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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1643 条记录，以下是131-140 订阅

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A Fast Contour Model-based Localization Method for Robotic Picking in Shrimp Production Line 18

A Fast Contour Model-based Localization Method for Robotic P...

引用

3rd international conference on Robotics, Control and Automation (ICRCA) / 3rd international conference on Robotics and Machine vision (ICRMV)

作者： Chen, Niya Ruan, Jiayang Yang, Wei ABB Corp Res 103 BEZ IT Pk Beijing Peoples R China ABB Engn Shanghai Ltd 4528 Kangxin Highway Shanghai Peoples R China

ISBN: (纸本)9781450365307

Irregular-shape food processing by robotic arms like shrimp picking is a common problem in industrial automation, which can be summarized as localization of particular points on an image, emphasizing on both good accuracy and high speed with relatively very limited hardware resources. In most cases, the points do not have a distinct visual characteristic in color or size. In this paper, first we outline the suspicious search range resorting to intelligently learning the coarse mapping function between shrimp shape and target points, based on the proposed contour model of shrimp body, which significantly simplifies numerical representation of the original image. Next, priori knowledge of the shrimp body is used for more accurate fine localization of the target points. More specifically, in this step, the shrimp body pose is normalized for edge extraction after proper rotation and projection. The extracted edge curve on the back of the shrimp is then analyzed to accurately pick out the target corner point. During validation, in the search region detection step, the method is able to efficiently avoid wrong search in neighboring joints of shrimp body. After finer localization of the target points, the final detection rate turns out to be 93%.

关键词： Robot vision robot food picking contour model neural network

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A geometric model of spatial distortions in virtual and augmented environments 3

A geometric model of spatial distortions in virtual and augm...

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3rd IEEE international conference on image processing, Applications and Systems (IPAS)

作者： Chessa, Manuela Solari, Fabio Univ Genoa Dept Inf Bioengn Rob & Syst Engn DIBRIS Genoa Italy

ISBN: (纸本)9781728102474

The 3D structure of a virtual or augmented scene is affected by distortions that can affect the natural fruition of the experience. Here, we consider the distortions of the spatial layout of virtual scenes, specifically the underestimation of egocentric distances, as experienced by users. The aim of our work is to develop a computer vision based modeling that is able to describe such an underestimation. In particular, we propose that the spatial misperception has origin from the fact that the world is seen by a user through an uncalibrated camera, thus producing a virtual view of a distorted space for him/her. The proposed geometric model is able to explain the experimental evidence of the egocentric distance underestimation that is reported in the literature.

关键词： Human focused analysis 3D computer vision egocentric distance perception head-mounted display video see-through

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Automatic Caption Generation for Medical images 18

Automatic Caption Generation for Medical Images

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3rd international conference on Smart City Applications (SCA')

作者： Allaouzi, Imane Ben Ahmed, M. Benamrou, B. Ouardouz, M. Abdelmalek Essaadi Univ LIST FSTT Tangier Morocco Abdelmalek Essaadi Univ MMC FSTT Tangier Morocco

ISBN: (纸本)9781450365628

With the increasing availability of medical images coming from different modalities (X-Ray, CT, PET, MRI, ultrasound, etc.), and the huge advances in the development of incredibly fast, accurate and enhanced computing power with the current graphics processing units. The task of automatic caption generation from medical images became a new way to improve healthcare and the key method for getting better results at lower costs. In this paper, we give a comprehensive overview of the task of image captioning in the medical domain, covering: existing models, the benchmark medical image caption datasets, and evaluation metrics that have been used to measure the quality of the generated captions.

关键词： Medical image Captioning Deep Neural Networks LSTM CNN RNN Generative models Retrieval-based models Encoder-Decoder framework Attention mechanism computer vision Natural Language processing

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Performance Comparison of Typical Spatial Domain image Enhancement for a Mobile Robot in Low Contrast Environment 3

Performance Comparison of Typical Spatial Domain Image Enhan...

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3rd international conference on Robotics and Automation Engineering (ICRAE)

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

ISBN: (纸本)9781538695944

images could be understood and analyzed more easily through enhance processing. Generally, image enhancement can be divided into spatial domain enhancement and frequency domain enhancement according to different processing domains. Yet compared with frequency domain processing, spatial domain enhancement is more easily to realize real-time enhancement of images, namely, spatial domain processing is easier to satisfy the real-time requirements of applications in practical engineering. While, real-time is critical to robots for performing operations. Hence, this paper devotes to exploring performance of four typical spatial domain enhancement algorithms for a mobile robot operating in low contrast environment. More precisely, fundamental theory of contrast stretch, histogram equalization, histogram matching, and local histogram are overviewed. Furthermore, a mobile robot system is briefly introduced. Finally, in condition of low contrast, performance of above four algorithms are compared in mobile robot via experiments. This work will provide a basic reference for further research on image enhancement in robot vision.

关键词： image enhancement spatial domaint enhancement performance mobile robot low contrast

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Research on Question Answering System Based on BERT Model 3

Research on Question Answering System Based on BERT Model

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3rd international conference on computer vision, image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yin, Jie Jiangsu University of Science and Technology School of Computer ZhenJiang China

ISBN: (纸本)9781665459112

The core of natural language processing is the science of how computers understand and respond to the influence of human language. This is also the main research direction in the field of machine intelligence development. Question answering system is one of the development directions. How to make the answer of question answering system more intelligent is the focus of research in recent years. Based on this, this thesis proposes a question answering system based on the BERT model that combines the co-attention mechanism and the self-attention mechanism. In order to better show the representation of questions and articles at their respective semantic levels, this article will choose BERT language to encode them. Attention and fusion are then performed horizontally and vertically at different levels of granularity between questions and paragraphs. Experiments show that the model has an average EM of 46.56 and an average F1 of 58.90 on all datasets. The average F1 for the development dataset is 54.85, and the average F1 for the test dataset is 62.95. The content answered by the question answering system based on the BERT model is basically reasonable, and the intelligence of the question answering system is effectively improved. © 2022 IEEE.

关键词： Semantics

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Automatic Classification of Natural Stone Tiles with computer vision 3

Automatic Classification of Natural Stone Tiles with Compute...

引用

3rd international conference on computer Science and Engineering (UBMK)

作者： Kaynar, Oguz Torun, Yunis Temiz, Mustafa Gormez, Yasin Cumhuriyet Univ Iktisadi Idari Bilimler Fak Yonetim Bilisim Sistemleri Sivas Turkey Cumhuriyet Univ Muhendislik Fak Elekt Elekt Muhendisligi Sivas Turkey

ISBN: (纸本)9781538678930

Classification in natural stone industry have a great importance for enterprises. There are reinstatement cases arising from the fact that ordered granite parties are not the same as the agreed sample parties at the beginning, which causes significant economic losses for the companies. There is a greater need to classify tiles using computer-aided image processing methods for the development of quality control processes that have become increasingly important due to the rapidly increasing competition and globalization in the natural stone industry. In this type of automatic systems, the attributes that give information about color and surface are extracted from the images of natural stone tiles with image processing techniques and then the data set obtained by using these attributes are classified by various artificial intelligence and data mining techniques. In this study, a classification was made on a dataset consisting of 996 pictures of natural stone tiles from six categories obtained from a natural stone producer (Beta Mermer I. C.) operating in Sivas. Gray level co-occurrence matrix (GLCM) and local binary pattern (LPB) are used to obtain pattern information of granite tiles. Several statistics related to each color channel were used to obtain color information of granites. Various datasets are created using only pattern information and combination of pattern and color information of tiles. Subsequently, classification performance of these datasets are compared using several algorithms such as, artificial neural networks, support vector machines, and naive bayes.

关键词： natural stone classification grey level cooccurence matrix local binary pattern

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CNN Design for Real-Time Traffic Sign Recognition 3

CNN Design for Real-Time Traffic Sign Recognition

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3rd international conference on Information Technology and Nanotechnology (ITNT)

作者： Shustanov, Alexander Yakimov, Pavel Samara Natl Res Univ Moskovskoye Shosse 34 Samara Russia

Nowadays, more and more object recognition tasks are being solved with Convolutional Neural Networks (CNN). Due to its high recognition rate and fast execution, the convolutional neural networks have enhanced most of computer vision tasks, both existing and new ones. In this article, we propose an implementation of traffic signs recognition algorithm using a convolution neural network. The paper also shows several CNN architectures, which are compared to each other. Training of the neural network is implemented using the TensorFlow library and massively parallel architecture for multithreaded programming CUDA. The entire procedure for traffic sign detection and recognition is executed in real time on a mobile GPU. The experimental results confirmed high efficiency of the developed computer vision system. (C) 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 3rd international conference "Information Technology and Nanotechnology".

关键词： TensorFlow Convolutional Neural Networks Traffic Sign Recognition image processing computer vision Mobile GPU

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Study of Target Tracking and Recovery with Low Resolution image Series 3

Study of Target Tracking and Recovery with Low Resolution Im...

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3rd IEEE international conference on computer and Communications (ICCC)

作者： Bao, Xueting Zhan, Hongchen Yuan, Jie Nanjing Univ Sch Informat Sci & Engn Jinling Coll Nanjing 210089 Jiangsu Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Jiangsu Peoples R China

ISBN: (纸本)9781509063529

Object tracking is one of the most important tasks in computer vision. It is aimed to track and identify the targets in every frame automatically, thus making it possible extracting the targets in the frames for the post-processing. The described method encompasses two important issues in image tracking system that have received increased attention in the few years: moving object tracking and object recognition. It is based on kernel algorithm and due to the continuously changing background and moving targets, object tracking has been a challenging task We review strategies under kernel algorithm and successfully track and restore the image.

关键词： object tracking image restoration kernel algorithm ensemble tracking image extraction

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An Application of Numbers and Characters Recognition and Classification on Radar images Using for Flood Monitoring 3

An Application of Numbers and Characters Recognition and Cla...

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3rd international conference on computer and Communication Systems (ICCCS)

作者： Deeprasertkul, Prattana Praikan, Worawit Minist Sci & Technol Hydro & Agro Informat Inst Bangkok Thailand

ISBN: (纸本)9781538663509

The rainfall monitoring from Radar images in any areas of Thailand is the one task for the flood monitoring system. Optical Character Recognition or OCR technique was applied to recognize these text images and convert them into editable text. The propose of this work is to develop a text image recognition program which can identify the date and time in images for collecting and arranging the Radar images in archive. Because of the editable date and time texts are able to replace in the original image filename.

关键词： template matching text image Radar image recognition image processing

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Research Analysis of Stream processing System Storm Based on Knowledge Map 3

Research Analysis of Stream Processing System Storm Based on...

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3rd international conference on computer vision, image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Lina Lian, Peng Zhang, Tingting Li, Nianfeng Changchun University College of Computer Science and Technology Changchun China Changchun University College of Cyber Security Changchun China

ISBN: (纸本)9781665459112

With the development of the Internet of Things and 5G technology, stream processing systems are more and more widely used, especially the open source Storm platform. A comprehensive understanding of the research status and progress of Storm is of a certain reference value for the academic and industry to carry out related work. Based on bibliometrics theory, this paper takes the citation index of CNKI (China national knowledge infrastructure) and WOS (web of science) databases from 2013 to 2021 as the data source, and the relevant literature of Storm research and application as the research object, which is visualized and analyzed by the CiteSpace software. The knowledge maps are drawn from the aspects of temporal and spatial distribution of literature, distribution of authors and institutions, keyword co-occurrence, citation frequency and keyword prominence, and the research status, research basis, research hotspots and research trends of Storm at home and abroad are analyzed. The research results can provide references for the follow-up theoretical and application research of Storm system. © 2022 IEEE.

关键词： Storms

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