检索结果-内蒙古大学图书馆

23rd German Association for pattern recognition Symposium, DAGM 2001

作者： Siggelkow, Sven Schael, Marc Burkhardt, Hans Institute for Pattern Recognition and Image Processing Computer Science Department Albert-Ludwigs-Universität Freiburg Germany

ISBN: (纸本)3540425969

In this paper we present SIMBA, a content based image retrieval system performing queries based on image appearance. We consider absolute object positions irrelevant for image similarity here and therefore propose to use invariant features. Based on a general construction method (integration over the transformation group), we derive invariant feature histograms that catch different cues of image content: features that are strongly influenced by color and textural features that are robust to illumination changes. By a weighted combination of these features the user can adapt the similarity measure according to his needs, thus improving the retrieval results considerably. The feature extraction does not require any manual interaction, so that it might be used for fully automatic annotation in heavily fluctuating image databases. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

Analytic reduction of the Kruppa equations

Analytic reduction of the Kruppa equations

引用

24th German Association for pattern recognition Symposium, DAGM 2002

作者： Canterakis, Nikos Albert-Ludwigs-Universität Freiburg Computer Science Department Institute for Pattern Recognition and Image Processing FreiburgD-79110 Germany

ISBN: (纸本)354044209X

Given the fundamental matrix between a pair of images taken by a nonstationary projective camera with constant internal parameters, we show how to use the two independent Kruppa equations in order to explicitly cut down the number of parameters of the Kruppa matrix KKT by exactly two. Thus, we derive a procedure which results in a closed formula for the Kruppa matrix that depends on exactly three remaining parameters. This formula allows an easy incorporation of the positivity constraint and admits of an interpretation in terms of the image of the horopter. We focus on the general case where the camera motion is unknown and not restricted to some special type. Solutions of the Kruppa equations given three fundamental matrices have been attempted in the past by iterative numerical methods that are searching in multidimensional spaces. As an application of the reduced Kruppa matrix mentioned above we also outline how this problem can be analytically reduced to the determination of the real, positive roots of a polynomial of 14-th degree in one variable. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Dysfunction and recovery of the cortical connectome gradient and its association with gene expression profiles in methamphetamine and heroin use disorders

引用

Progress in Neuro-Psychopharmacology and Biological Psychiatry 2025年 139卷 111391页

作者： Du, Zhe Yang, Wenhan Wen, Xinwen Cai, Suping Liu, Jun Yuan, Kai School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China Department of Radiology Second Xiangya Hospital Central South University Changsha 410011 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou 014010 China Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'an China Ganzhou City Key Laboratory of Mental Health The Third People's Hospital of Ganzhou City Jiangxi Ganzhou 341000 China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information School of Life Science and Technology Xidian University Shaanxi Xi'an 710126 China

Background: The hierarchy and segregation of community-based brain networks can be characterized by functional connectome gradient (FCG). Whether the cortical FCG was disrupted and could even be reversed after prolonged abstinence in substance use disorders (SUDs) remained unclear. Less is known about the underlying genetic basis. Methods: We utilized three independent datasets (n = 247) to investigate cortical FCG dysfunction and recovery after prolonged abstinence in methamphetamine use disorder (MUD) (n = 131), and validated in heroin use disorder (HUD) (n = 36). Furthermore, magnetic resonance spectroscopy (MRS) was employed to test the relationship between the glutamate in the ventral tegmental area (VTA) and cortical FCG. Postmortem gene expression was utilized to assess the transcriptional profiles related to cortical FCG alterations. Support vector regression (SVR) model based on the baseline cortical FCG was built to predict craving changes after long-term abstinence. Results: MUD and HUD individuals exhibited similar cortical FCG dysfunction and recovery at global and network levels, mainly in dorsal and ventral attentions network (DAN and VAN), frontoparietal network (FPN) and default mode network (DMN). The glutamate levels in VTA were correlated with the gradient distance between FPN and limbic network (LIM). The transcriptional profiles explained variance of the altered gradient pattern, with the most related genes enriched in synaptic signaling. Baseline cortical FCG significantly predicted craving changes after long-term abstinence. Conclusions: We provided novel insights into the understanding of SUD as treatable health conditions from cortical FCG changes, which could be considered as the potential biomarkers of craving changes after prolonged abstinence for SUDs. © 2025 Elsevier Inc.

关键词： Cortical functional connectome gradient Gene expression Glutamate Heroin use disorder Methamphetamine use disorder Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Building classification of terrestrial images by generic geometric hierarchical cluster analysis features

Building classification of terrestrial images by generic geo...

引用

9th IAPR Conference on Machine Vision Applications, MVA 2005

作者： Brunner, Gerd Burkhardt, Hans Computer Science Department Institute for Pattern Recognition and Image Processing University of Freiburg Georges-Koehler-Allee 052 79110 Freiburg Germany

ISBN: (纸本)4901122045

The scope of this paper is the challenging task of classifying terrestrial images of buildings, automatically. Straight line segments and their connectivity incorporate significant information about object shapes. Man-made buildings exhibit special generic shapes which are extracted from embedded spatial and angular line segment relationships by cluster analysis. After employing an agglomerative hierarchical cluster analysis we obtain geometrical structure information features on different scales. For the classification process we apply support vector machines (SVM) with polynomial and radial basis function (RBF) kernels to separate the feature space by a hyperplane into 2 classes. The method is applied to an image collection taken from the Corel image database and compared with traditional edge- orientation histogram features. We obtained a 88 % true positive classification rate (recall) with an F-measure value of 81.3 %. Copyright © 2005 by MVA Conference Committee.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation

引用

Medical image Analysis 2025年 103卷 103606页

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Jiang, Zihang Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Suzhou Institute for Advanced Research USTC Jiangsu Suzhou215123 China Department of Automation Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM. © 2025

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Data classification on multiple manifolds

Data classification on multiple manifolds

引用

International Conference on pattern recognition

作者： Xiao, Rui Zhao, Qijun Zhang, David Shi, Pengfei Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Department of Computer Science Hongkong Polytechnic University Hongkong Hong Kong

ISBN: (纸本)9780769541099

Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach. © 2010 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Evaluation of a new point clouds registration method based on group averaging features

Evaluation of a new point clouds registration method based o...

引用

International Conference on pattern recognition

作者： Temerinac-Ott, Maja Keuper, Margret Burkhardt, Hans Department of Pattern Recognition and Image Processing Institute of Computer Science Albert-Ludwigs-University of Freiburg Germany Albert-Ludwigs-University of Freiburg Germany

ISBN: (纸本)9780769541099

Registration of point clouds is required in the processing of large biological data sets. The tradeoff between computation time and accuracy of the registration is the main challenge in this task. We present a novel method for registering point clouds in two and three dimensional space based on Group Averaging on the Euclidean transformation group. It is applied on a set of neighboring points whose size directly controls computing time and accuracy. The method is evaluated regarding dependencies of the computing time and the registration accuracy versus the point density assuming their random distribution. Results are verified in two biological applications on 2D and 3D images. © 2010 IEEE.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Linear Correction for Visual Odometry 19

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Line...

引用

19th IEEE International Conference on Intelligent computer Communication and processing Conference, ICCP 2023

作者： Cobarzan, Corvin-Petrut Golban, Catalin-Cosmin Nedevschi, Sergiu Technical University of Cluj-Napoca Computer Science Department Romania Technical University of Cluj-Napoca Image Processing and Pattern Recognition Group Romania

ISBN: (纸本)9798350370355

Our previously proposed linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method corrects it at selected points in time based on the alignment of one past and the current 3D LiDAR measurements (see [7]). In this paper we improve on that method essentially by adding multi-past frame LiDAR point cloud alignment constraints and by performing the correction more often. While this significantly improves the accuracy of the estimation, it has the downside effect of decreasing the overall runtime performance. We thus study the trade-off between accuracy and speed and propose a couple of higher accuracy but real time correction versions. Their evaluation on the KITTI dataset results in an overall performance falling into the so called SLAM-nonSLAM gap. © 2023 IEEE.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Visual Odometry Drift Reduction Based on LiDAR Point Clouds Alignment 17

Visual Odometry Drift Reduction Based on LiDAR Point Clouds ...

引用

17th IEEE International Conference on Intelligent computer Communication and processing, ICCP 2021

作者： Golban, Cǎtǎlin-Cosmin Cobârzan, Corvin-Petruţ Nedevschi, Sergiu Technical University of Cluj-Napoca Computer Science Department Romania Technical University of Cluj-Napoca Image Processing and Pattern Recognition Group Romania

ISBN: (纸本)9781665409766

Visual and LiDAR based odometry methods are key enablers for autonomous robots sub-problems such as mapping and localization or temporal aggregation and fusion of sensor data. When it comes to trajectory reconstruction the errors of independent local estimations accumulate over time, resulting in overall drifts from the real trajectory. This paper proposes a method for reducing the global drift by correcting the video-based frame-to-frame trajectory estimation at selected points in time based on the alignment of past and current LiDAR 3D measurements. The main strength of the proposed method is the formulation of the problem as a system of linear equations, which is an adequate numerical approximation as the corrective component is small enough. In this way we eliminate the need to deploy expensive non-linear optimization methods for calculating the correction transform. © 2021 IEEE.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Comparative studies of feature extraction methods with application to face recognition

Comparative studies of feature extraction methods with appli...

引用

2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

作者： Jiang, Yunfei Guo, Ping Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)1424409918

In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include Eigenfaces, Kernel Principal Component Analysis (KPCA), Fisherfaces, Direct Linear Discriminant Analysis (D-LDA), Regularized Linear Discriminant Analysis (R-LDA), and Kernel Direct Discriminant Analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt Nearest Neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones. © 2007 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：