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UAV photogrammetry for generating 3D campus model

AIP conference Proceedings 2019年第1期2097卷

作者： Muliady Erwani Merry Sartika Cindrawaty Lesmana Elizabeth 1Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia 2Department of Civil Engineering Universitas Kristen Maranatha Bandung Indonesia 3Department of Visual Communication Design Universitas Kristen Maranatha Bandung Indonesia

Unmanned Aerial Vehicle (UAV) is increasingly popular as platforms for inspection, professional mapping, and modeling issues. UAV as a low-cost alternative to the classical manned aerial photogrammetry can survey buildings of various heights to take the necessary angle for a favorable photo. This paper presents results of using 3D reconstruction technique based on 2D pictures of a building taken by UAV. Automation and feasible image orientation create images to be processed in the latest developments of UAV image processing methods for photogrammetric applications, mapping, and 3D modeling issues. Images from a campus building construction in Bandung was taken for a case study using UAV with an attached camera. The images were processed to generate a 3D model of campus building using several integrated software. The integrated software was used to create image processing, mapping, orthophoto, and meshing. By using the proposed setting of altitude, overlapping percentage, direction of flight, the results show good quality of regenerated images which contain information of altitude and position of the building.

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Converting Image into Bas Reliefs Using Image processing Techniques

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Journal of Physics: conference Series 2019年第1期1196卷 012037-012037页

作者： Suciati, Nanik Baihaqi, Maserati Teja Handayani Tjandrasa Agus Zainal Arifin Chastine Fatichah Yuniarti, Anny Dini, Adni Navastara Wijaya, Arya Yudhi Wijayanti Nurul Khotimah Henning Titi Ciptaningtyas Department of Informatics Faculty of Information and Communication Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

In this study, we develop an application to convert an image into bas reliefs, a kind of 3D sculpture on a flat surface, by using image processing techniques. First, the color image is converted into gray image, and then is used as a base surface in xy-coordinate. Two different methods for determining height in z-coordinate provides two different carving styles. In the first method, the height is specified by using gray value of the blurred image, while the second specifies the height by using gray value of the dilation of the edge image. Frame effect is then created by adding a flat plane around the carving. The experiment shows that the application can transfer visual contents of the image into the bas reliefs and also can generate artistic reliefs model easily.

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S2S translator using CNN

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AIP conference Proceedings 2023年第1期2492卷

作者： Jeethu Philip Y. Lakshmi Prasanna G. Kiran Kumar P. Neelakanta Rao Department of Information Technology MLR Institute of Technology Hyderabad India Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

Sign language is a language used by dumb and deaf people to communicate with normal people. Normal people use sounds, unlike them, this language uses visual communication to convey the thoughts of dumb and deaf people. Sign language is achieved by continuously showing hands, the orientation of fingers, and facial expressions. In this project, we will develop a programmatic model that converts voice to sign language and also sign language to voice/text. we may be using different APIs (python modules or Google API) and natural language processing semantics to break the text into a large number of smaller understandable words which require machine learning as a part. Predefined alphabet signs are given as inputs to the model. So this can use Artificial Intelligence technology to translate audio into sign language and sign language to text.

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Research on Intelligent Control and Integration Technology for visual Inspection of TEC Components

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Journal of Physics: conference Series 2021年第1期2101卷

作者： Zhishan Cai Jiangwei Chen Shaobin Yan Shizhan Li Tingdi Liao School of Physics and Information Engineering Quanzhou Normal University Quanzhou 362000 Fujian China. Photonic Technology Research Center of Quanzhou Normal University Quanzhou 362000 Fujian China.

Technical schemes such as uniform TEC component feeding, posture adjustment, multi-station positioning, and communication between upper and lower machines were designed. The opto-electromechanical integration mechanism was integrated by software to realize the detection of TEC component surface defects, and qualified TEC components were separated from unqualified TEC components by blow sieving. Adopt ARM MCU for real-time control; Image recognition adopts the machine vision deep learning platform based on linux to learn and recognize the TEC components, and sends the results to the lower computer for sieving. The accuracy of triggering the camera to take pictures is 99.9%, which provides a guarantee for capturing and processing images accurately. The accuracy of blow sieving is over 99.9%, which can meet the sieving requirements. The electrical control system of the whole machine can meet the motion control requirements of detection.

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Design and Implementation of Lifting Wavelet Transform Using Field Programmable Gate Arrays

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IOP conference Series: Materials Science and Engineering 2020年第1期767卷

作者： T B Taha R Ngadiran P L Ehkan IT Department Faculty of Sciences Tishk International University Erbil Iraq School of Computer and Communication Engineering University Malaysia Perlis (UniMAP) Perlis Malaysia

Lifting Wavelet transform (LWT) has an extensive usage in different image processing applications as image compression and information hiding. LWT is considered a good solution for hardware designs as it relies only on integer calculations while applying the wavelet transform. In this paper, an FPGA design and implementation of LWT is presented, the implementation is achieved using VHDL coding without importing off-shelf components which make the proposed design applicable to a wide range of devices. The design is based on parallel execution to perform LWT implementation with real time response. The design utilized 421 logic registers of DE2 Cyclone ii (EP2C35F672C6) FPGA device with 151.91MHz frequency.

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Yolk color measurement using image processing and deep learning

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IOP conference Series: Earth and Environmental Science 2021年第1期686卷

作者： C Kaewtapee A Supratak Department of Animal Science Faculty of Agriculture Kasetsart University 50 Wgam Wong Wan Rd. Latyao Chatuchak Bangkok 10900 Thailand Computer Science Academic Group Faculty of Information and Communication Technology Mahidol University 999 Phuttamonthon 4 Road Salaya Nakhon Pathom 73170 Thailand

A high yellow yolk color of laying hens is required by customer. As yolk color measurement is determined by visual perception, color score may be expressed differently. The objective of this study was to develop the recognition of yolk color using red green blue (RGB) image and deep learning. The three hundred and fifty-three RGB images were obtained. The rectified linear unit (ReLU) and softmax were used as the activation function. An optimizer was configured with Adam, and categorical crossentropy was used as a loss function. The results showed that the loss had decreased to 0.45 and 0.63, whereas the accuracy had increased and reached 0.80 and 0.76 for training dataset and testing dataset, respectively. For evaluation, the loss value was 0.27 and 0.63, whereas the accuracy value was 0.90 and 0.76 for training dataset and testing dataset, respectively. The average f1-score was 0.76, whereas the highest precision (1.00) was observed in color score 5, 6 and 8. In conclusion, RGB image can be used as an alternative method to classify yolk color score with lower cost of analysis for egg producers in the near future.

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Artificial intelligence based defect classification for weld joints

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IOP conference Series: Materials Science and Engineering 2018年第1期402卷

作者： S Esther Florence R Vimal Samsingh Vimaleswar Babureddy Department of Electronics and Communication Engineering SSN College of Engineering Kalavakkam Chennai Tamilnadu - 603110 India - 603110 Department of Mechanical Engineering SSN College of Engineering Kalavakkam Chennai Tamilnadu- 603110 India - 603110

This paper mainly deals with the development of a defect classification system that uses Artificial Neural Network (ANN) to classify weld defects based on ultrasonic test data. The system enables real-time identification of weld defects which finds application in testing of critical welding applications and also reduces dependency on skilled workforce for the function. The study mainly consists of three parts- (i) Weld defect detection using Ultrasonic Testing (UT) (ii) Implementation of ANN (iii) Defect classification. An ultrasonic test performed on welded samples shows different results for welds with and without defects and further between defects as well. The ultrasonic test data is fed into the ANN algorithm to train it to identify the various weld defects. An Artificial Neural Network (ANN) is an information processing paradigm that uses a large number of highly interconnected processing elements called neurons, working in unison to solve the specific problems. There are two types of neural network architectures that are used for classification - a back propagation network (BPN) and a probabilistic neural network (PNN). Back propagation network has been used for the purpose of this study. In order to test the performance of the back propagation neural network, four classes of defect namely porosity, lack of side wall fusion, lack of penetration and slag inclusion are considered.

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POS0978 EXPLORING SMARTPHONE-BASED DIGITAL ENDPOINTS FOR RHEUMATIC CONDITIONS

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Annals of the Rheumatic Diseases 2023年 82卷 805-805页

作者： R. Araújo P. Matias P. Studenic M. Valada R. Graca N. Nakhost Lotfi D. Belo F. Nunes Fraunhofer Portugal AICOS Research Center for Assistive Information and Communication Solutions Porto Portugal Medical University of Vienna Internal Medicine 3 Division of Rheumatology Wien Austria Karolinska Institute Medicine (Solna) Division of Rheumatology Stockholm Sweden Nova Medical School EpiDoC Unit CHRC Lisbon Portugal

Background The care of rheumatic diseases is currently episodic, based on visits every 3-6 months at the rheumatologist, which may fail to characterize the condition state. To address this issue, the research community is investigating ways to use smartphones and sensors to monitor conditions passively. Objectives Explore associations between smartphone-generated data, standardized functional tests, and Patient Reported Outcome Measures (PROMS) to support the creation of digital endpoints. Methods Participants from Portugal and Austria participated in a Data Collection that included: (i) physical activities, such as walking with a smartphone in the pocket; (ii) hand dexterity exercises, such as copying text sentences with the smartphone keyboard; (iii) downloading and processing sociability data from the participants' smartphone; (iv) performing functional tests, such as Moberg Pickup Test (Moberg) and Timed Up-and-Go (TUG); and (v) answering validated PROMS, such as MD-HAQ, EQ-5D-5L, and visual analogue scale (VAS) for pain, fatigue, and disease activity. Statistical analysis focused on the correlation between smartphone-collected data and functional tests or PROMs, and independent t-test for between-group comparison. Results We collected data from 59 participants (76% female, 24% male). From this set, 31 were patients diagnosed with osteoarthritis (45%), rheumatoid arthritis (26%), or psoriatic arthritis (29%). The remaining 28 were age-matched healthy controls. In terms of age, 17% of participants were under 41 years old, 52% were between 41 and 60, and 31% were 61 or older. Most patients reported stiffness or pain in the upper (90%) and lower (83%) parts of the body. Subjective health status was high (M=77.95, SD= 16.31) in the VAS of EQ-5D-5L. Independent t-tests (Table 1) showed significant differences between patient and control groups regarding Mobility (M= 1.90, SD= 0.77), Pain/Well-Being (M= 2.48, SD= 0.81), and Mental Health (M= 1.68, SD=0.83) with higher

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Two New Initiatives: Promoting communication among Those Interested in Early Maps

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Imago Mundi 2014年第2期66卷 299页

作者： page 299View full textDownload full textAccess optionsDOI:10.1080/03085694.2014.902620Published online: 27 May 2014Citing articles: 0Article Views: 3Article usage statistics combine cumulative total PDF downloads full-text HTML views from publication date (but no earlier than 25 Jun 2011 launch date of this website) to 14 Jun 2014. Article views are only counted from this site. Although these data are updated every 24 hours there may be a 48-hour delay before the most recent numbers are available.Article usage statistics combine cumulative total PDF downloads full-text HTML views from publication date (but no earlier than 25 Jun 2011 launch date of this website) to 14 Jun 2014. Article views are only counted from this site. Although these data are updated every 24 hours there may be a 48-hour delay before the most recent numbers are available.Further informationFirst Page PreviewRelated articles To select/unselect all items click here Choose an action Add to shortlist Download citationJournal news Find out more about The 25th International conference on the History of Cartography Helsinki Free Access to IM Prize Winning ArticlesMost read Most citedThe Maps Are the Message: Mehmet ii's Patronage of an #x2018 Ottoman Cluster #x2019 Karen Pinto Volume 63 Issue 2 2011Italian Mapmakers in the Spanish Civil War (1937 #x2013 1939)Carme Montaner et al. Volume 64 Issue 1 2012Maps Mapping Modernity: Art Cartography in the Twentieth CenturyDenis Cosgrove Volume 57 Issue 1 2005The Map of Macrobius before 1100Alfred Hiatt Volume 59 Issue 2 2007The Vinland Map R. A. Skelton Josef FischerP. D. A. Harvey Volume 58 Issue 1 2006More Silences secrecy: The hidden agenda of cartography in early modern EuropeJ. B. Harley Volume 40 Issue 1 1988The study of early maps: MethodologyJ. B. Harley Volume 22 Issue 1 1968Sacred Geography Antiquarianism visual Erudition: Benito Arias Montano the Maps in the Antwerp Polyglot BibleZur Shalev Volume 55 Issue 1 2003Maps Mapping Modernity: Art Cartography in the Twentieth CenturyDenis Cosgro

Imago Mundi Two New Initiatives: Promoting communication among Those Interested in Early Maps

关键词： Imago Early

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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