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检索条件"机构=Image Processing and Pattern Recognition Laboratory Beijing Normal University"

共 146 条记录，以下是11-20 订阅

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Two-dimensional PCA combined with PCA for neural network based image registration

引用

2nd International Conference on Natural Computation, ICNC 2006

作者： Xu, Anbang Jin, Xin Guo, Fing Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)3540459073

A novel image registration scheme is proposed. In the proposed scheme, two-dimensional principal component analysis (2DPCA) combined with principal component analysis (PCA) is used to extract features from the image sets and these features are fed into feedforward neural networks to provide translation, rotation and scaling parameters. Comparison experiments between 2DPCA combined with PCA based method and the other two former methods: discrete cosine transform (DCT) and Zernike moment, are performed. The results indicate that the proposed scheme is both accurate and remarkably robust to noise. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Principal component analysis

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Software-based non-invasive implementation of binocular vision

Software-based non-invasive implementation of binocular visi...

引用

International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Tong, Gao Xin, Zheng Qian, Yin Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9780769533360

In this paper, we present a software-based noninvasive system to implement binocular stereo vision with the help of polarized glass or other auxiliary equipments. For any application based on OpenGL, our system can give its binocular vision by displaying its original scene as projecting one frame from the left viewpoint and the next frame from the right viewpoint alternately to simulate the imaging on left eye and right eye. Focus and pupillary distance are two parameters in the implementation which we can set manually. By the way of making a new *** to substitute the old one to intercept the OpenGL API functions, we can change the projection matrix and modify the rendering flow non-invasively by overloading some OpenGL API functions without modifying the application's source code. © 2008 IEEE.

关键词： Auxiliary equipment

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Enlightening the relationship between distribution and regression fitting 2nd

Enlightening the relationship between distribution and regre...

引用

2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Yu, Hang Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9783319681207

Statistical distribution fitting and regression fitting are both classic methods to model data. There are slight connections and differences between them, as a result they outperform each other in different cases. A analysis model for processing natural data, say astronomical pulsar data in this paper, is proposed to improve data fitting method performance. Then the insight behind the comprehensive fitting model is given and discussed. © IFIP International Federation for Information processing 2017.

关键词： Data handling

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Sparse representation for multi-label image annotation

Sparse representation for multi-label image annotation

引用

2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9780769545844

image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label annotation algorithm is proposed, which is based on sparse representation theory and employs a multi-level decision method to deal with the multi-object classification problem. The experimental results show that the proposed algorithm can provide more promising results compared with the traditional classification based image annotation methods. © 2011 IEEE.

关键词： image analysis

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Multi-level kernel machine for scene image classification

Multi-level kernel machine for scene image classification

引用

2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Hu, Junlin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9780769545844

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. © 2011 IEEE.

关键词： Principal component analysis

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Weighting features before applying machine learning methods to pulsar search 2nd

Weighting features before applying machine learning methods ...

引用

2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Wang, Dayang Yin, Qian Wang, Hongfeng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China

ISBN: (纸本)9783319681207

In recent years, different Artificial Intelligence methods have been applied to pulsar search, such as Artificial Neural Network method, PEACE Sorting Algorithm, Real-time Classification method. In this paper, Weighting Feature method before applying machine learning (ML) was proposed. We give weight to each feature according to its ability to distinguish pulsar and non-pulsar candidates. The ability is determined by the separation degree of the distribution of pulsars and non-pulsars on particular feature. And then use the ML methods to classify different types of candidates. The results show that this method is significant. The accuracy of identifying pulsars and modeling time were both improved after weighting. © IFIP International Federation for Information processing 2017.

关键词： Pulsars

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image registration with regularized neural network

引用

13th International Conference on Neural Information processing, ICONIP 2006

作者： Xu, Anbang Guo, Fing Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)3540464816

In this paper, we propose a new method to improve the image registration accuracy in feedforward neural networks (FNN) based scheme. In the proposed method, Bayesian regularization is applied to improve the generalization capability of the FNN. The features extracted from the image sets by kernel independent component analysis (KICA) technique are input vectors of regularized FNN. The outputs of the neural network are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments are performed between FNN with regularization and without regularization under various conditions. The results show that the proposed method is much improved not only at accuracy but also remarkably at robust to noise. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Feedforward neural networks

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Texture segmentation based on neuronal activation degree of visual model

Texture segmentation based on neuronal activation degree of ...

引用

19th International Conference on Neural Information processing, ICONIP 2012

作者： Ma, Jin Duan, Fuqing Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9783642344992

In the study of object recognition, image texture segmentation has being a hot and difficult aspect in computer vision. Feature extraction and texture segmentation algorithm are two key steps in texture segmentation. An effective texture description is the important factor of texture segmentation. In this paper, the neuronal activation degree (NAD) of visual model is exploited as the texture description of image patches. By processing the length and direction of NAD, we develop an effective segmentation strategy. First, the length of the NAD are used to partition blank area and non-blank area, then the mark index of neuron is used, which is maximally activated to identify the label of each segment unit to get an initial segmentation. Finally, region merging steps is exerted to get a desired result. © 2012 Springer-Verlag.

关键词： image texture

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image recognition Based on Combined Filters with Pseudoinverse Learning Algorithm 15th

Image Recognition Based on Combined Filters with Pseudoinver...

引用

15th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Deng, Xiaodan Sun, Xiaoxuan Guo, Ping Yin, Qian Image Processing and Pattern Recognition Laboratory School of Systems Science Beijing Normal University Beijing100875 China Image Processing and Pattern Recognition Laboratory College of Information Science and Technology Beijing Normal University Beijing China

ISBN: (纸本)9783030198220

Deep convolution neural network (CNN) is one of the most popular Deep neural networks (DNN). It has won state-of-the-art performance in many computer vision tasks. The most used method to train DNN is Gradient descent-based algorithm such as Backpropagation. However, backpropagation algorithm usually has the problem of gradient vanishing or gradient explosion, and it relies on repeated iteration to get the optimal result. Moreover, with the need to learn many convolutional kernels, the traditional convolutional layer is the main computational bottleneck of deep CNNs. Consequently, the current deep CNN is inefficient on computing resource and computing time. To solve these problems, we proposed a method which combines Gabor kernel, random kernel and pseudoinverse kernel, incorporating with pseudoinverse learning (PIL) algorithm to speed up DNN training processing. With the multiple fixed convolution kernels and pseudoinverse learning algorithm, it is simple and efficient to use the proposed method. The performance of the proposed model is tested on MNIST and CIFAR-10 datasets without using GPU. Experimental results show that our model is better than existing benchmark methods in speed, at the same time it has the comparative recognition accuracy. © 2019, IFIP International Federation for Information processing.

关键词： image recognition

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Mallat fusion for multi-source remote sensing classification

Mallat fusion for multi-source remote sensing classification

引用

ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications

作者： Dongdong, Cao Qian, Yin Ping, Guo IEEE Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)0769525288

The fusion of multi-source remote sensing data is to offer improved accuracies in land cover classification. The conventional fusion methods such as HIS and PCA can not enhance information and simultaneously preserve high fidelity. Thus, the fused image is not preferable for classification. In this paper, the multi-source remote sensing data fusion based on Mallat algorithm for classification is proposed. The purpose of fusion is to create a new image that is more suitable for recognition. The topic focuses on the pyramid decomposition and choosing coefficients in the fusion process. The performance of proposed method is assessed by statistical methods and its effectiveness also testified by classification accuracies. © 2006 IEEE.

关键词： Remote sensing

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