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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

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An Orientation-correction Detection Method for Scene Text Based on SPP-CNN 4

An Orientation-correction Detection Method for Scene Text Ba...

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4th IEEE International conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Dai, Jin Guo, Liang Wang, Zu Liu, Shuguang Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Peoples R China

ISBN: (纸本)9781728114101

Scene text detection plays an important role in computer vision and pattern recognition field in recent years due to extract the accurate and rich text information. At present, component-based methods have become the trend, and there are still some challenges because of illumination, blur and difficult background. In this paper, an orientation-correction detection method for scene text based on Spatial Pyramid Pooling Convolutional Neural Networks, SPP-CNN, is proposed. Firstly, an enhanced multi-channel MSER model, which is constructed from R, G, B, H, S, V and grey channels of manually blurred and sharpened images, is built. then, the manually-designed features are embedded in SPP-CNN as the effective text feature detector and classifier. Finally, a twolayer text grouping algorithm is achieved which can handle slightly-slanted text. Experiments on ICDAR 2011 show the f-measure has improved to 84%, the precision and f-measure of ICDAR 2013 have reached 87% and 85% respectively.

关键词： multi-channel MSER SPP-CNN scene text detection orientation correction

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A Remote Sensing Image Registration Method Based on Multi-features 4

A Remote Sensing Image Registration Method Based on Multi-fe...

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4th IEEE International conference on Image, vision and Computing, ICIVC 2019

作者： Xie, Kun Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Beijing100190 China

ISBN: (纸本)9781728123257

Remote sensing image registration technology has important significance in the field of image processing. the registration technology of SAR images has been a hot research in this field, and it is also a challenge. Aiming at this difficulty, this paper proposes a fusion feature algorithm, which combines gradient, grayscale, texture, geometry and other features in heterogeneous remote sensing images for comprehensive matching. Compared with the traditional single feature, the improved detector has good performance and registration accuracy in SAR image and visible light image registration. © 2019 IEEE.

关键词： Synthetic aperture radar

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Research on False Alarm Removal Method for Aircraft Target Detection under Small Sample Conditions 4

Research on False Alarm Removal Method for Aircraft Target D...

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4th IEEE International conference on Image, vision and Computing, ICIVC 2019

作者： Zeng, Qinghao Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Institute of Automation Beijing100190 China

ISBN: (纸本)9781728123257

Aiming at the aircraft target in visible light remote sensing image, this paper proposes a false alarm removal method for target detection under small sample training conditions. First, use data enhancement methods for small sample data to increase the number of samples and improve the generalization ability of features. then, according to the clear outline of the aircraft target and the obvious characteristics of the edge features, the Haar feature plus AdaBoost algorithm is used to preliminary detect the aircraft target. Finally, the convolutional neural network is used to extract the target features, and the preliminary detection results are classified to remove the false alarm targets and improve the accuracy of the detection. the experimental results show that the detection effect is good in aircraft target detection. © 2019 IEEE.

关键词： Errors

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Robust and efficient visual-inertial odometry with multi-plane priors 2nd

Robust and efficient visual-inertial odometry with multi-pla...

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2nd chinese conference on pattern recognition and computer vision, PRCV 2019

作者： Li, Jinyu Yang, Bangbang Huang, Kai Zhang, Guofeng Bao, Hujun State Key Lab of CAD&CG Zhejiang University ZJU-SenseTime Joint Lab of 3D Vision Hangzhou China

ISBN: (纸本)9783030317256

Planes commonly exist in a human-made scene and are useful for robust localization. In this paper, we propose a novel monocular visual-inertial odometry system which leverages multi-plane priors. A novel visual-inertial-plane PnP algorithm is introduced to use plane information for fast localization. the planes are expanded via a reprojection consensus-based way, which is robust to depth estimation error. A novel structureless plane-distance cost is used in sliding-window optimization, which allows to use a small size window while maintaining good accuracy. Together with modified marginalization and sliding window strategy, the computational cost is significantly reduced. Our VIO system is tested on various datasets and compared with several state-ofthe- art systems. Our system can achieve very competitive accuracy, and work pretty well on long and challenging sequences. Our system is also very efficient and can perform 30 fps averagely on an iPhone 7 mobile phone with a single thread. © Springer Nature Switzerland AG 2019.

关键词： computer vision

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ICDAR2019 robust reading challenge on multi-lingual scene text detection and recognition-RRC-MLT-2019 15

ICDAR2019 robust reading challenge on multi-lingual scene te...

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15th IAPR International conference on Document Analysis and recognition, ICDAR 2019

作者： Nayef, Nibal Liu, Cheng-Lin Ogier, Jean-Marc Patel, Yash Busta, Michal Chowdhury, Pinaki Nath Karatzas, Dimosthenis Khlif, Wafa Matas, Jiri Pal, Umapada Burie, Jean-Christophe L3i Laboratory University of la Rochelle France Computer Vision Center Universitat Autonoma de Barcelona Spain CVPR Unit Indian Statistical Institute India Robotics Institute Carnegie Mellon Universiry Pittsburgh United States Center for Machine Perception Department of Cybernetics Czech Technical University Prague Czech Republic National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781728128610

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. the real dataset consists of 20,000 images containing text from 10 languages. the challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. this paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. © 2019 IEEE.

关键词： Competition

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Binocular depth estimation using convolutional neural network with siamese branches

Binocular depth estimation using convolutional neural networ...

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2019 IEEE International conference on Robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. the first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. the second is a fully convolutional deep network, which reconstructions the depth map to original resolution. the two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

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DeepScores - A Dataset for Segmentation, Detection and Classification of Tiny Objects 24

DeepScores - A Dataset for Segmentation, Detection and Class...

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24th International conference on pattern recognition (ICPR)

作者： Tuggener, Lukas Elezi, Ismail Schmidhuber, Juegen Pelillo, Marcello Stadelmann, thilo ZHAW Datalab Winterthur Switzerland USI Lugano Switzerland Univ Venice Venice Italy ZHAW Winterthur Switzerland IDSIA Manno Switzerland

ISBN: (纸本)9781538637883

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small object recognition by placing the question of object recognition in the context of scene understanding. DeepScores contains high quality images of musical scores, partitioned into 300;000 sheets of written music that contain symbols of different shapes and sizes. With close to a hundred million small objects, this makes our dataset not only unique, but also the largest public dataset. DeepScores comes with ground truth for object classification, detection and semantic segmentation. DeepScores thus poses a relevant challenge for computer vision in general, and optical music recognition (OMR) research in particular. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets. Finally, we provide baseline performances for object classification, intuition for the inherent difficulty that DeepScores poses to state-of-the-art object detectors like YOLO or R-CNN, and give pointers to future research based on this dataset.

关键词： Object recognition

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Fully Convolutional Network for Head Detection with Depth Images 24

Fully Convolutional Network for Head Detection with Depth Im...

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24th International conference on pattern recognition (ICPR)

作者： Ballotta, Diego Borghi, Guido Vezzani, Roberto Cucchiara, Rita Univ Modena & Reggio Emilia DIEF Via P Vivarelli 10 I-41125 Modena Italy

ISBN: (纸本)9781538637883

Head detection and localization are one of the most investigated and demanding tasks of the computer vision community. these are also a key element for many disciplines, like Human computer Interaction, Human Behavior Understanding, Face Analysis and Video Surveillance. In last decades, many efforts have been conducted to develop accurate and reliable head or face detectors on standard RGB images, but only few solutions concern other types of images, such as depth maps. In this paper, we propose a novel method for head detection on depth images, based on a deep learning approach. In particular, the presented system overcomes the classic sliding-window approach, that is often the main computational bottleneck of many object detectors, through a Fully Convolutional Network. Two public datasets, namely Pandora and Watch-n-Patch, are exploited to train and test the proposed network. Experimental results confirm the effectiveness of the method, that is able to exceed all the state-of-art works based on depth images and to run with real time performance.

关键词： Task analysis Face Detectors Real-time systems Training computer vision

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2018 Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

2018 Joint 7th International Conference on Informatics, Elec...

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Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

ISBN: (纸本)9781538651612

the proceedings contain 103 papers. the topics discussed include: parking gate control based on mobile application;traffic analysis using decentralized social Internet of things;faster image compression technique based on LZW algorithm using GPU parallel processing;low latency image processing of transportation system using parallel processing co-incident multithreading (PPcM);neural network bayesian regularization backpropagation to solve inverse kinematics on planar manipulator;a convolutional autoencoder for detecting tumors in double contrast X-ray images;frontal gait recognition from incomplete RGB-D streams using gait cycle analysis;and understanding real time traffic characteristics of urban zones using GPS data: a computational study on Dhaka City

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Image recognition Based on Combined Filters with Pseudoinverse Learning Algorithm 15th

Image Recognition Based on Combined Filters with Pseudoinver...

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15th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Deng, Xiaodan Sun, Xiaoxuan Guo, Ping Yin, Qian Image Processing and Pattern Recognition Laboratory School of Systems Science Beijing Normal University Beijing100875 China Image Processing and Pattern Recognition Laboratory College of Information Science and Technology Beijing Normal University Beijing China

ISBN: (纸本)9783030198220

Deep convolution neural network (CNN) is one of the most popular Deep neural networks (DNN). It has won state-of-the-art performance in many computer vision tasks. the most used method to train DNN is Gradient descent-based algorithm such as Backpropagation. However, backpropagation algorithm usually has the problem of gradient vanishing or gradient explosion, and it relies on repeated iteration to get the optimal result. Moreover, with the need to learn many convolutional kernels, the traditional convolutional layer is the main computational bottleneck of deep CNNs. Consequently, the current deep CNN is inefficient on computing resource and computing time. To solve these problems, we proposed a method which combines Gabor kernel, random kernel and pseudoinverse kernel, incorporating with pseudoinverse learning (PIL) algorithm to speed up DNN training processing. With the multiple fixed convolution kernels and pseudoinverse learning algorithm, it is simple and efficient to use the proposed method. the performance of the proposed model is tested on MNIST and CIFAR-10 datasets without using GPU. Experimental results show that our model is better than existing benchmark methods in speed, at the same time it has the comparative recognition accuracy. © 2019, IFIP International Federation for Information Processing.

关键词： Image recognition

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