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Region-Based Simplification of Laser Scanned Meshes

Journal of Graphics Tools 2003年第2期8卷 41-50页

作者： Ye Jianhui[a] [a]Image Processing & Intelligent Control Key Laboratory of China Institute of Pattern Recognition & Artificial Int elligence Huazhong University of Science and Technology Wuhan 430074 PR China E-mail:

To simplify the mesh acquired from at hree-dimensional laser scanner, it is more important to keep the boundary and quality of the region of interest than of other regions. The algorithm must not be sensitive to noise introduced in practical applications. In this paper, we present a novel vertex merging mesh simplification algorithm based on region segmentation. The algorithm can be divided into two stages: segmentation and simplification. After the segmentation of the 3D color mesh into different regions, vertices are classed into a region-boundary vertex, which can only be merged into a region-boundary vertex in order to guarantee the completeness of the regions' boundary, and region-inner vertex. The iterative vertex merging is applied with a region-weighted error metric, which implements controllable simplifications. We demonstrate our method with several examples of a 3D color human head mesh.

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Incremental tensor discriminant analysis for image detection

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Advanced Materials Research 2013年 710卷 579-583页

作者： Yan, Tao Liu, Chang Zhao, Wei Dong College of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu 610106 China

ISBN: (纸本)9783037857205

In order to settle incremental learning and preserve the space information of images, this paper proposes an incremental tensor discriminant analysis for facial image detection. The proposed algorithm employs tensor representation to preserve the structure information and introduces the incremental learning to solve the on-line learning for new added samples. The experiments have shown that our method achieves better classification performance and reduces the computational cost effectively compared with other algorithms. © (2013) Trans Tech Publications, Switzerland.

关键词： Discriminant analysis

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Integer Haar wavelet for remote sensing image compression

Integer Haar wavelet for remote sensing image compression

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International Conference on Signal Processing Proceedings (ICSP)

作者： Wang Zhenhua Xu Hongbo Tian Yan Tian Jinwen Liu Jian Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Image Processing and Intelligent Control Huazhong University of science and Technology Wuhan China

ISBN: (纸本)0780374886

With the development of remote sensing technique, onboard data compression has become an urgent need and a lot of study has been directed toward the development of efficient techniques. In this paper, the construction approach of integer Haar is discussed briefly, and a simple image compression scheme based on the integer Haar wavelet transform and block DPCM is proposed. The scheme can be easily designed for data processing in real-time systems of remote sensing with parallel algorithms. Simulation experimental results demonstrate that the proposed approach is a efficient image compression method.

关键词： Image coding Remote sensing Wavelet transforms Frequency Data compression Image reconstruction pattern recognition Artificial intelligence Image processing intelligent control

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Multi-feature content-based product image retrieval based on region of main object

Multi-feature content-based product image retrieval based on...

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International Conference on Information, Communications and Signal Processing

作者： Lunshao Chai Honggang Zhang Zhen Qin Jie Yu Yonggang Qi Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Riverside Laboratory for Artificial Intelligence Research University of California Riverside Riverside CA USA

ISBN: (纸本)9781457700293

Content-based image retrieval (CBIR) has got an intense interest and seen considerable progress over the last decade. But most of the time it is only applied in laboratory. One important reason for this is the diversity of images. Different practical situations call for different taxonomy definitions of images, and lead to very different solutions. At present, and even in the foreseeable future, a general purpose CBIR system is not really possible. However, image search engines oriented at specific domains are feasible in technology and also have the actual demand. With the rapid development of electronic commerce, searching specific product by image has become one of the most attractive related research topics. In this paper, we propose a region-based method fit for the content-based retrieval of product images. The method focuses on two key issues: fast extraction of the main region, in which the product locates, as well as efficient shape and color features extraction. To show the validity of the proposed region-based method, compared experiments are carried out and illustrated on the PI 100 dataset.

关键词： Image color analysis Feature extraction Shape Histograms Image retrieval Image edge detection pattern recognition

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A fusion algorithm of remote sensing image based on discrete wavelet packet

A fusion algorithm of remote sensing image based on discrete...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hai-Hui Wang Jia-Xiong Peng Wei Wu State Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780378652

Image fusion refers to the techniques that integrate complementary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and the compute processing tasks. In this paper, a new image fusion algorithm based on wavelet packet transform to fuse multisensor images is presented. Discrete wavelet transform (DWT) can offer a more precise way for image analysis, than other multi-resolution analysis. It decomposes an image into low frequency band and high frequency band in different level, and it can also be reconstructed gradually in different level. But this method only decomposes low frequency band in a higher scale, so that it omits some useful details of the images. In this paper, we present a new image fusion algorithm. In the algorithm, we use discrete wavelet packet transform (DWPT) to decompose and reconstruct the images. When images are merged in wavelet packet space, different frequency ranges are processed differently. It can merge information from original images adequately and improve abilities of information analysis and feature extraction. This image fusion is performed at the pixel level. In this fusion algorithm, a feature-based fusion rule is used to combine original subimages and to form a pyramid for the fused image. Through merging remote sensing images from multi-sensor to a same object by applying method of wavelet packet analysis, we have obtained a fused picture. In this paper, mutual information is employed as a means of objective assessing image fusion performance. The experiment results show that this fusion algorithm, based on wavelet packet transform, is an effective approach in image fusion area.

关键词： Remote sensing Discrete wavelet transforms Wavelet packets Image fusion Frequency Image analysis Wavelet analysis Image reconstruction Image sensors Humans

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<i>L</i>(2,1)-Labeling of the Brick Product Graphs

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Journal of Applied Mathematics and Physics 2017年第8期5卷 1529-1536页

作者： Xiujun Zhang Hong Yang Hong Li School of Information Science and Engineering Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China

A k-L(2,1)-labeling for a graph G is a function such that whenever and whenever u and v are at distance two apart. The λ-number for G, denoted by λ(G), is the minimum k over all k-L(2,1)-labelings of G. In this paper, we show that for or 11, which confirms Conjecture 6.1 stated in [X. Li, V. Mak-Hau, S. Zhou, The L(2,1)-labelling problem for cubic Cayley graphs on dihedral groups, J. Comb. Optim. (2013) 25: 716-736] in the case when or 11. Moreover, we show that? if 1) either (mod 6), m is odd, r = 3, or 2) (mod 3), m is even (mod 2), r = 0.

关键词： Graph Labeling Brick Product Graph L((2 1)-Labeling Frequency Assignment Problem

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A robust cell tracking framework by fusing global and local optimization algorithms

A robust cell tracking framework by fusing global and local ...

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第28届中国控制与决策会议

作者： Shu Meng Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.

关键词： Multi-object tracking Bipartite graph Optimization Hierarchical tracking

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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A Novel Approach to Edge Detection of Color Image Based on Quaternion Fractional Directional Differentiation

A Novel Approach to Edge Detection of Color Image Based on Q...

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The 2011 International Conference on Automation and Robotics(ICAR 2011)

作者： Chaobang Gao Jiliu Zhou Fangnian Lang Qiang Pu Chang Liu College of Computer Science and Technology Chengdu University School of Computer Science Sichuan University Key Laboratory of Pattern Recognition and Intelligent Information Processing

In this paper, we denote a color image by a quaternion function, then find edge points by solving the maximum of quaternion fractional directional differentiation(QFDD)'s norm. This method is called edge detection based on QFDD. Experiments indicate that the method has special advantages. Comparing with Canny, LOG, Sobel, and general fractional differentiation, we discover that QFDD has fewer false negatives in the textured regions and is also better at detecting edges which are partially defined by texture, which means we will obtain better results in the interesting regions by QFDD and these results are more consistent with the characteristics of human visual system.

关键词： quaternion fractional differentiation QFDD norm similarity

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Sequence-level training for non-autoregressive neural machine translation

arXiv

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arXiv 2021年

作者： Shao, Chenze Feng, Yang Zhang, Jinchao Meng, Fandong Zhou, Jie Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Pattern Recognition Center WeChat AI Tencent Inc

In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregressive mechanism leads to high translation latency of the NMT and restricts its low-latency applications. Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously. Nevertheless, NAT still takes the word-level cross-entropy loss as the training objective, which is not optimal because the output of NAT cannot be properly evaluated due to the multimodality problem. In this article, we propose using sequence-level training objectives to train NAT models, which evaluate the NAT outputs as a whole and correlates well with the real translation quality. Firstly, we propose training NAT models to optimize sequence-level evaluation metrics (e.g., BLEU) based on several novel reinforcement algorithms customized for NAT, which outperforms the conventional method by reducing the variance of gradient estimation. Secondly, we introduce a novel training objective for NAT models, which aims to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. The BoN training objective is differentiable and can be calculated efficiently without doing any approximations. Finally, we apply a three-stage training strategy to combine these two methods to train the NAT model. We validate our approach on four translation tasks (WMT14 En↔De, WMT16 En↔Ro), which shows that our approach largely outperforms NAT baselines and achieves remarkable performance on all translation tasks. The source code is available at https://***/ictnlp/Seq-NAT. Copyright © 2021, The Authors. All rights reserved.

关键词： Neural machine translation

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