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IAPR International Workshop on Document Analysis Systems, DAS

作者： Partha Pratim Roy Umapada Pal Josep Lladós Fumitaka Kimura Computer Vision Center Universitat Autònoma Barcelona Bellaterra Spain Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India Graduate School of Engineering Mie University Mie Japan

In graphical documents (map, engineering drawing), artistic documents etc. there exist many printed materials where text lines are not parallel to each other and they are multi-oriented and curve in nature. For the OCR of such documents we need to extract individual text lines from the documents. Extraction of individual text lines from multi-oriented and/or curved text document is a difficult problem. In this paper, we propose a novel method to extract individual text lines from such document pages and the method is based on the foreground and background information of the characters of the text. To take care of background information, water reservoir concept is used here. In the proposed scheme at first, individual components are detected and grouped into 3-character clusters using their inter-component distance, size and positional information. Applying concept of graph, initial 3-character clusters are merged to have larger cluster group. Using inter-character background information, orientations of the extreme characters of a larger cluster are decided and based on these orientation, two candidate regions are formed from the cluster. Finally, with the help of these candidate regions, individual lines are extracted. From the experiment, we obtained encouraging result.

关键词： Data mining Water resources Reservoirs Optical character recognition software Image segmentation Character recognition Text analysis Information analysis pattern analysis pattern recognition

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Convex hull based approach for multi-oriented character recognition from graphical documents

Convex hull based approach for multi-oriented character reco...

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International Conference on pattern recognition

作者： Partha Pratim Roy Umapada Pal Josep Llados Fumitaka Kimura Computer Vision Center Universitat Autònoma De Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Mie Japan

ISBN: (纸本)9781424421749

In this paper, we present a scheme towards recognition of English character in multi-scale and multi-oriented environments. Graphical document such as map consists of text lines which appear in different orientation. Sometimes, characters in a single word may follow a curvilinear way to annotate the graphical curve lines. For recognition of such multi-scale and multi-oriented characters a Support Vector Machine (SVM) based scheme is presented in this paper. The feature used here is invariant to character orientation. Circular ring and convex hull have been used along with angular information of the contour pixels of the character to make the feature rotation invariant. We tested our proposed scheme on two different datasets. Combining circular and convex hull feature we have obtained 96.73% and 99.56% accuracy in these two datasets.

关键词： Character recognition pattern recognition Support vector machines Frequency Text recognition Histograms Clocks Rivers Optical character recognition software Testing

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Keyword spotting for self-training of BLSTM NN based handwriting recognition systems

Keyword spotting for self-training of BLSTM NN based handwri...

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作者： Frinken, Volkmar Fischer, Andreas Baumgartner, Markus Bunke, Horst Computer Vision Center Autonomous University of Barcelona Edifici O 08193 Bellaterra Barcelona Spain Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 CH-3012 Bern Switzerland

The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. © 2013 Elsevier Ltd. All rights reserved.

关键词： Character recognition

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Motion constraint patterns

Motion constraint patterns

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IEEE Workshop on Qualitative vision

作者： C. Fermuller Department for Pattern Recognition and Image Processing Institute for Automation Technical University of of Vienna Vienna Austria Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements.< >

关键词： Motion estimation computer vision Automation Image motion analysis Fluid flow measurement Motion measurement Rotation measurement Laboratories Educational institutions Geometrical optics

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Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Med-DANet V2: A Flexible Dynamic Architecture for Efficient ...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Haoran Shen Yifu Zhang Wenxuan Wang Chen Chen Jing Liu Shanshan Song Jiangyun Li School of Automation and Electrical Engineering University of Science and Technology Beijing Center for Research in Computer Vision University of Central Florida National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet [44]) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019. Code will be available at https://***/Rubics-Xuan/Med-DANet.

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Color constancy and non-uniform illumination: Can existing algorithms work?

Color constancy and non-uniform illumination: Can existing a...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

关键词： Image color analysis Lighting Databases Light sources Image segmentation Estimation Bayesian methods

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Tracking of human motion based on adaptive foreground segmentation and particle filter

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Journal of Test and Measurement Technology 2005年第3期19卷 320-326页

作者： Li, Wei Zhang, Yun-Hua Wang, Ya-Ming Research Center for Computer Vision and Pattern Recognition Zhejiang University of Sciences Hangzhou 310018 China

An approach to the tracking of trajectories of 3-D human motion from image sequence based on adaptive foreground segmentation and particle filter is proposed in this paper. First, the Gaussian model for image pixel is presented. Based on this, the adaptive segmentation of human body is finished using the information of difference image and the prior distribution of pixel density. Then, the tracking model under perspective imaging for body plane is established. Due to the fact that image function is nonlinear and the distribution of the noise in images is unknown, the particle filter based tacking is used. Finally, the 3-D trajectory of body plane is obtained. Experimental results show that 3-D trajectory of body plane can be effectively tracked, and the tracking results of particle filter is better than that of extended Kalman filter for this human motion tracking problem.

关键词： Image segmentation

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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture recognition

ChaLearn Looking at People RGB-D Isolated and Continuous Dat...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Jun Wan Stan Z. Li Yibing Zhao Shuai Zhou Isabelle Guyon Sergio Escalera National Laboratory of Pattern Recognition Chinese Academy of Sciences China Macau University of Science and Technology Macau UPSud and INRIA Université Paris-Saclay ChaLearn University of Barcelona Computer Vision Center ChaLearn

In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD) and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset (CGD) that has a total of more than 50000 gestures for the "one-shot-learning" competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences. Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for "user independent" gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented.

关键词： Gesture recognition Training Indexes computer vision Testing Conferences

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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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Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation

Box-driven Class-wise Region Masking and Filling Rate Guided...

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Chunfeng Song Yan Huang Wanli Ouyang Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) The University of Sydney SenseTime Computer Vision Research Group

ISBN: (纸本)9781728132945

Semantic segmentation has achieved huge progress via adopting deep Fully Convolutional Networks (FCN). However, the performance of FCN based models severely rely on the amounts of pixel-level annotations which are expensive and time-consuming. To address this problem, it is a good choice to learn to segment with weak supervision from bounding boxes. How to make full use of the class-level and region-level supervisions from bounding boxes is the critical challenge for the weakly supervised learning task. In this paper, we first introduce a box-driven class-wise masking model (BCM) to remove irrelevant regions of each class. Moreover, based on the pixel-level segment proposal generated from the bounding box supervision, we could calculate the mean filling rates of each class to serve as an important prior cue, then we propose a filling rate guided adaptive loss (FR-Loss) to help the model ignore the wrongly labeled pixels in proposals. Unlike previous methods directly training models with the fixed individual segment proposals, our method can adjust the model learning with global statistical information. Thus it can help reduce the negative impacts from wrongly labeled proposals. We evaluate the proposed method on the challenging PASCAL VOC 2012 benchmark and compare with other methods. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results.

关键词： bounding boxes filling rate Masking Semantics Supervision Regulated industries

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