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检索条件"机构=Image Processing and Pattern Recognition Institute"

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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2012 19th International Conference on Systems, Signals and image processing, IWSSIP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab. Vienna University of Technology Austria

ISBN: (纸本)9783200023284

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate. © 2012 institute of Telecommunica.

关键词： Classification (of information)

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Adaptive multi-level region merging for salient object detection 25

Adaptive multi-level region merging for salient object detec...

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25th British Machine Vision Conference, BMVC 2014

作者： Fu, Keren Gong, Chen Yun, Yixiao Li, Yijun Gu, Irene Yu-Hua Yang, Jie Yu, Jingyi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden University of Delaware Newark United States

Most existing salient object detection algorithms face the problem of either under-or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph-based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance. © 2014. The copyright of this document resides with its authors.

关键词： Merging

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Graph theory based image segmentation

Graph theory based image segmentation

引用

Congress on image and Signal processing, CISP

作者： Songhao Zhu Xinshuai Zhu Qingqing Luo Institute of Image Processing and Pattern Recognition Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781479927654

image segmentation is a fundamental process in many image, video, and computer vision applications. It is very essential and critical to image processing and pattern recognition, and determines the quality of final result of analysis and recognition. This paper presents a semi-supervised strategy to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized cut criterion is utilized to segment images into meaningful units. The experimental results conducted on Berkeley image databases and MSRC image databases demonstrate the effectiveness of the proposed strategy.

关键词： image segmentation pattern recognition image databases Graph theory image edge detection Computer vision Approximation algorithms

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An Self-supervised Learning Self-attention Based Method for Defects Classification on PCB Surface images 2

An Self-supervised Learning Self-attention Based Method for ...

引用

2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021

作者： Zhou, Lijie Ling, Xufeng Zhu, Shan Sun, Zheng Yang, Jie AI School Tianhua College Shanghai Normal University Shanghai China ASIC Motor Corporation Limited China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781665437578

Automatic detection of PCB defects become a difficult work in electronics industry, with the rapid development of Integrated Circuit. A good detection method can effectively improve production efficiency and reduce the costs of quality inspection. The traditional image processing methods have the backwards of complicated system structure, difficult image segmentation, and low detection accuracy, etc. In order to solve the above problems, this paper proposes a method of extracting features by self-supervised learning with supervised learning to complete the classification task. This method uses a deep neural network model based on self-supervised learning technology, uses multi-head self-attention mechanism;automatically recognize single or multiple defect areas in PCB, to extract defects features of PCB, and uses full connection classifier to classify features. The open-source (DeepPCB) is selected as the dataset, including 6 defect categories and non-defect categories. In the training process, data enhancement technology is used to increase the training samples, train the model and make it converge, and realize low error recognition on the verification dataset. The experimental results show that the features extracted by the self-supervised learning method are richer than those extracted by the supervised learning. The composite model formed by combining the self-attention mechanism has high accuracy and good robustness, reaches the classification accuracy of 94.33%, and has high accuracy and good generalization ability. © 2021 IEEE.

关键词： Deep neural networks

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OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow

OAS-Net: Occlusion Aware Sampling Network for Accurate Optic...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Lingtong Kong Xiaohang Yang Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781728176055;9781728176062

Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key process is to adopt warped target feature based on previous flow prediction to correlate with source feature for building 3D matching cost volume. However, the warping operation can lead to troublesome ghosting problem that results in ambiguity. Moreover, occluded areas are treated equally with non occluded regions in most existing works, which may cause performance degradation. To deal with these challenges, we propose a lightweight yet efficient optical flow network, named OAS-Net (occlusion aware sampling network) for accurate optical flow. First, a new sampling based correlation layer is employed without noisy warping operation. Second, a novel occlusion aware module is presented to make raw cost volume conscious of occluded regions. Third, a shared flow and occlusion awareness decoder is adopted for structure compactness. Experiments on Sintel and KITTI datasets demonstrate the effectiveness of proposed approaches.

关键词： Correlation Three-dimensional displays Estimation Optical fiber networks Decoding Noise measurement Task analysis

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Liver fibrosis identification based on ultrasound images

Liver fibrosis identification based on ultrasound images

引用

2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Guitao, Cao Pengfei, Shi Bing, Hu IEEE Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China Department of Ultrasound in Medicine Shanghai Sixth Hospital Shanghai Jiaotong University Shanghai 200233 China

ISBN: (纸本)0780387406

Diagnostic ultrasound is one of useful and noninvasive tools for clinical medicine. However, due to its qualitative, subjective and experience-based nature, ultrasound images can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and Support Vector Machine are employed to test on a group of 99 liver fibrosis images from 18 patients, as well as other 273 healthy liver images from 18 specimens. © 2005 IEEE.

关键词： Medical problems

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Sparse slow feature analysis for enhanced control monitoring and fault isolation 1

Sparse slow feature analysis for enhanced control monitoring...

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1st International Conference on Industrial Artificial Intelligence, IAI 2019

作者： Shang, Chao Huang, Xiaolin Yang, Fan Huang, Dexian Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China

ISBN: (纸本)9781728135939

This paper presents sparse slow feature analysis (SFA) for efficient process monitoring and fault isolation, which is a new latent variable model for time series data. We first recast sparse SFA in terms of a novel regression-Type problem, and further incorporate l-{1}-norm penalty into the objective in order to promote sparsity. To solve the induced nonconvex optimization problem, a tailored iterative algorithm is developed. With the sparse representation, process variables with insignificant contributions can be completely omitted, and each latent variable relates only to a fraction of crucial process variables in comparison with the generic SFA. A new process monitoring and fault isolation approach is developed based on the sparse SFA, which results in improved monitoring performance, easy-To-interpret diagnostics, and meaningful process knowledge discovery. Case studies on the Tennessee Eastman process are carried out to address the applicability of the proposed approach. © 2019 IEEE.

关键词： Process monitoring

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Practical multi-camera calibration algorithm with 1D objects for virtual environments

Practical multi-camera calibration algorithm with 1D objects...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zijian Zhao Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

A new calibration algorithm for multi-camera systems using the 1D calibration objects is proposed. The algorithm integrates the rank-4 factorization with Zhangpsilas method. The intrinsic parameters as well as the extrinsic parameters are recovered only by capturing with cameras the 1D objectpsilas rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depths of which is estimated in an analytic equation instead of a recursive form. For the conditions that there are more than 3 points on the 1D object, our algorithm may solve them by extending the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. The validity of the proposed technique was verified through simulation and experiments with real images.

关键词： Cameras Calibration Transmission line matrix methods Equations Matrix decomposition Mathematical model Shape

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Fast sar image matching algorithm in naviglation system

Fast sar image matching algorithm in naviglation system

引用

2003 IEEE Aerospace Conference

作者： Li, Yang Xin, Yuan Shuhua, Wang Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University 1954 HuaShan Road 200030 Shanghai China Nanjing University of Aeronautics and Astronautics 29 YuDao Road 210016 Nanjing China

ISBN: (纸本)078037651X

The paper presents a novel fast matching algorithm between airborne and satellite-borne SAR images so as to efficiently integrate SAR images into GPSISINS navigation system. Because the character such as gray level and resolution of an airborne image is different from that of a satellite-home SAR image, the matching algorithm in this paper is in principle based on edge extracting method on consideration of the real-time calculation ability. Wavelet decomposition is carried out on the input images and the edges are extracted from fine to coarse, where certain solutions are adopted against the inherent speckle noise in SAR image. On the other hand, the matching process between reference image and sensed image from coarse to fine is based on Hausdorff similarity. Furthermore, Kalman filter contributes to estimation and compensation of matching error so that matchng precision can be obviously improved. Theoretic analysis and simulation results not only illustrate the feasibility and reliability of the presented algorithm in this paper, but also manifest its fast matching process. © 2003 IEEE.

关键词： Synthetic aperture radar

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A new printing measurement method and structure feature extraction of microscopic dot

A new printing measurement method and structure feature extr...

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Yonggang Wang Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780382730

To overcome the drawbacks of print measurements, a novel measurement approach, namely dot structure based measurement, is proposed. In addition, the structure feature extraction of microscopic dot is discussed. The proposed scheme provides a precondition for such further research as print quality surveillance, fault diagnosis, color design and printing theories as well.

关键词： Printing Feature extraction Microscopy US Department of Transportation image processing pattern recognition Surveillance Fault diagnosis Quality control image color analysis

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