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image representation and recognition based on directed complex network model

Image representation and recognition based on directed compl...

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8th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2013

作者： Chen, Ying Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei230039 China

ISBN: (纸本)9783642375019

image structure representation is a vital technique in the image recognition. A novel image representation and recognition method based on directed complex network is proposed in this paper. Firstly, the key points are extracted from an image as the nodes to construct an initial complete undirected complex network. Then, the k-nearest neighbor evolution method is designed to form a series of directed networks. At last, the feature descriptor of the image is constructed by concatenating the structure features of each directed network to finally achieve image recognition. Experimental results demonstrate that the proposed method outperforms the traditional methods in image recognition and can describe the structure of images more effectively. © Springer-Verlag Berlin Heidelberg 2013.

关键词： image recognition

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Spiking neural P systems for arithmetic operations

Spiking neural P systems for arithmetic operations

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6th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2011

作者： Zeng, Xiangxiang Song, Tao Pan, Linqiang Zhang, Xingyi Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9780769545141

Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes. © 2011 IEEE.

关键词： Encoding (symbols)

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Multi-class segmentation based on generalized fuzzy Gibbs random fields

Multi-class segmentation based on generalized fuzzy Gibbs ra...

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Proceedings: 2003 International Conference on image processing, ICIP-2003

作者： Lin, Yazhong Chen, Wufan Chan, Francis H.Y. Key Lab. for Med. Image Processing First Military Medical University Guangzhou China Department of Electrical Engineering University of Hong Kong Hong Kong Hong Kong

The model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered In this paper, two multi-class approaches, based on the model of Piecewise Fuzzy Gibbs Random Fields (PFGRF) and that of Generalized Fuzzy Gibbs Random Fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical "hard" one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.

关键词： image segmentation

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Evaluation of bodily-kinesthetic intelligence based on computer contextual method

Evaluation of bodily-kinesthetic intelligence based on compu...

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2013 International Conference on Sport Science and Computer Science (CCCS 2013)

作者： Qi Xia Wei Wang Jun Wang College of Education Science Nanjing Normal University Key Laboratory of Image Processing and Image Communication of Jiangsu Province Nanjing University of Posts and Telecommunications

There are two main types of tests to assess one's bodily-kinesthetic intelligence,which are writing tests and scenario ***,these two methods are subjective and time-consuming and not suitable for large-scale *** paper proposed a new assess method to assess bodily-kinesthetic intelligence based on the writing intelligence test *** method is designed with the computer virtual reality *** result shows the method can get better reliability and *** the evaluation process is easier to realize.

关键词： bodily-kinesthetic intelligence multiple intelligence computer contextual method

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Crowd instability analysis using velocity-field based social force model

Crowd instability analysis using velocity-field based social...

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IEEE Visual communications and image processing (VCIP)

作者： Jing Zhao Yi Xu Xiaokang Yang Qing Yan Institute of Image Communication and Information Processing Shanghai Key Laboratories of Digital Media Processing and Communication Shanghai Jiaotong University Shanghai China

This paper proposes a novel method to locate crowd behavior instability spatio-temporally using a velocity-field based social force model. Considering the impacts of velocity field on interaction force between individuals, we establish an improved social force model by introducing collision probability in view of velocity distribution. As compared with commonly- used social force model, which defines interaction force as a dependent variable of relative geometric (physical) position of the individuals, this improved model can provide a better prediction of interactions using the collision probability in a dynamic crowd. With spatio-temporal instability analysis, we can extract video clips with potential abnormality and as well locate region of interest where abnormality is likely to happen. The experimental results demonstrate that the proposed method can be applied to detection of abnormal events with high accuracy of instability estimation due to the velocity-field based social force model.

关键词： Force Computer vision image motion analysis Vectors Optical distortion Dynamics Predictive models

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Generative Compression for Face Video: A Hybrid Scheme

Generative Compression for Face Video: A Hybrid Scheme

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Tang, Anni Huang, Yan Ling, Jun Zhang, Zhiyu Zhang, Yiwei Xie, Rong Song, Li Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Ai Institute Shanghai Jiao Tong University MOE Key Lab of Artificial Intelligence China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

As the latest video coding standard, versatile video coding (VVC) has shown its ability in retaining pixel quality. To excavate more compression potential for video conference scenarios under ultra-low bitrate, this paper proposes a bitrate-adjustable hybrid compression scheme for face video. This hybrid scheme combines the pixel-level precise recovery capability of traditional coding with the generation capability of deep learning based on abridged information, where Pixel-wise Bi-Prediction, Low-Bitrate-FOM and Lossless keypoint Encoder collaborate to achieve PSNR up to 36.23 dB at a low bitrate of 1.47 KB/s. Without introducing any additional bi-trate, our method has a clear advantage over VVC under a completely fair comparative experiment, which proves the effectiveness of our proposed scheme. Moreover, our scheme can adapt to any existing encoder/configuration to deal with different encoding requirements, and the bitrate can be dynamically adjusted according to the network condition. © 2022 IEEE.

关键词： image compression

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A research of heart rate prediction model based on evolutionary neural network

A research of heart rate prediction model based on evolution...

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2011 IEEE International conference on Intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Xiao, Feng Yuchi, Ming Ding, Mingyue Jo, Jun Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Life College of Science and Technology Huazhong University of Science and Technology Wuhan China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9780769546230

Heart rate (HR) signal analysis is widely used in the medicine and medical research area. Physical activities (PA) are commonly recognized to greatly affect the changes of heart rate. A method of Evolutionary Neural Network - Neuro-evolution of Augmenting Topologies (NEAT) is used to build a PA-based HR predictor model. Through special coding, crossover and mutation operator, NEAT can implement network topology and connectivity weights evolution simultaneously. The common problem in evolutionary neural network, like competing conventions, how to protect the new innovation are effectively solved. The experimental results demonstrated the application potential of the approach. © 2011 IEEE.

关键词： Neural networks

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Human identification using body prior and generalized EMD

Human identification using body prior and generalized EMD

引用

IEEE International Conference on image processing

作者： Lianyang Ma Xiaokang Yang Yi Xu Jun Zhu Institute of Image Communication and Information Processing Shanghai Key Laboratories of Digital Media Processing and Communication Shanghai Jiaotong University Shanghai China

The general configuration of body is a valuable cue for human identification, which is ignored by the existing approaches. In this paper, we present an approach for human identification by using body prior and the generalized Earth Mover's Distance (EMD). The common knowledge that a pedestrian is composed of upper body and the lower one is employed as a body prior. To achieve more robust body segmentation, we pursue their boundary by inducing a logistic probability map, which is approximated based on minimizing its KL divergence to the posterior probability of the observed person image. Furthermore, we generalize EMD by assigning different weights to regions of body, which are learned through logistic regression to boost discriminative power for human identification. The experimental results show that both body prior and the generalized EMD facilitate performance on human identification.

关键词： Humans image segmentation Measurement Feature extraction image color analysis Conferences

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A NO-REFERENCE EVALUATION METRIC FOR LOW-LIGHT image ENHANCEMENT

A NO-REFERENCE EVALUATION METRIC FOR LOW-LIGHT IMAGE ENHANCE...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhang, Zicheng Sun, Wei Min, Xiongkuo Zhu, Wenhan Wang, Tao Lu, Wei Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (纸本)9781665438643

Low-light images, which are usually taken in dark or back-lighting conditions, are hard to perceive due to the low visibility and low contrast. To improve viewers' Quality of Experience (QoE) and support the application of vision-based systems, various low-light image enhancement algorithms (LIEAs) have been proposed to lighten low-light images. However, some LIEAs may amplify the hidden distortions in the dark like noise and even further, introduce new distortions such as structural damage, color shift, etc, which severely affect the quality of light-enhanced images and need to be evaluated quantificationally. However, in the literature, few measures are proposed to assess the quality of light-enhanced images. Therefore, in this paper, we develop a no-reference low-light image enhancement evaluation (NLIEE) metric to predict the quality of light-enhanced images. The image quality is mainly assessed from four key aspects: light enhancement, color comparison, noise measurement, and structure evaluation. The experiment results show that NLIEE achieves the best performance among the general no-reference image quality assessment (NR IQA) models and quality descriptors for light enhancement. © 2021 IEEE Computer Society. All rights reserved.

关键词： image enhancement

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Scale adaptive block target tracking based on multi-layer convolution features and Kernel correlation filter

Scale adaptive block target tracking based on multi-layer co...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Zhang, Ting Hu, Dong Zhang, Jing Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Target tracking

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