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检索条件"机构=Computer Vision and Pattern Recognition Lab."

共 299 条记录，以下是31-40 订阅

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Ui-net: Interactive artificial neural networks for iterative image segmentation based on a user model

Ui-net: Interactive artificial neural networks for iterative...

引用

2017 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2017

作者： Amrehn, Mario Gaube, Sven Unberath, Mathias Schebesch, Frank Horz, Tim Strumia, Maddalena Steidl, Stefan Kowarschik, Markus Maier, Andreas Pattern Recognition Lab. Computer Science Department Friedrich-Alexander University Erlangen-Nuremberg Germany Erlangen Germany Siemens Healthcare GmbH Forchheim Germany

ISBN: (纸本)9783038680369

For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient. We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction. © 2017 The Eurographics Association.

关键词： Neural networks

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Automatic patient pose estimation using pressure sensing mattresses

Automatic patient pose estimation using pressure sensing mat...

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2011 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2011

作者： Grimm, Robert Sukkau, Johann Hornegger, Joachim Greiner, Günther Pattern Recognition Lab. University of Erlangen-Nuremberg Germany Computer Graphics University of Erlangen-Nuremberg Germany Siemens AG Healthcare MR Erlangen Germany

ISBN: (纸本)9783642193347

We present a system to automatically estimate the body pose of a reclined patient, based on measurement data from a pressure sensing mattress. It can be used to replace or reduce manual input in clinical imaging procedures and thus improve the workflow. The proposed method consists of two stages. First, the body posture is classified into prone, supine, and left and light lateral orientation by a k-nearest-neighbor classifier. In the second algorithmic stage, a modified optimization scheme based on Powell's direction set method fits a model of the human body to the observed pressure distribution. Thus, the position of important body landmarks is estimated. For our database of 143 measurements from 16 subjects, a mean classification rate of 96.0 % was achieved for the posture, and an average localization error of 6.95 cm for the body parts. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Medical imaging

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Using a real-time web-based pattern recognition system to search for component patterns database

引用

1st EurAsian Conference on Advances in Information and Communication Technology, EurAsia-ICT 2002

作者： Hsiao, Sung-Jung Fan, Kuo-Chin Sung, Wen-Tsai Ou, Shih-Ching Artificial Intelligence and Pattern Recognition Lab. Department of Computer Science and Information Engineering National Central University Taiwan Automation and CAD Lab. Department of Electrical Engineering National Central University Taiwan

ISBN: (纸本)3540000283

Faraway engineers are able to sketch direct the shape of engineering components by the browser, and the recognition system will proceed with search for the component database of company by the Internet. In this paper, component patterns are stored in the database system. Component patterns with the approach of database system will be able to improve the capacity of recognition system effectively. In our approach, the recognition system adopts distributed compute, and it will raise the recognition rate of system. The system uses a recurrent neural network (RNN) with associative memory to perform the action of training and recognition. The final phase joins the technology of database match in process of the recognition except distributed compute, and it will solve the problem of spurious state. In this paper, our system will be carried out in the Yang-Fen Automation Electrical Engineering Company. The plan of experiment has gone through four months, and their engineers are also used to take advantage of the way of Web-Based pattern recognition. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Recurrent neural networks

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Latent variable models for predicting file dependencies in large-scale software development

Latent variable models for predicting file dependencies in l...

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24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Hu, Diane J. Van Der Maaten, Laurens Cho, Youngmin Saul, Lawrence K. Lerner, Sorin Dept. of Computer Science and Engineering University of California San Diego United States Pattern Recognition and Bioinformatics Lab. Delft University of Technology Netherlands

ISBN: (纸本)9781617823800

When software developers modify one or more files in a large code base, they must also identify and update other related files. Many file dependencies can be detected by mining the development history of the code base: in essence, groups of related files are revealed by the logs of previous workflows. From data of this form, we show how to detect dependent files by solving a problem in binary matrix completion. We explore different latent variable models (LVMs) for this problem, including Bernoulli mixture models, exponential family PCA, restricted Boltzmann machines, and fully Bayesian approaches. We evaluate these models on the development histories of three large, open-source software systems: Mozilla Firefox, Eclipse Subversive, and Gimp. In all of these applications, we find that LVMs improve the performance of related file prediction over current leading methods.

关键词： Bayesian networks

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Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement 20th

Bootstrap Diffusion Model Curve Estimation for High Resolut...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Huang, Jiancheng Liu, Yifan Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819970247

Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. To address these problems, we propose BDCE, a bootstrap diffusion model that exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. Specifically, we adopt the curve estimation method to handle the high-resolution images, where the curve parameters are estimated by our bootstrap diffusion model. In addition, a denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration. We evaluate BDCE on commonly used benchmark datasets, and extensive experiments show that it achieves state-of-the-art qualitative and quantitative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image enhancement

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Non-deterministic Behavior of Ranking-Based Metrics When Evaluating Embeddings 2nd

Non-deterministic Behavior of Ranking-Based Metrics When Eva...

引用

2nd International Workshop on Reproducible Research in pattern recognition, RRPR 2018

作者： Nicolaou, Anguelos Dey, Sounak Christlein, Vincent Maier, Andreas Karatzas, Dimosthenis Computer Vision Center Edificio O Campus UAB Bellaterra08193 Spain Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783030239862

Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. © Springer Nature Switzerland AG 2019.

关键词： Embeddings

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Nonlinear manifold clustering by dimensionality

Nonlinear manifold clustering by dimensionality

引用

18th International Conference on pattern recognition, ICPR 2006

作者： Wenbo, Cao Haralick, Robert Computer Science Pattern Recognition Lab. Graduate Center City University of New York 365 Fifth Avenue New York NY 10016 United States

ISBN: (纸本)0769525210

Because of variable dependence, high dimensional data typically have much lower intrinsic dimensionality than the number of its variables. Hence high dimensional data can be expected to lie in (nonlinear) lower dimensional manifold. In this paper, we describe a nonlinear manifold clustering algorithm. By connecting data vectors with their neighbors in feature space, we construct a neighborhood graph from given set data vectors. Furthermore, geometrical invariance, namely dimensionality, are extracted from the neighborhood of vectors, and used to facilitate the clustering procedure. In addition, we discuss a latent model for data cluster descriptions and an EM algorithm to find such descriptions. Preliminary experiments illustrate that this new algorithm can be used to explore the nonlinear structure of data. © 2006 IEEE.

关键词： Clustering algorithms

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On road vehicle make and model recognition via sparse feature coding

On road vehicle make and model recognition via sparse featur...

引用

Iranian Machine vision and Image Processing (MVIP)

作者： A. Nazemi M. J. Shafiee Z. Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Automatic vehicle Make and Model recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit (OMP), some variation of Sparse Coding (SC) methods and compares them to choose the best one. Our method employs the sparse feature coding methods on dense Scale-Invariant Feature Transform (SIFT) features and Support Vector Machine (SVM) for classification. The proposed system is examined by an Iranian on road vehicles dataset, which its samples are in different point of views, various weather conditions and illuminations.

关键词： Feature extraction Vehicles Encoding Dictionaries Image coding Kernel computer vision

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A new approach to speed up in action recognition based on key-frame extraction

A new approach to speed up in action recognition based on ke...

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Iranian Machine vision and Image Processing (MVIP)

作者： Neda Azouji Zohreh Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Human action recognition is the process of lab.ling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address this problem using video abstraction techniques including key-frame extraction and video skimming. At first we extract key-frames and then skim the video clip by concatenating excerpts around the selected key-frames. This shorter sequence is used as input for classifier. Our proposed approach not only reduces the space complexity but also reduces the run time in both train and test steps. The experimental results provided on KTH action datasets show that the proposed method achieves good performance without losing considerable classification accuracy.

关键词： Videos Feature extraction Accuracy Image color analysis Shape Support vector machines computer vision

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A deep learning architecture for limited-angle computed tomography reconstruction

A deep learning architecture for limited-angle computed tomo...

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Workshops on Image processing for the medicine, 2017

作者： Hammernik, Kerstin Würfl, Tobias Pock, Thomas Maier, Andreas Institute of Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition Lab Friedrich-Alexander-University Germany Digital Safety and Security Department AIT Austrian Institute of Technology Austria

ISBN: (纸本)9783662543443

Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step deep learning architecture: First, we learn compensation weights that account for the missing data in the projection domain and correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking artifacts. We perform our experiments on realistic data and we achieve superior results for destreaking compared to state-of-the-art non-linear filtering methods in literature. We show that our approach eliminates the need for manual tuning and enables joint optimization of both correction schemes. © Springer-Verlag GmbH Deutschland 2017.

关键词： Image reconstruction

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