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AN EFFICIENT DUPLICATE IMAGE DETECTION METHOD BASED ON AFFINE-SIFT FEATURE

AN EFFICIENT DUPLICATE IMAGE DETECTION METHOD BASED ON AFFIN...

2010 3rd IEEE International Conference on Broadband Network & Multimedia Technology(2010年第三届IEEE宽带网络与多媒体国际会议 IC-BNMT 2010)

作者： Yudong Cao Honggang Zhang Yanyan Gao Jun Guo Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunica Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunica Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunica

In this paper, we present a novel scheme to tackle the task of near-duplicate image detection. Given two input images, the algorithm based on the refined similarity measure can judge rightly whether two input image are duplicate images or not. The two images are represented with local feature (i.e, Affine-SIFT) in bag of features model. The Affine-SIFT can undergo larger affine distortions than Hessian-Affine and MSER (Maximally Stable Extremal Region). The refined similarity measure exploits the spatial information between two images. The algorithm is demonstrated on some image pairs with scale change, viewpoint change, blur, noise and spatial deformation. The experimental results show that proposed algorithm is more effective than other state-of-the-art duplicate image detection algorithm.

关键词： duplicate detection image matching Affine-SIFT

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Size and Defect Detection of Valve Based on Computer Vision

Size and Defect Detection of Valve Based on Computer Vision

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2022 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Yan, Xiaobo Feng, Le Tan, Mian Yang, Yuan Zhang, Yu Wang, Lin Guizhou Key Laboratory of Pattern Recognition and Intelligent System China Guizhou Minzu University Guiyang550025 China Guiyang Fusheng Intelligent Technology Co. Ltd China

ISBN: (纸本)9781510663350

Engine valve is the core component of the engine, and its quality determines the performance of the engine. In industrial production quality inspection, it is necessary to detect the size of the valve and whether there are defects on the surface. Usually, the quality of the valve is determined by comparing the image of the valve surface with the standard image. However, the existing surface defect detection technology cannot detect the curved surface device. In order to solve this problem, this paper designs a valve size and defect detection method based on computer vision. The experimental results show that the method can quickly and accurately detect the rod diameter, groove radius and surface defects of the valve. The method is practical, robust and real-time. © 2023 SPIE.

关键词： Computer vision

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Misclassification and margin based SVM active learning algorithm for audio event detection

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International Journal of Advancements in Computing Technology 2011年第9期3卷 90-97页

作者： Leng, Yan Liu, Gang Qi, Guanghui Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing 100876 China College of Physics and Electronics Shandong Normal University Jinan 250014 China Department of Construction Machinery Shandong Jiaotong University Jinan 250023 China

Audio event detection has become a hot research due to its wide applications in many fields, such as multimedia retrieval etc., the detection needs large amounts of labeled samples to train the audio event models, but in real life, the labeled samples are expensive to obtain, the shortage of such labeled samples is a big obstacle. Active learning is an efficient way to deal with the problem of insufficient labeled samples. The most popular support vector machines active learning is the margin based sampling (MBS), which is to query the sample closest to the current hyperplane, but when the current hyperplane is far away from the true hyperplane, the sample closest to the current hyperplane is not so informative, querying such samples would have a much slower adjustment of the hyperplane. In order to accelerate the adjustment, this paper proposes the misclassification and margin based sampling (MMBS) active learning algorithm. In order to query more informative samples, MMBS selects samples based on misclassified samples' KL divergence in the first few iterations, after that, considering the lower misclassification confidence and the outlier problem, it switches to MBS. Experiments show that compared to MBS and representative sampling (RepS), MMBS can get the highest detection performance under the same human annotation workload.

关键词： Support vector machines

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N-LBP BASED VEHICLE MONITORING system

N-LBP BASED VEHICLE MONITORING SYSTEM

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2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Kunyan Lan Jun Guo Wenting Lu Honggang Zhang Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommun Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommuni

ISBN: (纸本)9781424448999

In recent years, feature based object detection has attracted increasing attention in computer vision research community. However, to our best knowledge, no previous work has focused on utilizing local binary pattern (LBP) for vehicle detection in intelligent Transportation system（ITS） domain. In this paper, we develop a novel traffic monitoring system based on N-LBP algorithm, which is the new LBP texture descriptor proposed. The approach includes three steps: firstly the general critical ingredients (GCI for short) are selected from LBP features through training to indicate vehicles. Then GCI are extracted from region of interest (ROI) in the new image for object detection and identification. Linear Kalman filter is employed for feature based tracking finally. Experimental results demonstrate the superiority of N-LBP feature over basic LBP feature, and performance of the new system is more stable and reliable.

关键词： ITS vehicle detection vehicle tracking N-LBP Kalman filter

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NOVEL ACTIVE LEARNING SAMPLE EVALUATION METHOD BASED ON MULTI-LEVEL CONFUSION NETWORKS

NOVEL ACTIVE LEARNING SAMPLE EVALUATION METHOD BASED ON MULT...

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2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： Wei Chen Jun Guo Gang Liu Pattern Recognition and Intelligent System LaboratoryBeijing University of Posts and Telecommunicati Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunicat

Active Learning (AL) is designed to aid the laborintensive process of training acoustic model for speech recognition. In AL, only the most informative training samples are selected for manual annotation. Thus, how to evaluate the unlabeled samples is worth researching. In this paper, we propose a unified framework to generate confusion networks of multiple levels including character, syllable and phone, and present a novel active learning sample evaluation method for Chinese acoustic modeling, posterior probabilities obtained from multi-level confusion networks are respectively adopted to evaluate the unlabeled samples. Our experiments show that compared with the widely used sample evaluation method using word posterior probability obtained from word confusion network, our proposed method can achieve satisfying performances.

关键词： Active learning confusion network acoustic model speech recognition

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An efficient duplicate image detection method based on Affine-SIFT feature

An efficient duplicate image detection method based on Affin...

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2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology, IC-BNMT2010

作者： Cao, Yudong Zhang, Honggang Gao, Yanyan Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China College of Electrical and Information Engineering Liaoning University of Technology Jinzhou China

ISBN: (纸本)9781424467709

关键词： Affine-SIFT Duplicate detection Image matching

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Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Du, Ruoyi Chang, Dongliang Bhunia, Ayan Kumar Xie, Jiyang Ma, Zhanyu Song, Yi-Zhe Guo, Jun Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China SketchX CVSSP University of Surrey Guildford United Kingdom

ISBN: (数字)9783030585655

ISBN: (纸本)9783030585648

Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works are mainly part-driven (either explicitly or implicitly), with the assumption that fine-grained information naturally rests within the parts. In this paper, we take a different stance, and show that part operations are not strictly necessary – the key lies with encouraging the network to learn at different granularities and progressively fusing multi-granularity features together. In particular, we propose: (i) a progressive training strategy that effectively fuses features from different granularities, and (ii) a random jigsaw patch generator that encourages the network to learn features at specific granularities. We evaluate on several standard FGVC benchmark datasets, and show the proposed method consistently outperforms existing alternatives or delivers competitive results. The code is available at https://***/PRIS-CV/PMG-Progressive-Multi-Granularity-Training. © 2020, Springer Nature Switzerland AG.

关键词： Computer vision

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A new RCNN-RF for efficient and robust parking lot available detection 16

A new RCNN-RF for efficient and robust parking lot available...

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16th International Conference on Digital Image Processing, ICDIP 2024

作者： Zhang, Qian Ran, Yaqin Luo, Di Xu, Kaili Shi, Jilaing School of Data Science and Information Engineering GuiZhou MinZu University Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

It's very meaningful to conduct the driver to the parking space available in the parking lot clearly and accurately by computer vision and computational intelligence. While it is an extremely difficult task, because of the scene complexity in the parking lot. In this paper, a new computational intelligence technology is proposed to detect parking spaces available in the parking lot outdoor based on fusing of Region Convolution Neural Network (RCNN) and random forests (RF), and we called it as RCNN-RF. Within the RCNN-RF, a new activation function is design. And with the help of random forests, RCNN-RF overcomes scene complexity includes illumination, weather, and slight occlusion successfully. Extensive experimental results on the benchmark dataset named PKLot and the self-built dataset named GZMU-LOT outperform those of the state-of-the-art, and it shows the superiority of our proposed RCNN-RF method. © 2024 SPIE.

关键词： Random forests

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BLOG EXTRACTION WITH TEMPLATEINDEPENDENT WRAPPER

BLOG EXTRACTION WITH TEMPLATEINDEPENDENT WRAPPER

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2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： Zhixuan Zhang Chuang Zhang Zhiqing Lin Bo Xiao Pattern Recognition and Intelligent System Laboratory(PRIS) Beijing University of Posts and Telecomm Pattern Recognition and Intelligent System Laboratory(PRIS) Beijing University of Posts and Telecom

Rich information is contributed to blogs by millions of users all around the world with the development of blogsphere. However, few work has been done on the study of blog extraction so far. Unlike the traditional template-dependent wrapper, not only blog articles but also blogroll is extracted with template-independent wrapper in this paper. In our method, blog extraction is formalized as a machine learning problem and a template-independent wrapper is learned by using labeled blog pages from a single site. Testing pages are obtained from 10 popular Chinese blog sites. And experimental results on 300 real blog pages indicate that the proposed method can correctly extract data from blogs with the accuracy of 90％ or even above.

关键词： template-independent data extraction web mining

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Quantile-based method for the recognition of handwritten Chinese characters

Quantile-based method for the recognition of handwritten Chi...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Ren Junling Wang Chengquan Guo Jun Pattern Recognition and Intelligent System Laboratory BUPT Beijing China

In course of generating templates of handwritten Chinese characters, the method based on average of the samples features is popular. In this paper, an approach based on quantile is put forward to generate templates. According to our experiments, this method of template generating is robust than that one based on average and it improves the performance of recognition system in the case of not increasing any complexity of the algorithm. At the same time, the method is universal and can be in combination with diversified feature extraction methods.

关键词： Character recognition Handwriting recognition Character generation Robustness Statistics pattern recognition Feature extraction Gaussian distribution intelligent systems Utility programs

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