Bibliografías temáticas / Large image processing

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Literatura académica sobre el tema "Large image processing"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Large image processing"

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S.M., Karpagavalli. "Large Scale Image Processing Using Hadoop Image Processing Interface System." Journal of Advances in Computational Intelligence Theory 5, no. 3 (2023): 28–36. https://doi.org/10.5281/zenodo.8317788.

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<em>Because of the expanding prevalence of modest advanced photography gear, individualized computing gadgets with simple to utilize cameras, and a general improvement of picture catch innovation concerning quality; the measure of information created by individuals every day demonstrates patterns of becoming quicker than the handling capacities of single gadgets. It turns out to be computationally wasteful to examine such tremendous information. The sum of raw information accessible has been expanding at an exponential rate. For the viable treatment of such monstrous information, the utilizati
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Lee, Youngrim, Wanyong Park, Hyunchun Park, and Daesik Shin. "FAST Design for Large-Scale Satellite Image Processing." Journal of the Korea Institute of Military Science and Technology 25, no. 4 (2022): 372–80. http://dx.doi.org/10.9766/kimst.2022.25.4.372.

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This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Be
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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main s
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Vinichuk, O. N., and V. I. Dravitsa. "Development of Algorithms for Processing Images of Large Volumes." Digital Transformation 28, no. 2 (2022): 52–60. http://dx.doi.org/10.35596/2522-9613-2022-28-2-52-60.

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In recent years, interest in digital image processing has increased significantly, so it is no coincidence that digital processing is one of the intensively developed areas of research. When working with a computer system, a rather important factor is the high-quality display of images, as a result of which the methods of processing and improving images are no less important factors, which are not only responsible for the highquality display of the image, but also allow to increase the visibility of interesting details in the image. Today it is quite difficult to find an application or a web a
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Pal, N. R., and J. C. Bezdek. "Complexity reduction for "large image" processing." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 5 (2002): 598–611. http://dx.doi.org/10.1109/tsmcb.2002.1033179.

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Khellah, F., P. Fieguth, M. J. Murray, and M. Allen. "Statistical processing of large image sequences." IEEE Transactions on Image Processing 14, no. 1 (2005): 80–93. http://dx.doi.org/10.1109/tip.2004.838703.

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Utkarsh, Gupta, Kumar Sudhanshu, Singhal Devansh, Tomar Parth, and Kumar Ajay. "IMAGE PROCESSING SYSTEM USING JAVA." International Journal of Innovative Research in Information Security VII, no. IV (2020): 36–40. https://doi.org/10.26562/ijiris.2020.v0704.002.

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The article is all about the Image Processing System that can be defined as, processing and altering an existing image in the desired manner. Image is one of the perceptible sources in applications of Image Processing including a large number of tools and techniques which help to extract complex features of an image. Probably the most powerful image processing system is the human brain together with the eye. The system receives, enhances, and stores images at enormous rates of speed. The objective of Image Processing is to visually enhance or statistically evaluate some aspect of an image not
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Filatov, Valerii, Anna Filatova, Anatolii Povoroznyuk, and Shakhin Omarov. "IMAGE CLASSIFIER FOR FAST SEARCH IN LARGE DATABASES." Advanced Information Systems 8, no. 2 (2024): 12–19. http://dx.doi.org/10.20998/2522-9052.2024.2.02.

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Relevance. The avalanche-like growth in the amount of information on the Internet necessitates the development of effective methods for quickly processing such information in information systems. Clustering of news information is carried out by taking into account both the morphological analysis of texts and graphic content. Thus, an urgent task is the clustering of images accompanying textual information on various web resources, including news portals. The subject of study is an image classifier that exhibits low sensitivity to increased information in databases. The purpose of the article i
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P, Sai Gyaneshwar Chary, Rahul Pasha Shaik, Sandeep Sirra, and Vishwa Shanthi M. "TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT." TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT 2 2, M. Vishwa Shanthi (2023): 54. https://doi.org/10.5281/zenodo.7868137.

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Text-to-image generation is a fascinating application of computer vision and natural language processing, where the goal is to generate realistic and diverse images based on textual descriptions. In this project, we propose a text-to-image generation system using Python programming language and two main libraries, Imagen and Streamlit. The system consists of a generative adversarial network (GAN) model trained on a large dataset of images and their corresponding captions, and a text processing and generation module.&nbsp;
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Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (1997): 3–33. http://dx.doi.org/10.1142/s0218001497000020.

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A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful
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Tesis sobre el tema "Large image processing"

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Le, Riguer E. M. J. "Generic VLSI architectures : chip designs for image processing applications." Thesis, Queen's University Belfast, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368593.

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McCusker, Sean. "A digital image processing approach to large-scale turbulence studies." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843989/.

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An image processing approach to turbulence studies has been developed. The approach employs a structure tracking technique to quantify the movement of coherent, large-scale turbulent structures. The 'structure tracking' technique has been applied to the shear layer of a low speed jet issuing into a low speed crossflow. A study of the characteristics of the turbulent flow within this region involved comparative measurements with hot-wire anemometry measurements within the same flow regime and fractal analysis of the flow visualisation images used by the tracking routine. Fractal analysis was ap
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Trotter, John A. "A fault tolerance scheme for large integrated processor arrays." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.276877.

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Nguyen, Quang Vinh. "Space-efficient visualisation of large hierarchies /." Electronic version, 2005. http://adt.lib.uts.edu.au/public/adt-NTSM20051123.174122/index.html.

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Hudson, James. "Processing large point cloud data in computer graphics." Connect to this title online, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1054233187.

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Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains xix, 169 p.; also includes graphics (some col.). Includes bibliographical references (p. 159-169). Available online via OhioLINK's ETD Center
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Rose, Tony Gerard. "Large vocabulary semantic analysis for text recognition." Thesis, Nottingham Trent University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333961.

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Ali, Faridah M. "Parallel pipelined VLSI arrays for real-time image processing." Diss., Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/49914.

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Real-time image processing involves processing a wide spectrum of algorithms on huge data sets. Processing at the pixel data rate demands more powerful parallel machines than those developed for conventional image processing. This research takes advantage of current VLSI technology to examine a new approach for processing arbitrary algorithms at real-time data rate. It is based on embedding the algorithms, expressed by their dependency graphs, into two dimensional regularly connected processing arrays. Each node in a graph represents an operation which can be processed by an individual proces
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Cooper, Lee Alex Donald. "High Performance Image Analysis for Large Histological Datasets." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250004647.

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Carter, Caleb. "High Resolution Visualization of Large Scientific Data Sets Using Tiled Display." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/CarterC2007.pdf.

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Yeung, Henry Wing Fung. "Efficient Deep Neural Network Designs for High Dimensional and Large Volume Image Processing." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24336.

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Over time, more advanced methods of imaging are being developed for capturing richer information from the scene. Such advancement leads to an increase in the spatial resolution, i.e. number of pixels in the width and height of the image, from the angular resolution, i.e. light rays from multiple angles, or spectral resolution, i.e. bands across the electromagnetic spectrum. As a result, the number of dimensions and volume per image increases significantly. Examples of such images are light field images and satellite images. Light field images, which capture the ray of light at each point of
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Libros sobre el tema "Large image processing"

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J, Offen R., ed. VLSI image processing. Collins, 1985.

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J, Offen R., ed. VLSI image processing. McGraw-Hill, 1985.

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Takao, Nishitani, Ang Peng H, and Catthoor Francky, eds. VLSI video/image signal processing. Kluwer Academic Publishers, 1993.

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J, Offen R., ed. VLSIimage processing. Collins, 1985.

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Jonker, Petrus Paulus. Morphological image processing: Architecture and VLSI design. Kluwer, 1992.

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European Workshop on 3D Structure from Multiple Images of Large-Scale Environments (2nd 2000 Dublin, Ireland). 3D structure from images - SMILE 2000: Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, Dublin, Irleand [i.e. Ireland], July 1-2, 2000 : revised papers. Springer, 2001.

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P, Pirsch, ed. VLSI implementations for image communications. Elsevier, 1993.

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W, Klein William, and National Institute of Standards and Technology (U.S.), eds. Creating and validating a large image database for METTREC. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1997.

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Moyal, Ami. Phonetic Search Methods for Large Speech Databases. Springer New York, 2013.

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Tlelo-Cuautle, Esteban. Integrated Circuits for Analog Signal Processing. Springer New York, 2013.

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Capítulos de libros sobre el tema "Large image processing"

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Zhou, Rong, and Liqing Zhang. "Contour-Based Large Scale Image Retrieval." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_64.

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Marques, Oge, and Gustavo Benvenutti Borba. "Recipe 29: Processing very large images." In Image Processing Recipes in MATLAB®. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003170198-38.

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Kosydor, Paweł, Ewa Warchala, and Adam Piórkowski. "Impact of ICT Infrastructure on the Processing of Large Raster Datasets." In Image Processing and Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_17.

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Lu, Jun, and Li Zhang. "Cascaded Deep Hashing for Large-Scale Image Retrieval." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04224-0_36.

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Delponte, Elisabetta, Francesco Isgrò, Francesca Odone, and Alessandro Verri. "Large Baseline Matching of Scale Invariant Features." In Image Analysis and Processing – ICIAP 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_97.

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Štajduhar, Ivan, Teo Manojlović, Franko Hržić, et al. "Analysing Large Repositories of Medical Images." In Bioengineering and Biomedical Signal and Image Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88163-4_17.

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Mai, Tien-Dung, Thanh Duc Ngo, Duy-Dinh Le, Duc Anh Duong, Kiem Hoang, and Shin’ichi Satoh. "Learning Balanced Trees for Large Scale Image Classification." In Image Analysis and Processing — ICIAP 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23234-8_1.

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Mazurek, Przemysław. "Large LED Displays Panel Control Using Splitted PWM." In Image Processing and Communications Challenges 10. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03658-4_11.

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Sun, Zhanquan, Feng Li, and Huifen Huang. "Large Scale Image Classification Based on CNN and Parallel SVM." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_57.

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Grossi, Giuliano, Raffaella Lanzarotti, and Jianyi Lin. "A Selection Module for Large-Scale Face Recognition Systems." In Image Analysis and Processing — ICIAP 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23234-8_49.

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Actas de conferencias sobre el tema "Large image processing"

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Cupitt, John, and Kirk Martinez. "VIPS: an image processing system for large images." In Electronic Imaging: Science & Technology, edited by V. Ralph Algazi, Sadayasu Ono, and Andrew G. Tescher. SPIE, 1996. http://dx.doi.org/10.1117/12.233043.

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Hering, Alessa, and Stefan Heldmann. "Unsupervised learning for large motion thoracic CT follow-up registration." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2506962.

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Soleymani, Farzin, Mohammad Eslami, Tobias Elze, Bernd Bischl, and Mina Rezaei. "Deep variational clustering framework for self-labeling large-scale medical images." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2022. http://dx.doi.org/10.1117/12.2613331.

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Hoffmann, Rolf. "A High Quality Image Stitching Process for Industrial Image Processing and Quality Assurance." In OCM 2021 - 5th International Conference on Optical Characterization of Materials. KIT Scientific Publishin, 2021. http://dx.doi.org/10.58895/ksp/1000128686-18.

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The size of the recording area of a camera is limited. The resolution of a camera image is also limited. To capture larger areas, a wide angle lens can be used, for example. However, the image resolution per unit area decreases. The decreased image resolution can be compensated by image sensors with a higher number of pixels. However, the use of a high pixel number of image sensors is limited to the current state of the art and availability of real image sensors. Furthermore the use of a wide angle lens has the disadvantage of a stronger distortion of the image scene. Also the viewing directio
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van der Zant, Tijn, Lambert Schomaker, and Edwin Valentijn. "Large scale parallel document image processing." In Electronic Imaging 2008, edited by Berrin A. Yanikoglu and Kathrin Berkner. SPIE, 2008. http://dx.doi.org/10.1117/12.765482.

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Bhattacharjee, Shiben, Suryakant Patidar, and P. J. Narayanan. "Real-Time Rendering and Manipulation of Large Terrains." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.85.

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Abidin, Anas Z., Adora M. DSouza, and Axel Wismüller. "Detecting connectivity changes in autism spectrum disorder using large-scale Granger causality." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2513023.

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Gao, Yurui, Dylan R. Lawless, Muwei Li, et al. "Automatic preprocessing pipeline for white matter functional analyses of large-scale databases." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2023. http://dx.doi.org/10.1117/12.2653132.

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Graf, Laura Franziska, Hanna Siebert, Sven Mischkewitz, Ron Keuth, and Mattias P. Heinrich. "Highly accurate deep registration networks for large deformation estimation in compression ultrasound." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2023. http://dx.doi.org/10.1117/12.2653870.

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Nilsback, Maria-Elena, and Andrew Zisserman. "Automated Flower Classification over a Large Number of Classes." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.47.

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Informes sobre el tema "Large image processing"

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Bhatt, Parth, Curtis Edson, and Ann MacLean. Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS). Michigan Technological University, 2022. http://dx.doi.org/10.37099/mtu.dc.michigantech-p/16366.

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Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we dev
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Mazari, Mehran, Yahaira Nava-Gonzalez, Ly Jacky Nhiayi, and Mohamad Saleh. Smart Highway Construction Site Monitoring Using Artificial Intelligence. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2336.

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Construction is a large sector of the economy and plays a significant role in creating economic growth and national development,and construction of transportation infrastructure is critical. This project developed a method to detect, classify, monitor, and track objects during the construction, maintenance, and rehabilitation of transportation infrastructure by using artificial intelligence and a deep learning approach. This study evaluated the performance of AI and deep learning algorithms to compare their performance in detecting and classifying the equipment in various construction scenes.
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Delwiche, Michael, Yael Edan, and Yoav Sarig. An Inspection System for Sorting Fruit with Machine Vision. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7612831.bard.

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Concepts for real-time grading of fruits and vegetables were developed, including multi-spectral imaging with structured illumination to detect and distinguish surface defects from concavities. Based on these concepts, a single-lane conveyor and inspection system were designed and evaluated. Image processing algorithms were developed to inspect and grade large quasi-spherical fruits (peaches and apples) and smaller dried fruits (dates). Adjusting defect pixel thresholds to achieve a 25% error rate on good apples, classification errors for bruise, crack, and cut classes were 51%, 42%, and 46%,
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FDG-PET/CT SUV for Response to Cancer Therapy, Clinically Feasible Profile. Chair Nathan Hall and Jeffrey Yap. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20230615.

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This QIBA Profile documents specifications and requirements to provide comparability and consistency for quantitative FDG-PET across scanners in oncology. It can be applied to both clinical trial use as well as individual patient management. This document organizes acquisition, reconstruction and post-processing, analysis and interpretation as steps in a pipeline that transforms data to information to knowledge. The document, developed through the efforts of the QIBA FDG-PET Biomarker Committee, has shared content with the FDG-PET UPICT protocol, as well as additional material focused on the d
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McGarrigle, Malachy. Watchpoints for Consideration When Utilising a VDI Network to Teach Archicad BIM Software Within an Educational Programme. Unitec ePress, 2023. http://dx.doi.org/10.34074/ocds.099.

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This research identifies factors to be considered in the adoption of a virtual desktop infrastructure (VDI) accommodating the software needs of a tertiary institution. The study discusses the potential advantages and disadvantages of VDI, focusing specifically on the performance of the architectural software Archicad when used virtually. The findings will be relevant to similar programmes, such as Revit, and software used in other disciplines, especially where processing power is important. Aims discussed include reducing high-specification computers rarely used to capacity, assessing user exp
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale, and Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data: A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), 2023. http://dx.doi.org/10.53055/icimod.1016.

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Data from camera trap networks provide crucial information on various important aspects of wildlife presence, movement, and behaviour. However, manual processing of large volumes of images captured is time and resource intensive. This study explores three different approaches of deep learning methods to detect and classify images of key animal species collected from the ICIMOD Knowledge Park at Godavari, Nepal. It shows that transfer learning with ImageNet pretrained models (A1) can be used to detect animal species with minimal model training and testing. These methods when scaled up offer tre
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Author, Unknown. DTRS56-02-T-0005 Digital Mapping of Buried Pipelines with a Dual Array System. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0011943.

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The technical goal of the Dual Array Project was to develop new technology for non-invasive mapping of buried pipelines, down to depths of 10 meters or more, using modern electromagnetic sensors and signal processing. A major proposed innovation in the work was the integration of the sensor arrays and software into a mobile system capable of mapping underground utility networks (and other buried infrastructure) efficiently over large areas. Ultimately, the goal is to have a non-invasive system that can produce an accurate infrastructure map of an entire urban or suburban utility network in dig
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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