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2012 3rd International Conference on Digital Manufacturing and Automation, ICDMA 2012

作者： Hong, Hanyu Yu, Zhejun Zhang, Xiuhua Laboratory for Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan 430205 China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769547725

Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmented method can be used in a wide variety of billet scenes. According to high temperature and complex scene in the rolling line, we use an effective clustering and projection characteristics to determine the terminal condition of recursive segmentation. Then we can label character candidate regions in turn by this effective characteristics, and select the regions we want to achieve. The experiments show that this method makes full use of the characteristics of region and clustering. It can improve the quality of detection, and the detection result meets the need of practical application. © 2012 IEEE.

关键词： CCD cameras

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KICA Feature Extraction in Application to FNN based Image Registration

KICA Feature Extraction in Application to FNN based Image Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Anbang Xu Xin Jin Ping Guo R. Bie Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

In this paper, a novel image registration method is proposed. In the proposed method, kernel independent component analysis (KICA) is applied to extract features from the image sets, and these features are input vectors of feedforward neural networks (FNN). Neural network outputs are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments are performed between KICA based method and other six feature extraction based method: principal component analysis (PCA), independent component analysis (ICA), kernel principal component analysis (KPCA), the discrete cosine transform (DCT), Zernike moment and the complete isometric mapping (Isomap). The results show that the proposed method is much improved not only at accuracy but also remarkably at robust to noise.

关键词： Feature extraction Image registration Neural networks Discrete cosine transforms Independent component analysis Kernel Principal component analysis Feedforward neural networks Intelligent robots Registers

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Fast DCT-I, DCT-III, and DCT-IV via Moments

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Eurasip Journal on Applied Signal processing 2005年第12期2005卷 1902-1909页

作者： Liu, J.G. Liu, Y.Z. Wang, G.Y. Key Laboratory of Education Huazhong University of Science and Technology Ministry for Image Processing and Intelligence Control Wuhan 430074 China Department of Computer Science and Engineering China University of Geosciences Wuhan 430074 China Key Laboratory of State Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

This paper presents a novel approach to compute DCT-I, DCT-III, and DCT-IV. By using a modular mapping and truncating, DCTs are approximated by linear sums of discrete moments computed fast only through additions. This enables us to use computational techniques developed for computing moments to compute DCTs efficiently. We demonstrate this by applying our earlier systolic solution to this problem. The method can also be applied to multidimensional DCTs as well as their inverses. © 2005 Hindawi Publishing Corporation.

关键词： Method of moments

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Using machine vision for observation of phenomena during laser hybrid welding

Using machine vision for observation of phenomena during las...

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ICALEO 2006 - 25th International Congress on Applications of Laser and Electro-Optics

作者： Fellman, Anna Fennander, Henri Salminen, Antti Kyrki, Ville Lappeenranta University of Technology Department of Mechanical Engineering Laser Processing Laboratory Lappeenranta Finland Lappeenranta University of Technology Department of Information Technology Machine Vision and Pattern Recognition Research Group

Laser hybrid welding process is widely studied today, with many research groups trying to clarify the phenomena during the process. Many of the changes occurring during laser hybrid welding process can be observed with the help of high speed imaging. The high speed of the droplet formation and their movement to the melt pool set strict requirements for the imaging frequency in order to provide reliable information about the process. Because of large number of images produced by the high-speed (over 1000 Hz) imaging, it is time-consuming, error-prone, and frustrating to inspect the process manually, using only the human eyes for the observation. These problems could be alleviated by an automated system for the observation. This paper presents an off-line machine vision system created and used to study videos of CO2 laser-MAG hybrid welding experiments in which welding parameters were changed one at a time. The quality of the welds was observed by visual and macrographic examination. In the study, the regularity of pulse frequency of the arc and direction of filler material droplet flight were measured with the help of machine vision. Due to stable nature of keyhole welding part of the process, these characteristics show the stability of the whole welding process. It was found out that the automatic observation can be used if the quality of the images remains good enough. Experimentally, the inspection system provided information that the welding parameters have an effect on direction of droplet movement. This kind of automatic observation of changes in the process could provide a valuable tool for process optimization. The gathered information can be utilized in achieving an optimized process giving the best possible productivity.

关键词： Computer vision

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Electrically controlled terahertz band pass filter with liquid crystal

Electrically controlled terahertz band pass filter with liqu...

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Photonics and Optoelectronics Meetings (POEM) 2008: Terahertz Science and Technology

作者： Hui, Li Xinyu, Zhang Tianxu, Zhang Xubang, Shen Institute for Pattern Recognition and AI Multi-spectral information processing technologies National Science and Technology Laboratory Huazhong University of Science and Technology 1037 Luoyu Wuhan Hubei 430074 China

ISBN: (纸本)9780819475367

The paper presents an electrically controlled terahertz (THz) band pass filter with liquid crystal (LC). Considering dichroic filters theory, the filter of two-dimensional metallic photonic crystals is designed as a band pass filter in THz wave band. Electric field is used to change the direction of LC molecules, so that refraction index of LC is changed and correspondingly the peak transmissivity frequency of the THz band pass filter is changed finally. By this way mentioned above electrically controlled THz band-pass filter with LC can be got. The peak transmissivity frequency of the electrically controlled THz band-pass filter is tunable from 0.18THz to 0.19THz by changing electric field. © 2009 SPIE.

关键词： Bandpass filters

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Multi-Level Kernel Machine for Scene Image Classification

Multi-Level Kernel Machine for Scene Image Classification

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International Conference on Computational Intelligence and Security

作者： Junlin Hu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.

关键词： Kernel Computed tomography Transforms Computer vision Histograms Conferences Support vector machines

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Curve reconstruction from a set of dense scattered points using A* algorithm

Curve reconstruction from a set of dense scattered points us...

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MIPPR 2009 - Automatic Target recognition and Image Analysis: 6th International Symposium on Multispectral Image processing and pattern recognition

作者： Sun, Yangguang Zhou, Chengping Cai, Chao Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Multi-spectral Information Processing Technologies HUST Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China HUST Wuhan 430074 China

ISBN: (纸本)9780819478061

In this paper, a novel curve reconstruction method based on A* algorithm from a set of dense scattered points was proposed. Our method can not only reconstruct dense scattered points with single connected complicated form, but also solve the problem building the curve from the scattered points with close form and multiply connected form. Moreover, the influence of noise points on reconstructed curves is farther decreased, and the shape of dense scattered points will be well hold. Experimental results have demonstrated that the proposed method has the feasibility and robustness. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： pattern recognition

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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd International Conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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Image fusion algorithm using nonsubsampled contourlet transform

Image fusion algorithm using nonsubsampled contourlet transf...

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MIPPR 2007: Multispectral Image processing

作者： Yang, Xiao Zhiguo, Cao Kai, Wang Zhengxiang, Xu Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong Uni. of Sci. and Tech. Wuhan 430074 China

ISBN: (纸本)9780819469519

In this paper, a pixel-level image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) has been proposed. Compared with Contourlet Transform, NSCT is redundant, shift-invariant and more suitable for image fusion. Each image from different sensors could be decomposed into a low frequency image and a series of high frequency images of different directions by multi-sacle NSCT. For low and high frequency images, they are fused based on local-contrast enhancement and definition respectively. Finally, fused image is reconstructed from low and high frequency fused images. Experiment demonstrates that NSCT could preserve edge significantly and the fusion rule based on region segmentation performances well in local-contrast enhancement.

关键词： Image fusion

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Edge detection using image feature detector

Edge detection using image feature detector

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International Conference on Signal processing Proceedings (ICSP)

作者： Yang Xuan Liang Dequn Yang Haijun Laboratory of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China

A new edge detection operator based on image features is proposed, which analyzes edges in images for edge features in two dimensions. The local extreme of the operator is created at the edge location and a low value is created at the smooth region. Edges can be located by obtaining the local extreme and a threshold of the operator response. The detection operator is shown to be better than the Canny operator in terms of signal-to-noise ratio and edge location accuracy.

关键词： Image edge detection Computer vision Detectors Image analysis Laboratories Image processing pattern recognition pattern analysis Signal to noise ratio Filters

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