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2012 International Meeting on Opto-Electronics Engineering and Materials Research, OEMR 2012

作者： Luo, Jun Chen, Ying Kang, Shengwu Zhang, Xinyu Xie, Changsheng Zhang, Tianxu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei Province 430074 China College of Sciences Huazhong Agricultural University Wuhan Hubei Province 430070 China Hubei Province Fiber Inspection Bureau Wuhan Hubei Province 430060 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan Hubei Province 430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan Hubei Province 430074 China

ISBN: (纸本)9783037854884

We present the design and fabrication of metamaterials, and investigated the power transmission properties of the metamaterials in the frequency ranging from 1.04THz to 4.25THz. The measured results reveal a global maximum transmission peak at 2.52THz. We compared measured power transmission performances of four types of metamaterials microstructures, and analyzed their transmission differences. © (2012) Trans Tech Publications, Switzerland.

关键词： Metamaterials

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Patch-based Object Tracking via Locality-constrained Linear Coding

Patch-based Object Tracking via Locality-constrained Linear ...

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第35届中国控制会议

作者： Kunqi Gu Mingna Liu Tao Zhou Fanghui Liu Xiangjian He Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology Infrared Detection Technology Research and Development Center China Aerospace Science and Technology Corporation School of Computing and Communications University of Technology The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

In this paper,the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking ***,we extract local patches within a candidate and then utilize the LLC algorithm to encode these *** on these codes,we exploit pyramid max pooling strategy to generate a richer feature *** feature histogram which integrates holistic and part-based features can be more discriminative and ***,an occlusion handling strategy is utilized to make our tracker more ***,an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and *** tracking,target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes,and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking *** on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines.

关键词： object tracking LLC pyramid max-pooling local appearance model manifold ranking

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The investigation of terahertz metamaterials by normalized power transmission

The investigation of terahertz metamaterials by normalized p...

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2012 3rd International Conference on Applied Mechanics and Mechanical Engineering, ICAMME 2012

ISBN: (纸本)9783037855577

We present the design and fabrication of metamaterials, and investigated the normalized power transmission properties of four metamaterials at different frequency in terahertz regime. We compared the power transmission performances of four types of metamaterials microstructures, and analyzed their transmission differences. © (2013) Trans Tech Publications, Switzerland.

关键词： Power transmission

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Abundant Semantic information Module for Object Detection

Abundant Semantic Information Module for Object Detection

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Taida Wu Qishen Li Qiufeng Li Jun Gao School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.

关键词： Location awareness Fuses High performance computing Semantics Neural networks Object detection Detectors

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Low-light image enhancement model based on self-calibration illumination and color adjustment

Low-light image enhancement model based on self-calibration ...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution Image color analysis Convolution Brightness Lighting information filters

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Efficient parallel architecture for lifting-based two-dimensional discrete wavelet transform

Efficient parallel architecture for lifting-based two-dimens...

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IEEE International Workshop on VLSI Design and Video Technology, IWVDVT

作者： Cheng-Yi Xiong Jin-Wen Tian Jian Liu Institute of Pattem Recognition & Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China College of Electronic Information Engineering South Central University of Nationalities Wuhan China

ISBN: (纸本)0780390059

An efficient generic architecture for two-dimensional discrete wavelet transform (2-D DWT) with line-based method is proposed with using lifting scheme, in which the parallelism of four subbands transform in lifting-based 2-D DWT is exploited. The proposed architecture is designed to generate 4 subbands coefficients concurrently per clock cycle that :an perform a 1-level decomposition of a N/spl times/N image in approximately N/sup 2//4 working clock cycles, which has faster throughput rate but requires less hardware cost compared 10 the designs reported in previous literature.

关键词： Discrete wavelet transforms Parallel architectures Two dimensional displays Clocks Image coding Throughput Hardware Costs Low pass filters Matrix converters

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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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IEEE International Conference on Mechatronics and Automation

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

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.

关键词： Image edge detection Image restoration Degradation Atmospheric modeling Optical scattering Optical imaging

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Novel low power supply DC-coupled 1.25 Gb/s laser diode driver

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Guti Dianzixue Yanjiu Yu Jinzhan/Research and Progress of Solid State Electronics 2006年第4期26卷 498-503页

作者： Fu, Shengmeng Chen, Zhaoyang Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory for Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

Since the DC-coupled interface between the driver and the laser diode makes it impossible for the conventional drivers to work with low power supply, an output stage has been proposed. A novel APC can suppress the output average optical power and extinction ratio within ±0.3 dBm and ±0.4 dB(-40°C to 100°C), respectively. The initialization time is not more than 0.6 μs because the fast binary search algorithm is incorporated into the APC. The burst-on delay and burst-off delay are less than 5 ns and meet the requirement of PON system. The chip is fabricated in TSMC 0.8 μm BiCMOS process and occupies an area of 1.56 mm × 1.67 mm with a power consumption of 105 mW.

关键词： Diodes

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New intra prediction mode for interlaced video in AVS

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Gaojishu Tongxin/Chinese High Technology Letters 2007年第3期17卷 221-225页

作者： Chen, Liang Tan, Yihua Tian, Jinwen State Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

The technologies of intra prediction and MBAFF were introduced, and a new intra prediction mode based on the characteristics of spatial distribution in interlaced video was proposed. The spatial correlation of five luma intra prediction modes in AVS-P2 and the new mode were analyzed. From the analysis result, it can be concluded that the new mode can exploit the spatial correlation better and predict the samples more precisely than the existed ones. The experimental results showed that the average gain in peak signal to noise ratio was above 0.12dB and the average reduction in bit-rate was above 1.77%, so the proposed mode is an effective prediction mode for improvement of coding performance.

关键词： AVS Interlaced video Intra prediction Video coding

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Remote Sensing Image Object Detection Method with Feature Denoising Fusion Module

Remote Sensing Image Object Detection Method with Feature De...

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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Penghui Chen Qishen Li Qiufeng Li Zhongyu Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models.

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