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2nd International Conference on pattern recognition Applications and Methods, ICPRAM 2013

作者： Tang, Qi Yang, Xin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

ISBN: (纸本)9789898565419

In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new descriptor is computationally efficient and induces a permutation property that guarantees invariance at the matching stage. Also, it is insensitive to small shape perturbations and mesh resolution. The retrieval performance on several 3D databases shows that the DBS provides state-of-art discrimination over a broad and heterogeneous set of shape categories.

关键词： Probability density function

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Combining Attention Mechanism and Local-Global Features Association Network for Vehicle Re-identification 11

Combining Attention Mechanism and Local-Global Features Asso...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Li, Cai-Xia Du, Xia Wu, Yun Wang, Da-Han Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Vehicle re-identification (Re-ID) aims to retrieve the target vehicle from a large dataset composed of vehicle images captured by multiple cameras. Most vehicles are difficult to recognize in the environment of low resolution, occlusion, and viewpoint change, which brings challenges to vehicle Re-ID. Existing work usually uses additional attribute information to distinguish different vehicles, such as color, viewpoint, and model. However, this requires expensive manual annotation. Therefore, we propose a three-branch network based on attention mechanism and local-global feature association (AM-LGFA) to improve the accuracy of vehicle Re-ID. In the global branch, the global features of the vehicle are extracted. A multi-scale channel attention module is introduced into the attention branch to suppress irrelevant information and extract important channel features. The features extracted from the backbone are divided into different stripe features in the horizontal direction in the local branch. Then connect each stripe feature with the global information to enhance the context between features. Finally, the features extracted from the three branches are concatenated as the feature representation of the test phase. The experimental results show that the features extracted by the AM-LGFA network are complementary. The effectiveness of this method is verified on two challenging public datasets, VehicleID and VeRi-776. © 2022 ACM.

关键词： Vehicles

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A Lightweight Brain Tumor Segmentation Network Based on 3D Inverted Residual Modules 11

A Lightweight Brain Tumor Segmentation Network Based on 3D I...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Liu, Yuchao Du, Xia Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated segmentation methods for brain tumors include 2D and 3D convolution networks. The 3D networks give better results but lead to a significant increase in parameters and computational cost. In this paper, we propose a lightweight brain tumor segmentation network composed of 3D inverted residual modules, which can significantly reduce the computational complexity of 3D models. Based on a lightweight depthwise separable convolution, our 3D inverted residual module extracts high-dimensional brain tumor features through an intermediate expansion layer, thus improving performance. On the brain tumor dataset BraTS 2018, our network achieves dice scores of 80.8%, 90.7%, and 84.3% (for ET, WT, and TC, respectively) with only 0.68M parameters and 51.46G FLOPs. The results show that our method can significantly reduce the complexity of the 3D model and achieve very competitive performance. © 2022 ACM.

关键词： Semantics

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A fast quantum mechanics based contour extraction algorithm

A fast quantum mechanics based contour extraction algorithm

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Medical Imaging 2009 - image processing

作者： Lan, Tian Sun, Yangguang Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819475107

A fast algorithm was proposed to decrease the computational cost of the contour extraction approach based on quantum mechanics. The contour extraction approach based on quantum mechanics is a novel method proposed recently by us, which will be presented on the same conference by another paper of us titled "a statistical approach to contour extraction based on quantum mechanics". In our approach, contour extraction was modeled as the locus of a moving particle described by quantum mechanics, which is obtained by the most probable locus of the particle simulated in a large number of iterations. In quantum mechanics, the probability that a particle appears at a point is equivalent to the square amplitude of the wave function. Furthermore, the expression of the wave function can be derived from digital images, making the probability of the locus of a particle available. We employed the Markov Chain Monte Carlo (MCMC) method to estimate the square amplitude of the wave function. Finally, our fast quantum mechanics based contour extraction algorithm (referred as our fast algorithm hereafter) was evaluated by a number of different images including synthetic and medical images. It was demonstrated that our fast algorithm can achieve significant improvements in accuracy and robustness compared with the well-known state-of-the-art contour extraction techniques and dramatic reduction of time complexity compared to the statistical approach to contour extraction based on quantum mechanics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Wave functions

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Multi-Scale Channel Attention for Chinese Scene Text recognition 11

Multi-Scale Channel Attention for Chinese Scene Text Recogni...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Liao, Hai-Qing Du, Xia Wu, Yun Wang, Da-Han Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Scene text recognition have proven to be highly effective in solving various computer vision tasks. Recently, numerous recognition algorithms based on the encoder-decoder framework have been proposed for handling scene texts with perspective distortion and curve shape. Nevertheless, most of these methods only consider single-scale features while not taking multi-scale features into account. Meanwhile, the existing text recognition methods are mainly used for English texts, whereas ignoring Chinese texts' pivotal role. In this paper, we proposed an end-to-end method to integrate multi-scale features for Chinese scene text recognition (CSTR). Specifically, we adopted and customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture multi-scale features of the input image while simultaneously extending the receptive fields. Moreover, we added Squeeze-and-Excitation Networks (SE) to capture attentional features with global information to improve the performance of CSTR further. The experimental results of the Chinese scene text datasets demonstrate that the proposed method can efficiently mitigate the impacts of the loss of contextual information caused by the text scale varying and outperforms the state-of-the-art approaches. © 2022 ACM.

关键词： Character recognition

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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 11

Multi-Scale Features Integration for Handwritten Mathematica...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Liu, Xianghao Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method. © 2022 ACM.

关键词： Character recognition

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Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of Remote Sensing images 11

Swin Transformer with Multi-Scale Residual Attention for Sem...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Lin, Yuanyang Wang, Da-Han Wu, Yun Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Semantic segmentation of remote sensing images usually faces the problems of unbalanced foreground-background, large variation of object scales, and significant similarity of different classes. The FCN-based fully convolutional encoder-decoder architecture seems to have become the standard for semantic segmentation, and this architecture is also prevalent in remote sensing images. However, because of the limitations of CNN, the encoder cannot obtain global contextual information, which is extraordinarily important to the semantic segmentation of remote sensing images. By contrast, in this paper, the CNN-based encoder is replaced by Swin Transformer to obtain rich global contextual information. Besides, for the CNN-based decoder, we propose a multi-level connection module (MLCM) to fuse high-level and low-level semantic information to help feature maps obtain more semantic information and use a multi-scale upsample module (MSUM) to join the upsampling process to recover the resolution of images better to get segmentation results preferably. The experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. © 2022 ACM.

关键词： Convolution

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Integrating segmentation and classification accuracy for accuracy assessment in object-based image analysis

Integrating segmentation and classification accuracy for acc...

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2012 3rd IEEE/IET International Conference on Audio, Language and image processing, ICALIP 2012

作者： Li, Nan Huo, Hong Fang, Tao Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781467301718

An accuracy assessment method that integrates segmentation and classification accuracy is proposed to meet the requirements of object-based image analysis. Segmentation errors are measured by establishing the relationship between pixels and their corresponding segments according to the overlaps of segments and reference polygons. Then, two improved confusion matrices that take the segmentation errors into consideration are used: one for pixel-level classification results, and the other for object-level classification results. A final accuracy assessment combines the statistics of these two confusion matrices. The proposed method can be applied to segmentation scale selection in the hierarchical interpretation system. An experiment on a SPOT5 image demonstrates the effectiveness of this method for segmentation scale selection, which can guide the fusion of objects of different scales to obtain a higher accuracy. © 2012 IEEE.

关键词： image analysis

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Why is the Danger Cylinder Dangerous in the P3P Problem?

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自动化学报 2006年第4期32卷 504-511页

作者： ZHANG Cai-Xia HU Zhan-Yi National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 Institute of Image Processing and Pattern Recognition North China University of Technology Beijing 100041

The PnP problem is a widely used technique for pose determination in computer vision community,and finding out geometric conditions of multiple solutions is the ultimate and most desirable goal of the multi-solution analysis,which is also a key research issue of the *** this paper,we prove that given 3 control points,if the camera's optical center lies on the so-called“danger cylinder”and is enough far from the supporting plane of control points,the corresponding P3P problem must have 3 positive *** result can bring some new insights into a better understanding of the multi-solution *** example,it is shown in the literature that the solution of the P3P problem is instable if the optical center lies on this danger cylinder,we think such occurrence of triple-solution is the primary source of this instability.

关键词： The P3P problem the danger cylinder instability of solution

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Runway detection of an unmanned landing aerial vehicle based on vision

Runway detection of an unmanned landing aerial vehicle based...

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MIPPR 2005: SAR and Multispectral image processing

作者： Wang, Hongqun Peng, Jiaxiong Li, Lingling Institute for Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

When an monocular vision-based unmanned aerial vehicle (UAV) based on vision is flown to the final approach fix to intercept the glide slope without the navigation of Global Positioning System (GPS), the position and orientation of the airport runway in image must be detected accurately so as to a host of suitable procedures have to be followed. The optimum length of the final approach is about five miles from the runway threshold. The front view of the runway, which is achieved at the moment, is very illegible. The approaching marking (cross bar) of the runway are showed as some white spots of high intensity and the complicated backgrounds of the airport are included in the images. In this case, spots with high intensity should be extracted and classified, some of these spots are just the images of the background noises and the pseudo-targets, which can't be separated with the spots of the runway as in the view there is no significant characteristic difference among them ostensibly. Fortunately, in the terrestrial coordinate space, most of the runway marks are located at the apexes of a rectangle, having some geometric relationships. The relationship among the projection coordinates of the runway spots in the images can be determined according to the perspective principle, the constraint condition of the rectangle as well as the front shot constraint condition of the target, by using this relationship, the runway approaching marks can be separated, the position and the direction of the runway in the images can be identified. In this paper, the clustering management is adopted so as to greatly reduce the computing time. The consequence of the experiments shows that by this algorithm, even from a place far away from the runway whose marks are unclear, we also can effectively detect the runway.

关键词： Unmanned vehicles

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