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检索条件"任意字段=Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"

共 557 条记录，以下是91-100 订阅

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 11

Anomaly Handwritten Text Detection for Automatic Descriptive...

引用

11th International conference on Computing and Pattern Recognition, ICCPR 2022

作者： Chatterjee, Nilanjana Shivakumara, Palaiahnaakote Pal, Umapada Lu, Tong Lu, Yue Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. this paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. the method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. this result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. the distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. the proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). the results on different datasets show that the proposed work outperforms the existing methods. © 2022 ACM.

关键词： Students

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Real-time Multi Pose Trajectory Tracking based on OpenPose Keypoints 11

Real-time Multi Pose Trajectory Tracking based on OpenPose K...

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11th IEEE International conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Surowka, Adam Cracow Univ Technol Fac Elect & Comp Engn Ul Warszawska 24 PL-31155 Krakow Poland

ISBN: (纸本)9781665426053

Currently, there exist several open-source computer vision libraries designed for human pose estimation from photos and videos. they are mainly focused on the possibility of detecting individuals in the image and returning their skeleton determinants. An often overlooked or underdeveloped functionality is tracking the trajectory of the detected people, which presents a serious problem in the process of design automated video surveillance systems. In this work, the author attempts to develop an algorithm for tracking multiple human poses in real-time, based on simple decision filters. the developed solution is designed to work with keypoints obtained from a selected open-source human poses recognition library. the author reveals details related to the method of processing and analysing obtained keypoints, describes the concept of decision filters and presents the results of the software implementation.

关键词： automated surveillance system human pose recognition human pose tracking body posture analysis computer vision data analysis

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computer-aided Selection of the Optimal Gamma Correction Parameter for image Enhancement in Video Analytics Systems 11

Computer-aided Selection of the Optimal Gamma Correction Par...

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11th IEEE International conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Golovin, Oleksandr NAS Ukraine VM Glushkov Inst Cybernet Glushkov Ave 40 UA-03187 Kiev Ukraine

ISBN: (纸本)9781665426053

Video analytics systems are rapidly evolving, and the effectiveness of their work depends on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. A novel method for determining the optimal value of a gamma correction parameter, which ensures the selection of the image of the best quality in automatic mode is represented in the paper. the method is based on the use of gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. the technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.

关键词： gamma correction image enhancement video analytics system gamma parameter histogram computer vision segmentation

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Camera Self-Calibration Using Linear Features from Targets 11

Camera Self-Calibration Using Linear Features from Targets

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11th International conference on graphics and image processing (ICGIP)

作者： Zhang, Longzhi Wu, Dongmei Harbin Inst Technol State Key Lab Robot & Syst Harbin Peoples R China

ISBN: (纸本)9781510635241

Objective of camera calibration is to provide a transformation between object space and image space in practical computer vision tasks. thus, in order to acquire the accurate and reliable information on visual image, intrinsic and extrinsic parameters of camera should be precisely calibrated. Compared with traditional methods and active vision based methods, self-calibration methods are not rely on prior information of visual scenes, which have more widely application prospects. However, in spite of self-calibration has been studied for years, and multitudes of ideas have been proposed, current research difficulties on self-calibration methods still lies in calibration precision. this paper put forward a camera self-calibration method only using linear features from targets, which could calibrate camera parameters in a highly accuracy. Moreover, an experiment is implemented to verify the effectiveness of our method, simultaneously, the experimental result illustrate that the proposed method has a highly calibration precision.

关键词： Camera self-calibration parameters linear features calibrate precision computer vision

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Object 6DoF Pose Estimation for Power Grid Manipulating Robots 11th

Object 6DoF Pose Estimation for Power Grid Manipulating Robo...

引用

11th International conference on image and graphics, ICIG 2021

作者： Du, Shan Zhang, Xiaoye Li, Zhongliang Yue, Jingpeng Zou, Qin School of Computer Science Wuhan University Wuhan China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China China Southern Power Grid Technology Co. Ltd. GuangZhou China

ISBN: (纸本)9783030873578

this paper introduces a six degree-of-freedom (6DoF) pose estimation method for manipulating robots to construct a robust machine-vision system. Generally, 2DoF results obtained by traditional object detectors cannot meet the requirements of manipulating operations, where the posture of targets are additionally needed. Meanwhile, due to the sensitivity to light and the limitation to distance, the depth sensor of RGB-D cameras could not always be reliable. To overcome these problems, we study 6DoF pose estimation from a single RGB image. To reduce the complexity and computation, we divide the task into four stages, i.e., data collection and pre-processing, instance segmentation, keypoints prediction, and 2D-to-3D projection. We build the model with deep neural networks, and test it in practical manipulating tasks. the experimental results demonstrate the high accuracy and practicality of our method. © 2021, Springer Nature Switzerland AG.

关键词： Robots

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In Situ Visual Quality Control in 3D Printing 15

In Situ Visual Quality Control in 3D Printing

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15th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP) / 11th International conference on Information Visualization theory and Applications (IVAPP)

作者： Kopsacheilis, Charalampos Charalampous, Paschalis Kostavelis, Ioannis Tzovaras, Dimitrios Ctr Res & Technol Informat Technol Inst CERTH ITI 6th Km Charilaou Thermi Rd Thessaloniki 57001 Greece

ISBN: (纸本)9789897584022

In the past decade, additive manufacturing technology has gained an immense attention in numerous research areas and has already been adopted in a wide range of industries relevant to transportation, healthcare, electronics and energy. However, the presence of defects and dimensional deviations that occur during the process hinder the broad exploitation of 3D printing. In order to enhance the capabilities of this emerging technology, online quality control methodologies and verifications of the manufacturing process are necessary to be developed. In the present article, a low cost in-situ vision-based monitoring technique applied in Fused Deposition Modeling (FDM) 3D printing technology is introduced. An optical scanning system was integrated in a commercial 3D Printer in order to scan and validate the performance of the procedure. the proposed methodology monitors the FDM process and correlates the theoretical 3D model with the manufactured one. this technique can be utilized in various additive manufacturing technologies providing integrity and reliability of the process, high quality standards and reduced production costs.

关键词： 3D Printing Additive Manufacturing Quality Control Optical Monitoring image processing

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musiXplora: Visual Analysis of a Musicological Encyclopedia 15

musiXplora: Visual Analysis of a Musicological Encyclopedia

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作者： Khulusi, Richard Kusnick, Jakob Focht, Josef Janicke, Stefan Univ Leipzig Image & Signal Proc Grp Leipzig Germany Univ Leipzig Inst Musicol Museum Mus Instruments Leipzig Germany Univ Southern Denmark Dept Math & Comp Sci Odense Denmark

ISBN: (纸本)9789897584022

Making large sets of digitized cultural heritage data accessible is a key task for digitization projects. While the amount of data available through print media is vast in humanities, common issues arise as information available for the digitization process is typically fragmented. One reason is the physical distribution of data through print media that has to be collected and merged. Especially, merging causes issues due to differences in terminology, hampering automatic processing. Hence, digitizing musicological data raises a broad range of challenges. In this paper, we present the current state of the on-going musiXplora project, including a multi-faceted database and a visual exploration system for persons, places, objects, terms, media, events, and institutions of musicological interest. A particular focus of the project is using visualizations to overcome traditional problems of handling both, vast amounts and anomalies of information induced by the historicity of data. We present several use cases that highlight the capabilities of the system to support musicologists in their daily workflows.

关键词： Biography Visualization Music Visualization Digital Humanities

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Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

Anomaly Handwritten Text Detection for Automatic Descriptive...

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proceedings of the 2022 11th International conference on Computing and Pattern Recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

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Semi-supervised image Classification via Attention Mechanism and Generative Adversarial Network 11

Semi-supervised Image Classification via Attention Mechanism...

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11th International conference on graphics and image processing (ICGIP)

作者： Xiang, Xuezhi Yu, Zeting Lv, Ning Kong, Xiangdong Saddik, Abdulmotaleb Ei Harbin Engn Univ Sch Informat & Commun Engn Harbin 150001 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781510635241

image classification plays a vital role in the field of computer vision. Many existing image classification methods with high accuracy are based on supervised learning, which requires a great number of labeled images. However, the labeling of images requires a lot of human and material resources. In this paper, we focus on semi-supervised image classification, which can build a classifier using a few labeled images and plenty of unlabeled images. We propose an attention-based generative adversarial network (GAN) for semi-supervised image classification, which can capture global dependencies and adaptively extract important information. Furthermore, we apply spectral normalization, which can stabilize the training of attention-based GAN. the experimental results obtained with the CIFAR-10 dataset show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.

关键词： Semi-supervised image classification Generative adversarial network Self-attention

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Combining image and Caption Analysis for Classifying Charts in Biodiversity Texts 15

Combining Image and Caption Analysis for Classifying Charts ...

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作者： Kaur, Pawandeep Kiesel, Dora Friedrich Schiller Univ Heinz Nixdorf Chair Distributed Informat Syst Jena Germany Bauhaus Univ Fac Media VR Grp Weimar Germany

ISBN: (纸本)9789897584022

Chart type classification through caption analysis is a new area of study. Distinct keywords in the captions that relate to the visualization vocabulary (e.g., for scatterplot: dot, y-axis, x-axis, bubble) and keywords from the specific domain (e.g., species richness, species abundance, phylogenetic associations in the case of biodiversity research), serve as parameters to train a text classifier. For better chart comprehensibility, along with the visual characteristics of the chart, a classifier should also understand these parameters well. Such conceptual/semantic chart classifiers then will not only be useful for chart classification purposes but also for other visualization studies. One of the applications of such a classifier is in the creation of the domain knowledge-assisted visualization recommendation system, where these text classifiers can provide the recommendation of visualization types based on the classification of the text provided along with the dataset. Motivated by this use case, in this paper, we have explored our idea of semantic chart classifiers. We have taken the assistance of state-of-the-art natural language processing (NLP) and computer vision algorithms to create a biodiversity domain-based visualization classifier. With an average test accuracy (F1-score) of 92.2% over all 15 classes, we can prove that our classifiers can differentiate between different chart types conceptually and visually.

关键词： Caption Analysis Chart Classification Natural Language processing NLP Caption Classification Visualization Recommendation Chart Recognition

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