检索结果-内蒙古大学图书馆

International Conference on computer vision Workshops (ICCV Workshops)

作者： Sebastian Bauer Jakob Wasza Sven Haase Natalia Marosi Joachim Hornegger Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen Nurnberg Germany Erlangen Graduate School in Advanced Optical Technologies (SAOT) Friedrich-Alexander-Universität Erlangen Nurnberg Germany Healthcare Sector Siemens AG Erlangen Germany

In radiation therapy, prior to each treatment fraction, the patient must be aligned to computed tomography (CT) data. Patient setup verification systems based on range imaging (RI) can accurately verify the patient position and adjust the treatment table at a fine scale, but require an initial manual setup using lasers and skin markers. We propose a novel markerless solution that enables a fully-automatic initial coarse patient setup. The table transformation that brings template and reference data in congruence is estimated from point correspondences based on matching local surface descriptors. Inherently, this point-based registration approach is capable of coping with gross initial misalignments and partial matching. Facing the challenge of multi-modal surface registration (RI/CT), we have adapted state-of-the-art descriptors to achieve invariance to mesh resolution and robustness to variations in topology. In a case study on real data from a low-cost RI device (Microsoft Kinect), the performance of different descriptors is evaluated on anthropomorphic phantoms. Furthermore, we have investigated the system's resilience to deformations for mono-modal RI/RI registration of data from healthy volunteers. Under gross initial misalignments, our method resulted in an average angular error of 1.5° and an average translational error of 13.4 mm in RI/CT registration. This coarse patient setup provides a feasible initialization for subsequent refinement with verification systems.

关键词： Computed tomography Histograms Topology Phantoms Measurement Accuracy Surface treatment

来源：评论

学校读者我要写书评

暂无评论

Hyper-sausage coverage function neuron model and learning algorithm for image classification

引用

pattern recognition 2023年 136卷

作者： Ning, Xin Tian, Weijuan He, Feng Bai, Xiao Sun, Le Li, Weijun Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Sch Integrated Circuits Beijing 100049 Peoples R China Beijing Key Lab Semicond Neural Network Intelligen Beijing 100083 Peoples R China Yangtze Univ Dept Comp Sci Jingzhou 434023 Peoples R China Beihang Univ Jiangxi Res Inst Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China Cognit Comp Technol Joint Lab Wave Grp Beijing 100083 Peoples R China Nanjing Univ Informat Sci & Technol Dept Jiangsu Collaborat Innovat Ctr Atmospher Envi Nanjing 210044 Peoples R China

Recently, deep neural networks (DNNs) promote mainly by network architectures and loss functions;however, the development of neuron models has been quite limited. In this study, inspired by the mechanism of human cognition, a hyper-sausage coverage function (HSCF) neuron model possessing a high flexible plasticity. Then, a novel cross-entropy and volume-coverage (CE_VC) loss is defined, which compresses the volume of the hyper-sausage to the hilt, and helps alleviate confusion among different classes, thus ensuring the intra-class compactness of the samples. Finally, a divisive iteration method is introduced, which considers each neuron model as a weak classifier, and iteratively increases the number of weak classifiers. Thus, the optimal number of the HSCF neuron is adaptively determined and an endto-end learning framework is constructed. In particular, to improve the classification performance, the HSCF neuron can be applied to classical DNNs. Comprehensive experiments on eight datasets in several domains demonstrate the effectiveness of the proposed method. The proposed method exhibits the feasibility of boosting DNNs with neuron plasticity and provides a novel perspective for further developments in DNNs. The source code is available at https://***/Tough2011/*** .(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： pattern recognition Deep neural networks Neuron model Brain -inspired computer vision

来源：评论

学校读者我要写书评

暂无评论

pfSense 2 Cookbook 1

引用

2011年

作者： Matt Williamson

ISBN: (数字)9781849514873

ISBN: (纸本)9781849514866

This book is written in a cookbook style. Each chapter consists of recipes, each of which is composed of step-by-step instructions to complete a particular task. Each recipe also includes tips, alternatives, and references to other recipes or appropriate external sources. The book can be explored chapter by chapter or in no particular order. This book is intended for all levels of network administrators. If you are an advanced user of pfSense, then you can flip to a particular recipe and quickly accomplish the task at hand, while if you are new to pfSense, you can read chapter by chapter and learn all of the features of the system from the ground up.

关键词： computer vision -- Congresses pattern recognition systems -- Congresses

来源：评论

学校读者我要写书评

暂无评论

Forest Fire Smog Feature Extraction Based on Pulse-Coupled Neural Network

Forest Fire Smog Feature Extraction Based on Pulse-Coupled N...

引用

2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference(ITAIC 2011)

作者： Wu Jiang School of Science Beijing Forestry University Beijing,China Huang Rule Information Centre Beijing Forestry University Beijing,China Xu Ziyue School of Technology Beijing Forestry University Beijing,China Han Ning School of Technology Beijing Forestry University Beijing,China

A novel algorithm for image-based forest fire smog feature extraction based on Pulse-Coupled neural network (PCNN) is *** PCNN is derived from the phenomena of synchronous pulse burst in mammals' visual *** outputs of PCNN represent unique features of imported images,and has been proved to be invariant to translation,rotation and *** this paper the image from video surveillance monitor is split into three dimensions in RGB color space,then input each dimension to PCNN to extract texture feature,the one-class support vector machine is applied to predict the feature in order to test feature's *** experimental results show that the algorithm we propose accurately distinguishes smog and non-smog images which outperform both the traditional Euclidean distance algorithms and the algorithms based on grey level co-occurrence matrix (GLCM) we applied in our earlier *** recognition accuracy is 98% with robustness on our smog image database.

关键词： Forest fire smog recognition,Pulse-Coupled neural network (PCNN),Support vector machine (SVM),Feature extraction,pattern recognition

来源：评论

学校读者我要写书评

暂无评论

基于计算机视觉的大田害虫远程自动识别系统

引用

农业工程学报 2013年第3期29卷 156-162页

作者：韩瑞珍何勇浙江大学生物系统工程与食品科学学院杭州310058 浙江传媒学院电子信息学院杭州310018 浙江大学唐仲英传感材料及应用研究中心杭州310058

为了实现大田害虫的快速实时识别和诊断,设计了一套大田害虫远程自动识别系统。该系统通过3G无线网络将害虫照片传输到主控平台中,在主控平台中实现远程自动识别。系统首先对害虫图像进行基于形态和颜色特征值的提取。害虫图像的形态特... 详细信息

为了实现大田害虫的快速实时识别和诊断,设计了一套大田害虫远程自动识别系统。该系统通过3G无线网络将害虫照片传输到主控平台中,在主控平台中实现远程自动识别。系统首先对害虫图像进行基于形态和颜色特征值的提取。害虫图像的形态特征由周长、面积、偏心率等以及7个胡不变矩共16个特征值组成,颜色特征值由9个颜色矩组成,然后建立支持向量机分类器。采用该系统对6种常见大田害虫进行了测试,平均准确率达到87.4%。考虑到不同的害虫姿态和大田中不同的光照条件,系统的分类效果是满意的。

关键词：计算机视觉图像处理监测大田害虫植保

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal Interest Points Chain (STIPC) for Activity recognition

Spatio-Temporal Interest Points Chain (STIPC) for Activity R...

引用

第一届亚洲模式识别会议

作者： Fei YUAN Veronique Prinet Gui-Song Xia Hichem Sahbi National Laboratory of Pattern Recognition Institute of Automation CAS Beijing P.R.C LTCI-CNRS TELECOM ParisTech Paris France

We present a novel feature, named Spatio-Temporal Interest Points Chain (STIPC), for activity representation and recognition. This new feature consists of a set of trackable spatio-temporal interest points, which correspond to a series of discontinuous motion among a long-term motion of an object or its part. By this chain feature, we can not only capture the discriminative motion information which space-time interest point-like feature try to pursue, but also build the connection between them. Specifically, we first extract the point trajectories from the image sequences, then partition the points on each trajectory into two kinds of different yet close related points: discontinuous motion points and continuous motion points. We extract local space-time features around discontinuous motion points and use a chain model to represent them. Furthermore, we introduce a chain descriptor to encode the temporal relationships between these interdependent local space-time features. The experimental results on challenging datasets show that our STIPC features improves local space-time features and achieve state-ofthe- art results.

关键词： Trajectory Feature extraction Tracking computer vision Humans pattern recognition Accuracy

来源：评论

学校读者我要写书评

暂无评论

Saliency detection based on Boosting learning

Saliency detection based on Boosting learning

引用

International Conference on Computational Problem-Solving (ICCP)

作者： Xiaohu Shao Hongliang Li School of Electronic Engineering University of Science and Technology of China Chengdu China

In this paper, we propose a method for saliency detection based on Boosting algorithms in still images. Compared to saliency detectors of pixel level based, we detect salient regions of an image based on sub-windows at any locations and sizes. For each window, we compute a set of features including local contrast, gradient histogram contrast. We construct our detector based on a cascade AdaBoost classifier to get the sub-windows which contain salient objects. Generally, more than one sub-window would get through the AdaBoost detector and we introduce a score function to remove redundant sub-windows and get the final one. The algorithm is tested on the MSRA Salient Object Database, and experiment results show that the proposed approach achieves a fast and accurate saliency detection system.

关键词： Feature extraction Conferences Training computer vision Histograms pattern recognition Testing

来源：评论

学校读者我要写书评

暂无评论

Video Reshuffling with Narratives toward Effective Video Browsing

Video Reshuffling with Narratives toward Effective Video Bro...

引用

International Conference on Image and Graphics (ICIG)

作者： Wei Fu Jinqiao Wang Xiaobin Zhu Hanqing Lu Songde Ma National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

With the rapid increasing of video cameras, large amount of video data everyday brings the problem of video storage and browsing. In this paper, we propose a novel approach to video reshuffling with a group of static images to effectively summarize the video content. Each static image called narrative is generated to depict the behavior of a specific object or a special event. Firstly background subtraction and object tracking are employed to extract the segmentations of moving objects and corresponding trajectories. After that, we apply three sampling rules to optimized select representative object samples from the spatial-temporal object tube and stitch them to the background image by Poisson blending. Experimental results show the promise of the proposed approach.

关键词： Electron tubes Histograms Trajectory Image segmentation computer vision Graphics Image color analysis

来源：评论

学校读者我要写书评

暂无评论

Off-line Signature Identification Using Background and Foreground Information

Off-line Signature Identification Using Background and Foreg...

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Srikanta Pal Alaei Alireza Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Gold Coast Australia Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Biometric systems play an important role in the field of information security as they are extremely required for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is currently receiving renewed interest and is only one of several techniques used to verify the identities of individuals. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing of documents containing embedded signatures. In this paper, a technique for a bi-script off-line signature identification system is proposed. In the proposed signature identification system, the signatures of English and Bengali (Bangla) are considered for the identification process. Different features such as under sampled bitmaps, modified chain-code direction features and gradient features computed from both background and foreground components are employed for this purpose. Support Vector Machines (SVMs) and Nearest Neighbour (NN) techniques are considered as classifiers for signature identification in the proposed system. A database of 1554 English signatures and 1092 Bengali signatures are used to generate the experimental results. Various results based on different features are calculated and analysed. The highest accuracies of 99.41%, 98.45% and 97.75% are obtained based on the modified chain-code direction, under-sampled bitmaps and gradient features respectively using 1800 (1100 English+700 Bengali) samples for training and 846 (454 English+392 Bengali) samples for testing.

关键词： Feature extraction Support vector machines Handwriting recognition Training Educational institutions Kernel Databases

来源：评论

学校读者我要写书评

暂无评论

EFFICIENT BLOCK-DIvision MODEL FOR ROBUST MULTIPLE OBJECT TRACKING

EFFICIENT BLOCK-DIVISION MODEL FOR ROBUST MULTIPLE OBJECT TR...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wenhan Luo Xiaoqin Zhang Yang Liu Xi Li Weiming Hu Wei Li National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China College of Mathematics & Information Science Wenzhou University Zhejiang China School of Computer Science University of Adelaide Australia

Tracking multiple objects under occlusion is one of the most challenging issues in computer vision. Occlusion results in mistaken match when finding the most similar candidate. Adapting to the change of objects is essential for tracking as objects often undergo intrinsic changes, but noise is unavoidably introduced during updating of the object, and this further confuses the tracker. In order to address these problems, a block-division appearance model is introduced to efficiently handle occlusion. In this model, spatial information is introduced to avoid the mistaken match between object and candidate. Based on this model, a selective updating strategy is proposed to incrementally learn the change of the object, avoiding introducing noise when updating. At the same time occlusion is deduced by monitoring the variation of each block. Experimental results in various videos validate the effectiveness of our algorithm in tracking multiple objects under occlusion.

关键词： Videos Covariance matrix Cognition Robustness Indexes Arrays Image reconstruction

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：