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WRI International Conference on communications and Mobile Computing, CMC

作者： Huaibin Huang Dong Hu key Laboratory of image processing and image communications of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China

An efficient fast inter mode decision algorithm for the baseline H.264/AVC video coding is proposed in this paper. The main idea of the algorithm is to reduce the number of candidate modes for rate distortion optimization (RDO) by using early mode detection in four different levels, viz. SKIP mode detection, type detection at macroblock-level, type detection at submacroblock-level and intra modes detection. In addition, the correlation of cost of current MB and the one of previous frame, as well as the cost's monotonous property are both used to help the mode decision. Compared to JM8.6 reference software, our algorithm can save encoding time 71.743% on average with negligible PSNR loss, which is very helpful for H.264/AVC inter frame. encoding.

关键词： Automatic voltage control Costs image coding Encoding Video coding Rate-distortion Video sequences Mobile communication Mobile computing Telecommunication computing

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A comparative study of radial basis function neural networks in dynamic clustering algorithm

A comparative study of radial basis function neural networks...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhou, Peng Li, Dehua Wu, Hong Zeng, Jun Chen, Feng State Key Lab. for Image Processing and Intelligent Control Institute for Pattern Recognition and Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478078

This paper developed two learning procedure, respectively, based on the orthogonal least squares (OLS) method and the "Innovation- Contribution" criterion (ICc) proposed newly. The orthogonal use of the stepwise-regression algorithm of the ICc mages the model structure independent of the selected term sequence and reduces the cluster region further as compared with orthogonal least squares (OLS). as the Bayesian information criteria (BIC) method is incorporate into the clustering process of the ICc, except for the widths of Gaussian functions, it has no other parameter that need tuning ,but the user is required to specify the tolerance ρ, which is relevant to noises and will be difficult to implement in the real system, for the OLS algorithm. The two algorithms are employed to the Radial Basis Function Neural Networks (RBFNN) to compare its performance for different noise nonlinear dynamic systems. Experimental results show that they provide an efficient approximation to the required results for fitting models, but the clustering procedures of the ICc is substantially better solutions than does the OLS. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Radial basis function networks

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Change detection in SAR image based on multisacle product and PCA

Change detection in SAR image based on multisacle product an...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Wang, Guiting Zhang, Fengyu Jiao, Licheng Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper, we present a new method of change detection in SAR images based on multiscale product of wavelet transform and PCA algorithm. This method applied multiscale product of wavelet transform, in order to avoid the affect of speckle noise in SAR, enhance the changed information and weaken the effect of noise. The algorithm of PCA is used to merge every scale after the process of multiscale product. To show the effectiveness of the proposed technique, experiments are conducted on two SAR datasets. Results are compared with two methods which based on wavelet transform and PCA, and it is proved to be superior. ©2009 IEEE.

关键词： Synthetic aperture radar

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Research on auto focusing algorithm of microscopic imaging system

Research on auto focusing algorithm of microscopic imaging s...

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2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022

作者： Lv, Meini Yang, Qiuhui Ma, Wenjing Huang, Zeqiong College of Wuzhou Wuzhou China Wuzhou University Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

In the intelligent microscopic imaging system, the focusing evaluation function is one of the important core links in the automatic focusing system. In order to solve the problem that the focusing curve loses the characteristics of the ideal curve caused by illumination change, an automatic focusing algorithm based on Robert and wavelet transform is proposed in this paper. The algorithm extracts the maximum value of the high-frequency component wavelet coefficients decomposed by the wavelet transform, and uses the extracted wavelet coefficients to weight the Robert evaluation value coefficients, increases the sensitivity factor of the algorithm, and makes an experimental analysis with five traditional focusing functions under the condition of different illumination intensity to verify the feasibility of the algorithm. The experimental results show that the algorithm can overcome the change of illumination, has strong anti noise, sensitivity and stability, and is suitable for the fine focusing process in the microscopic imaging system. © 2022 IEEE.

关键词： Imaging systems

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Yang, Jinhai Yang, Hua Shanghai Jiao Tong University Institution of Image Communication and Network Engineering China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

ISBN: (纸本)9781728163277

Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse labels but target fine-grained recognition among novel classes. As coarse supervision tends to eliminate feature discrimination among underlying sub-classes, existing methods commonly utilize self-supervision as a complement to explore intra-class variation. However, current methods suffer from an intrinsic conflict between contrastive learning and coarse supervision. In this paper, we locate the root cause of the intrinsic conflict. Then, we resolve it by exploiting the similarity among augmented views while ignoring the unreasonable constraint between negative pairs. Besides, we decouple contrastive learning and coarse supervision into parallel branches to better regularize the latent space. Albeit simple, our approach consistently outperforms state-of-the-art methods across different benchmarks. © 2023 IEEE.

关键词： cross-granularity transfer Few-shot learning generalization image classification

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Road extraction in remote sensing images based on nonsubsampled contourlet transform

Road extraction in remote sensing images based on nonsubsamp...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zhong, Hua Feng, Yingtao Jiao, Licheng Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

A new method for automated road extraction in remote sensing images is proposed based on Nonsubsampled Contourlet Transform (NSCT). Due to the advantages of multi-scale, multi-direction and translation invariance, NSCT is much efficient for the capture of linear singularity of road target. A pixel-wised feature extraction algorithm is given first based on the analysis of the NSCT coefficient responses for the road target. Final results can be achieved by the simple fuzzy C-means clustering and the postprocessing steps. Experimental results demonstrate that our method outperforms other methods such as Gabor wavelet in accuracy, localization and false detection. ©2009 IEEE.

关键词： image enhancement

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An unsupervised change detection based on clustering combined with multiscale and region growing

An unsupervised change detection based on clustering combine...

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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Zhang, Xiaohua Wang, Le Jiao, L.C. Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424494040

In this paper, a novel approach is proposed for unsupervised change detection of multitemporal remote sensing images. The proposed method is able to produce the change detection result on the difference image without a priori assumptions .Firstly, the difference image which is acquired from multitemporal images. Mean shift algorithm is used to reduce noise of difference image and fake change. Then stationary wavelet transform (SWT) is used to extract feature vector of each pixel .The final change detection map is achieved by k-means clustering combined with region growing. The comparisons with the state-of-the-art change detection methods are provided. ©2011 IEEE.

关键词： Change detection

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Improved SAI Method using Non-local Spatial Constraint for image Interpolation

Improved SAI Method using Non-local Spatial Constraint for I...

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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Jueping Bian Zongliang Gan Mengcheng Zhang Xiuchang Zhu Jiangsu province key lab on image processing & image communication NUPT Nanjing China

This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which is called non-local property. We can use this non-local strategy to improve the interpolation quality by better estimating the model parameters and Lagrangian multiplier. There are two steps in our method. In the first step, similar patches of the given block are found in the initialized high resolution image, and the model parameters can be determined properly using the expanded piecewise auto regression (PAR) model and non-local spatial constraint. In the second step, the self-similarity of patches across the high and low resolution images is exploited to solve the Lagrangian multiplier λ, thus to make the data estimation robust. Experiments indicate that the improved method can achieve good results both subjectively and objectively.

关键词： image interpolation the SAI algorithm non-local self-similarity

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Vehicle color recognition using monocular camera

Vehicle color recognition using monocular camera

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2011 International Conference on Wireless communications and Signal processing, WCSP 2011

作者： Yang, Mengjie Han, Guang Li, Xiaofei Zhu, Xiuchang Li, Liang Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing Univ. of PandT Nanjing China Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Education Nanjing Univ. of PandT Nanjing China

ISBN: (纸本)9781457710100

This paper introduces a novel moving vehicle color recognition method. By processing videos recorded by monocular camera on traffic, we get single color image of vehicles which are crossed the traffic in any direction. First, paper employed a H-S two dimension histogram method to detect the color of the vehicles, and distinguish red, yellow, green and blue four color vehicles. Due to deal with the vehicles that do not satisfy above 4 colors, Regional Color Judgment method is proposed. This method consists of the following five steps: Firstly, position the color detection region by identifying the travelling direction of the vehicle. Then pick out the candidate color extraction region used to vehicle recognition. At last extract the color of the candidate region and match the color with the templates we predefined, the color of the best fit template is the color of the vehicle. © 2011 IEEE.

关键词： Color

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Super-resolution based on two dictionary-pairs

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Sensors and Transducers 2013年第10期157卷 289-294页

作者： Li, Xin Zhu, Xiuchang Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

For learning-based super-resolution reconstruction, the selection and training of dictionary play an important role in improving image reconstruction quality. A super-resolution algorithm based on two dictionary-pairs is proposed in this paper. This algorithm selects image's high- and mid-frequency components as the features of high- and low-resolution patches respectively, and gets the first dictionary-pair, i.e. joint-basic high/low-resolution dictionary-pair, by means of joint training. Then it calculates the difference between original high/mid-frequency components and reconstructed high/mid-frequency components with the first dictionary-pair, and composes the second dictionary-pair, i.e. residual high- and low-resolution dictionary-pair. During super-resolution reconstruction, the high-frequency component and residual high-frequency component of low-resolution image are reconstructed respectively with the above two dictionary-pairs. The experiment results show that, the subjective and objective reconstruction quality could be effectively improved by our proposed algorithm. © 2013 IFSA.

关键词： image reconstruction

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