Relevant bibliographies by topics / Automatic region tagging

Academic literature on the topic 'Automatic region tagging'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Automatic region tagging"

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Miao, Y., X. Tang, and Z. Wang. "AN AUTOMATIC SEMANTIC MAP GENERATION METHOD USING TRAJECTORY DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 63–67. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-63-2020.

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Abstract. It’s easily to obtain the geometric information of terrain features in a timely manner using advanced surveying and mapping methods, but it is impossible to obtain their semantic information with low latency due to the rapid development of cities. The popularity of GPS-enabled devices and technologies provide us a large number of personal location information. Moreover, it is possible to extract the personal or group behavior pattern due to the regularity of human behavior. Those conditions make it possible to extract and identify human behavior patterns from their trajectory data. In this paper, we present an automatic semantic map generation method that extract semantic patterns and take advantage of them to tagging spatial objects in an unknown region based on known semantic patterns. We study the regularity of trajectory data and build the semantic pattern based on the regularity of human behavior. Most importantly, we use known semantic patterns to identify the semantics of the stay points in the unknown region, and use this method to realize the semantic recognition of the stay points. Results of the experiments show the effectiveness of our proposed method.
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Fišer, Darja, Tomaž Erjavec, and Nikola Ljubešić. "JANES v0.4: Korpus slovenskih spletnih uporabniških vsebin." Slovenščina 2.0: empirical, applied and interdisciplinary research 4, no. 2 (September 27, 2016): 67. http://dx.doi.org/10.4312/slo2.0.2016.2.67-100.

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The paper presents the current version of the Slovene corpus of netspeak Janes which contains tweets, forum posts, news comments, blogs and blog comments, and user and talk pages from Wikipedia. First, we describe the harvesting procedure for each data source and provide a quantitative analysis of the corpus. Next, we present automatic and manual procedures for enriching the corpus with metadata, such as user type, gender and region, and text sentiment and standardness level. Finally, we give a detailed account of the linguistic annotation workflow which includes tokenization, sentence segmentation, rediacritisation, normalization, morphosyntactic tagging and lemmatization.
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Rotger, Andreu. "Photo-identification of horseshoe whip snakes (Hemorrhois hippocrepis, Linnaeus, 1758) by a semi-automatic procedure applied to wildlife management." Herpetological Journal, Volume 29, Number 4 (October 1, 2019): 304–7. http://dx.doi.org/10.33256/29.4.304307.

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Photo-identification is an increasingly used method for the study of animal populations. Natural marks such as coloration or scale pattern to identify individuals provide an inexpensive and less invasive alternative to conventional tagging methods. Photo-identification has previously been used to distinguish individual snakes, usually by comparing the pileus region. Nevertheless, this method is seldom used in capture-recapture studies. We show the effectiveness of photo-identification in snakes using specific software for individual recognition applied to a wildlife control study of horseshoe whip snakes. Photos were analysed with Automatic Photo Identification Suite (APHIS), which allowed us to compare the variability of head scale patterns surrounding the parietal shields instead of the traditional method of using large scale groups of the pileus. APHIS correctly identified 100 % of recaptures of snakes. Although further studies are needed, the variability of the surrounding scales of the pileus region seems a robust method to identify and differentiate individuals.
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Chimlek, Sutasinee, and Punpiti Piamsa-nga. "Incremental Tag Suggestion for Landmark Image Collections." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 139. http://dx.doi.org/10.11591/ijece.v6i1.8540.

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In recent social media applications, descriptive information is collected through user tagging, such as face recognition, and automatic environment sensing, such as GPS. There are many applications that recognize landmarks using information gathered from GPS data. However, GPS is dependent on the location of the camera, not the landmark. In this research, we propose an automatic landmark tagging scheme using secondary regions to distinguish between similar landmarks. We propose two algorithms: 1) landmark tagging by secondary objects and 2) automatic new landmark recognition. Images of 30 famous landmarks from various public databases were used in our experiment. Results show increments of tagged areas and the improvement of landmark tagging accuracy.
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Chimlek, Sutasinee, and Punpiti Piamsa-nga. "Incremental Tag Suggestion for Landmark Image Collections." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 139. http://dx.doi.org/10.11591/ijece.v6i1.pp139-150.

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In recent social media applications, descriptive information is collected through user tagging, such as face recognition, and automatic environment sensing, such as GPS. There are many applications that recognize landmarks using information gathered from GPS data. However, GPS is dependent on the location of the camera, not the landmark. In this research, we propose an automatic landmark tagging scheme using secondary regions to distinguish between similar landmarks. We propose two algorithms: 1) landmark tagging by secondary objects and 2) automatic new landmark recognition. Images of 30 famous landmarks from various public databases were used in our experiment. Results show increments of tagged areas and the improvement of landmark tagging accuracy.
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Jun, Woogyoung, Yillbyung Lee, and Byoung-Min Jun. "Automatic Image Tagging Model Based on Multigrid Image Segmentation and Object Recognition." Advances in Multimedia 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/857682.

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Since rapid growth of Internet technologies and mobile devices, multimedia data such as images and videos are explosively growing on the Internet. Managing large scale multimedia data with correct tags and annotations is very important task. Incorrect tags and annotations make it hard to manage multimedia data. Accurate tags and annotation ease management of multimedia data and give high quality retrieve results. Fully manual image tagging which is tagged by user will be most accurate tags when the user tags correct information. Nevertheless, most of users do not make effort on task of tagging. Therefore, we suffer from lots of noisy tags. Best solution for accurate image tagging is to tag image automatically. Robust automatic image tagging models are proposed by many researchers and it is still most interesting research field these days. Since there are still lots of limitations in automatic image tagging models, we propose efficient automatic image tagging model using multigrid based image segmentation and feature extraction method. Our model can improve the object descriptions of images and image regions. Our method is tested with Corel dataset and the result showed that our model performance is efficient and effective compared to other models.
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Dean, Samuel, Jack Sunter, Richard J. Wheeler, Ian Hodkinson, Eva Gluenz, and Keith Gull. "A toolkit enabling efficient, scalable and reproducible gene tagging in trypanosomatids." Open Biology 5, no. 1 (January 2015): 140197. http://dx.doi.org/10.1098/rsob.140197.

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One of the first steps in understanding a protein's function is to determine its localization; however, the methods for localizing proteins in some systems have not kept pace with the developments in other fields, creating a bottleneck in the analysis of the large datasets that are generated in the post-genomic era. To address this, we developed tools for tagging proteins in trypanosomatids. We made a plasmid that, when coupled with long primer PCR, can be used to produce transgenes at their endogenous loci encoding proteins tagged at either terminus or within the protein coding sequence. This system can also be used to generate deletion mutants to investigate the function of different protein domains. We show that the length of homology required for successful integration precluded long primer PCR tagging in Leishmania mexicana . Hence, we developed plasmids and a fusion PCR approach to create gene tagging amplicons with sufficiently long homologous regions for targeted integration, suitable for use in trypanosomatids with less efficient homologous recombination than Trypanosoma brucei . Importantly, we have automated the primer design, developed universal PCR conditions and optimized the workflow to make this system reliable, efficient and scalable such that whole genome tagging is now an achievable goal.
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Włodarczyk, Matylda, Joanna Kopaczyk, and Michał Kozak. "Multilingualism in Greater Poland court records (1386–1448): tagging discourse boundaries and code-switching." Corpora 15, no. 3 (November 2020): 273–90. http://dx.doi.org/10.3366/cor.2020.0200.

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This paper introduces the Electronic Repository of Greater Poland Oaths, eROThA (1386–1446), a digitisation project of a diplomatic edition of mediaeval land court oaths recorded in Latin and Old Polish, resulting in a small, lightly tagged specialised bilingual corpus. We present the background, aims, design and methodology of the project. We also discuss the problems and limitations entrenched in turning a printed diplomatic edition into a machine-readable diplomatic edition equipped with a new interpretative layer that is sensitive to the switches between Latin and Old Polish. In addition to the automatic annotation of code-switched items on the basis of typographic characteristics of the printed edition, flexible coding of recurrent language and discourse boundary phenomena has been introduced manually to account for linguistically ambiguous or neutral forms. The project offers a fully multilingual corpus, as well as customised Polish-only and Latin-only datasets, and enables filtered metadata searches in the online front-end. Overall, the report presents a methodology for constructing multilingual corpora in the context of legal cultures in medieval Central Europe that may be extrapolated to datasets originating in other periods and regions.
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Li, Xia, Zhenhao Xu, Xi Shen, Yongxia Zhou, Binggang Xiao, and Tie-Qiang Li. "Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN." Current Oncology 28, no. 5 (September 16, 2021): 3585–601. http://dx.doi.org/10.3390/curroncol28050307.

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Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN architecture for the detection of abnormal cervical cells in cytology images from a cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cervical image dataset of “Digital Human Body” Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using traditional computer-vision techniques, 6–9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.
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Ammar, Adel, Anis Koubaa, and Bilel Benjdira. "Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images." Agronomy 11, no. 8 (July 22, 2021): 1458. http://dx.doi.org/10.3390/agronomy11081458.

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In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images.
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Dissertations / Theses on the topic "Automatic region tagging"

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Awg, Iskandar Dayang Nurfatimah, and dnfaiz@fit unimas my. "Image Retrieval using Automatic Region Tagging." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090302.155704.

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The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions.
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Rosa, Štěpán. "Vyhledávání podobných fotografií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-412825.

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This paper describes the way to realization such an application, where a user chooses a photo database to working with and enters a photo into the system. The system using a visual vocabulary finds the most similar photos from the database and offers tags of the searched photo with a suitable form based on the tag statistical analysis of this photo.
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Book chapters on the topic "Automatic region tagging"

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Li, Sheng-Hui, Chun-Ming Gao, and Hua-Wei Pan. "Automatic Image Tagging Based on Regions of Interest." In Artificial Intelligence and Computational Intelligence, 300–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23881-9_40.

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Conference papers on the topic "Automatic region tagging"

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Huang, Ke, Xiang Ding, Guanling Chen, and Kate Saenko. "Automatic mobile photo tagging using context." In TENCON 2013 - 2013 IEEE Region 10 Conference. IEEE, 2013. http://dx.doi.org/10.1109/tencon.2013.6719075.

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