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IEEE International Conference on image processing

作者： Xin Gao Xiaoguang Chen Jia Cao Zirui Deng Chongjin Liu Jufu Feng Key Laboratory of Machine Perception MOE Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing China Institute of Image Processing and Pattern Recognition North-China University of Technology Beijing China

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.

关键词： Pixel Feature extraction image quality Fingerprint recognition Detectors Frequency estimation Software algorithms

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Research on Multi-level Attention-based Human Pose Estimation

Research on Multi-level Attention-based Human Pose Estimatio...

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

作者： Jun Gao Hua Huang QiShen Li ShaoQi Xia 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

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network.

关键词： Computational modeling High performance computing Pose estimation Lighting Feature extraction Cognition Sun

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Research on Monocular Depth Estimation Method based on Multi-Level Attention and Feature Fusion

Research on Monocular Depth Estimation Method based on Multi...

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

作者： Zhongyu Wu Hua Huang Qishen Li Penghui Chen 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

Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, attention-based models and encoder-decoder architectures have led to great improvements in monocular depth estimation. Typically, most of the previous methods used repeated simple up-sampling operations during decoding, which may not make full use of the potential properties of the features extracted by the encoder, and there are problems of inaccurate prediction of the edge and depth maximum region. We propose an attention-based feature fusion module for encoder and decoder. We treat the monocular depth estimation as a pixel-level optimization problem, where the coarsest encoder feature is used to initialize the pixel-level optimization, which is then refined to higher resolution by the proposed attentional feature fusion (AFF). We formulate the prediction problem as ordinal regression over the bin centers that discretize the continuous depth range. It predicts a correspondingly different distribution of bins based on different pictures and we predict bins at the coarsest level using global pooling and MLP layers. In the NYUV2 dataset, the proposed architecture improving original model by 2.5.% and 1.1%, in terms of Log10 and Absolute relative error, respectively.

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Game-theoretic route planning for team of UAVs

Game-theoretic route planning for team of UAVs

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ping Yan Ming-Yue Ding Cheng-Ping Zhou Institute of Pattern Recognition and Artificial Intelligence. Education Ministry Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Department of Weaponry Engineering Naval University of Engineering Wuhan China

ISBN: (纸本)0780384032

Unmanned air vehicles (UAVs) are identified as an integral part of future military forces. The coordinated route-planning problems of UAV team with various architectures are addressed in the framework of game theory. A two-stage route planner has been proposed, which combines various game models and the concept of evolutionary computation and is compatible with the cooperative/competitive nature envisioned for UAV team. Our route planner can handle different kinds of mission constraints in hierarchical style. Potential routes of each vehicle form their own sub-population, and evolve only in their own sub-population, while the cooperation and competition among UAVs are reflected by the definition of fitness function. Experimental results show the feasibility of generating the coordinated routes for UAV team using game theory methods.

关键词： Unmanned aerial vehicles Game theory Evolutionary computation Automotive engineering Time of arrival estimation pattern recognition Artificial intelligence Control engineering education Laboratories image processing

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Research on attention-based multiscale information fusion with the real-time instance segmentation method

Research on attention-based multiscale information fusion wi...

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

作者： Yifan He Hua Huang Qishen Li Guiwen Zhang 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

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network.

关键词： Interpolation Computer vision Convolution High performance computing Interference Object detection Real-time systems

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Experimental Comparison of Geometric, Arithmetic and Harmonic Means for EEG Event Related Potential Detection

Experimental Comparison of Geometric, Arithmetic and Harmoni...

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International Conference on Computational Intelligence and Security

作者： Jarno M.A. Tanskanen X.Z. Gao Jing Wang Ping Guo Jari A.K. Hyttinen Vassil S. Dimitrov Department of Biomedical Engineering Tampere University of Technology and BioMediTech Finland College of Information Engineering Shanghai Maritime University China Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China ATIPS Laboratory University of Calgary AB Canada

In this paper, we experimentally evaluate three different averaging methods for processing of electroencephalogram (EEG) event related potentials (ERPs) measured from scalp in response to repeated stimulus. In ERP applications, arithmetic mean (AM) is normally employed in processing the ERPs prior to ERP detection, whereas also other averaging methods might have beneficial properties. Fast ERP detection is essential, for example, in brain computer interfaces and during spine surgery. Thus, it is of interest to search for methods to aid in detecting ERPs with as few stimulus repetitions as possible. Here, noise reduction properties of AM, geometric mean (GM), and harmonic mean (HM) are demonstrated with simulations, and ERP processing by the three methods is illustrated by processing real visual evoked potentials (VEPs).

关键词： Electroencephalography Gaussian noise Educational institutions Electrodes Visualization Harmonic analysis

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Generalization properties of hyper-RKHS and its applications

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 6220-6257页

作者： Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A.K. Suykens Department of Electrical Engineering ESAT-STADIUS KU Leuven Leuven Belgium Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai China Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view. Algorithmically, we consider two regularized regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) endowed with hyper-RKHS, and further combine divide-and-conquer with Nyström approximation for scalability in large sample cases. This framework is general: the underlying kernel is learned from a broad class, and can be positive definite or not, which adapts to various requirements in kernel learning. Theoretically, we study the convergence behavior of regularized regression algorithms in hyper-RKHS and derive the learning rates, which goes beyond the classical analysis on RKHS due to the non-trivial independence of pairwise samples and the characterisation of hyper-RKHS. Experimentally, results on several benchmarks suggest that the employed framework is able to learn a general kernel function form an arbitrary similarity matrix, and thus achieves a satisfactory performance on classification tasks.

关键词： hyper-RKHS approximation theory kernel learning out-of-sample extensions

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An improved mapping method of buffer for line-based architecture of 2-D DWT

An improved mapping method of buffer for line-based architec...

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International Conference on Communications, Circuits and Systems (ICCCAS)

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

ISBN: (纸本)0780390156

The number of arithmetic units used in the one-dimensional (1D) discrete wavelet transform (DWT) is the main consideration for reducing the area of VLSI implementation of 1D DWT, while the size of intermediate memory used for data buffering is another dominate factor of effecting hardware complexity of VLSI implementation for two-dimensional (2D) DWT. In this paper, we exploit the essential relationship between the size of temporal buffer (TB) required in the line-based architecture for 2D DWT (LBA2DDWT) and the number of registers used in the 1D DWT module, and present an improved method of mapping the registers used in the 1D DWT to the TB required in LBA2DDWT. Comparison results with the other design reported in previous literature demonstrate that, the proposed mapping method can reduce efficiently the size of memory required in LBA2DDWT.

关键词： Discrete wavelet transforms Registers Two dimensional displays Very large scale integration Hardware Convolution Pipeline processing Arithmetic Signal analysis image processing

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Impulse noise removal using linear prediction model

Impulse noise removal using linear prediction model

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International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： I. Prudyus S. Voloshynovskiy Y. Rytsar T. Holotyak National University Lvivska Politechnika Lviv Ukraine Coordinated Science Laboratory College of Engineering University of Illinois Urbana IL USA Department of Image Processing and Pattern Recognition Institute of Physics and Mechanics National Academy of Sciences Lviv Ukraine

Noise removal is an important problem in many applications. In this paper a new two-step scheme of the decision-based impulse noise removal method by means of contaminated pixel detection is proposed and comparison with direct order statistic filtering is given. The proposed methods satisfy both objective and subjective image quality.

关键词： Predictive models Statistics Noise reduction Pixel Electronic mail image quality Nonlinear filters Filtering algorithms Data analysis Statistical analysis

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Text scanner with text detection technology on image sequences

Text scanner with text detection technology on image sequenc...

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International Conference on pattern recognition

作者： Keechul Jung Kwang In Kim T. Kurata M. Kourogi JungHyun Han Pattern Recognition and Image Processing Laboratory Michigan State University USA Artificial Intelligence Laboratory KAIST South Korea National Institute for Advanced Industrial Science and Technology Japan School of Electrical and Computer Engineering Sung Kyun Kwan University South Korea

ISBN: (纸本)076951695X

We propose a text scanner which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron. To provide enhanced resolution of the extracted text images, we perform the text detection process after generating a mosaic image in a fast and robust image registration method.

关键词： image sequences Layout image registration Cameras Parameter estimation Videos Pixel image processing Robustness Optical character recognition software

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