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The equipment nameplate dataset for scene text detection and recognition

The equipment nameplate dataset for scene text detection and...

引用

2019 ieee International conference on Robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Zhang, Pu Guo, Lanqing Chen, Wenrui Chen, Chen Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly available on the website. © 2019 ieee.

关键词： computer vision

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Novel approach to real-time motion detection.

Novel approach to real-time motion detection.

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Proceedings - CVPR '88: computer society conference on computer vision and pattern recognition

作者： Lee, James S.J. Lin, C. Boeing Electronics High Technology Cent Seattle WA USA

ISBN: (纸本)0818608625

A real-time scheme is introduced that meets a need for fast, reliable, multiple-target motion detection without accurate object velocity or structure measurements. A multiresolution approach decomposes images into a pyramid with several spatial frequency bands to selectively detect motions of interest. Multiple moving targets are detected using a multiple window, coarse-to-fine focus of attention scheme to reconstruct motion energy and search for targets. Real-time sensor motion (translation and rotation) compensations use hierarchical correlation and minimum perturbation. Results are shown for multiple moving targets in FLIR (forward-looking infrared) image sequences from both stationary and moving sensors.

关键词： vision

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Dual Domain Auxiliary Particle Filter with Integrated Target Signature Update

Dual Domain Auxiliary Particle Filter with Integrated Target...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Colin M. Johnston Nick Mould Joseph P. Havlicek Guoliang Fan School of Electrical & Computer Engineering University of Oklahoma USA School of Electrical & Computer Engineering Oklahoma State University USA

ISBN: (纸本)9781424439942

For the first time, we formulate an auxiliary particle filter jointly in the pixel domain and modulation domain for tracking infrared targets. This dual domain approach provides an information rich image representation comprising the pixel domain frames acquired directly from an imaging infrared sensor as well as 18 amplitude modulation functions obtained through a multicomponent AM-FM image analysis. The new dual domain auxiliary particle filter successfully tracks all of the difficult targets in the well-known AMCOM closure sequences in terms of both centroid location and target magnification. In addition, we incorporate the template update procedure into the particle filter formulation to extend previously studied dual domain track consistency checking mechanism far beyond the normalized cross correlation (NCC) trackers of the past by explicitly quantifying the differences in target signature evolution between the modulation and pixel domains. Experimental results indicate that the dual domain auxiliary particle filter with integrated target signature update provides a significant performance advantage relative to several recent competing algorithms.

关键词： Particle filters Target tracking Particle tracking Infrared sensors Optical computing Infrared detectors Object detection Filtering Image representation Pixel

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Beyond local search: Tracking objects everywhere with instance-specific proposals

Beyond local search: Tracking objects everywhere with instan...

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2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Zhu, Gao Porikli, Fatih Li, Hongdong Australian National University Australia NICTA Australia ARC Centre of Excellence for Robotic Vision Australia

ISBN: (纸本)9781467388511

Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra lowframe-rate videos.

关键词： Object detection

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A new robust operator for computer vision: application to range data

A new robust operator for computer vision: application to ra...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Stewart Department of Computer Science Rensselaer Polytechnic Institute Troy NY

The basic MINPRAN (MINimize the Probability of RANdomness) technique, introduced by C.V. Stewart (1994), is extended to handle range data taken from complex scenes. Such data often includes: (1) a large numbers of outliers, (2) points from multiple surfaces interspersed over large image regions, and (3) extended regions containing only bad data. The initial version of MINPRAN handles cases (1) and (3). For (2), given an image region containing data from more than one surface, the basic technique tends to favor a single fit that "bridges" two surfaces. We analyze the extent of this problem and introduce two modifications to solve it. The new version of the algorithm, called MINPRAN2, produces extremely good results on difficult range data.< >

关键词： Machine vision Probability Minimization methods Surfaces

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Multiple Kernel Learning Based Modality Classification for Medical Images

Multiple Kernel Learning Based Modality Classification for M...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Viktor Gál Etienne Kerre Mike Nachtegael Department of Applied Mathematics and Computer Science Ghent University Ghent Belgium

ISBN: (纸本)9781467316118

Modality is a key facet in medical image retrieval, as a user is likely interested in only one of e.g. radiology images, flowcharts, and pathology photos. While assessing image modality is trivial for humans, reliable automatic methods are required to deal with large un-annotated image bases, such as figures taken from the millions of scientific publications. We present a multi-disciplinary approach to tackle the classification problem by combining image features, meta-data, textual and referential information. We test our system’s accuracy on the ImageCLEF 2011 medical modality classification data set. We show that using multiple kernel based classification, where the kernels are carefully selected for the different features, significantly increases the classification accuracy. Moreover, we demonstrate that by using linear support vector machine with explicit feature maps [35] of the selected kernels one can achieve comparable results to the (non-linear) kernel based one. Our best method achieves 88.47% accuracy and outperforms the state of the art.

关键词： Multiple Kernel Learning Modality Classification Medical Images Modality Seeds Support Vector Network image retrieval Kernel state of the art Medical imaging images Scientific Publication Medical Trivial based

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Multi-view reconstruction preserving weakly-supported surfaces

Multi-view reconstruction preserving weakly-supported surfac...

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作者： Jancosek, Michal Pajdla, Tomas Department of Cybernetics Faculty of Elec. Eng. Czech Technical University Prague Czech Republic

ISBN: (纸本)9781457703942

We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time1. © 2011 ieee.

关键词： computer vision

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Learning indexing functions for 3-D model-based object recognition

Learning indexing functions for 3-D model-based object recog...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Beis Lowe Department of Computer Science University of British Columbia Vancouver BC Canada

Indexing is an efficient method of recovering match hypotheses in model-based object recognition. Unlike other methods, which search for viewpoint-invariant shape descriptors to use as indices, we use a learning method to model the smooth variation in appearance of local feature sets (LFS). Indexing from LFS effectively deals with the problems of occlusion and missing features. The indexing functions generated by the learning method are probability distributions describing the possible interpretations of each index value. During recognition, this information can be used to select the least ambiguous features for matching. A verification stage follows so that the final reliability and accuracy of the match is greater than that from indexing alone. This approach has the potential to work with a wide range of image features and model types.< >

关键词： Object recognition

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Deformable boundary finding influenced by region homogeneity

Deformable boundary finding influenced by region homogeneity

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chakraborty Staib Duncan Departments of Electrical Engineering and Diagnostic Radiology Yale University New Heaven CT USA

Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation and boundary finding, often suffer from a variety of limitations. We propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. Our approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a grey-level-gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity.< >

关键词： Image region analysis Machine vision

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Distance Guided Selection of the Best Base Classifier in an Ensemble with Application to Cervigram Image Segmentation

Distance Guided Selection of the Best Base Classifier in an ...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Wei Wang Xiaolei Huang Department of Computer Science and Engineering Lehigh University

ISBN: (纸本)9781424439942

We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers. By generating an ensemble of base classifiers trained independently on separate training images, we can use the distance metric to select those classifiers in the ensemble whose decision boundaries are similar to that of an unknown test image. In an extreme case, we select the base classifier with the most similar decision boundary to accomplish classification and segmentation on the test image. Our approach is novel in the way that the nearest neighbor is picked and effectively solves classification problems in which base classifiers with good overall performance are not easy to construct due to a large variation in the training examples. In our experiments, we applied our method and several popular ensemble methods to segmenting acetowhite regions in cervical images. The overall classification accuracy of the proposed method is significantly better than that of a single classifier learned using the entire training set, and is also superior to other ensemble methods including majority voting, STAPLE, Boosting and Bagging.

关键词： Image segmentation Support vector machines Support vector machine classification Biomedical optical imaging Shape measurement Testing Lesions Application software computer science Learning systems

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