键词： Unmanned aerial vehicles Planning Synthetic aperture sonar Cost function Algorithm design and analysis Heuristic algorithms Leg

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