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

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Artículos de revistas sobre el tema "Large image processing"

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Lee, Youngrim, Wanyong Park, Hyunchun Park y Daesik Shin. "FAST Design for Large-Scale Satellite Image Processing". Journal of the Korea Institute of Military Science and Technology 25, n.º 4 (5 de agosto de 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. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.
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Pal, N. R. y J. C. Bezdek. "Complexity reduction for "large image" processing". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, n.º 5 (octubre de 2002): 598–611. http://dx.doi.org/10.1109/tsmcb.2002.1033179.

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Khellah, F., P. Fieguth, M. J. Murray y M. Allen. "Statistical processing of large image sequences". IEEE Transactions on Image Processing 14, n.º 1 (enero de 2005): 80–93. http://dx.doi.org/10.1109/tip.2004.838703.

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Tripathi, Rakesh y Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images". SMART MOVES JOURNAL IJOSCIENCE 4, n.º 4 (20 de abril de 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 steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.
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Vinichuk, O. N. y V. I. Dravitsa. "Development of Algorithms for Processing Images of Large Volumes". Digital Transformation 28, n.º 2 (2 de septiembre de 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 application with a simple and user-friendly interface, as well as with relatively low characteristics in terms of energy consumption needed to supply the operating system and the device in general. This article presents new algorithms that improve the efficiency of image processing by reducing application loading and processing time, as well as by reducing the load on the operating system.
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Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing". International Journal of Pattern Recognition and Artificial Intelligence 11, n.º 01 (febrero de 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 applications. In this paper, we discuss several aspects of Bayesian paradigms: uncertainty present in the observed image, prior distribution modeling, Bayesian-based estimation techniques in image processing, particularly, the maximum a posteriori estimator and the Kalman filtering theory, robustness, and Markov random fields and applications.
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Shinde, Prof Dyanda. "Air Pollution Checker Using Image Processing". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, n.º 10 (1 de octubre de 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26515.

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In this paper we present a new method to visualize air pollutant through image processing. In order to achieve a realistic effect, we further enhance thus above obtained images in spatial domain. In the proposed method we map the densities of air pollutants to different gray levels, and visualize them by blending those gray levels with background images. The proposed method can visualize large-scale air pollution data from different viewpoints in real time and provide the resulting image with any resolution theoretically, which is very important and favorable for the Internet transmission. Keywords: Machine Learning; Air Pollution; Air Pollution Prediction,images
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Dong, Lei, Tingtao Zhang, Fangjian Liu, Rui Liu y Hongjian You. "GPU Acceleration for SAR Satellite Image Ortho-Rectification". Remote Sensing 16, n.º 7 (7 de abril de 2024): 1301. http://dx.doi.org/10.3390/rs16071301.

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Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.
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Remondino, F., E. Nocerino, I. Toschi y F. Menna. "A CRITICAL REVIEW OF AUTOMATED PHOTOGRAMMETRIC PROCESSING OF LARGE DATASETS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5 (21 de agosto de 2017): 591–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w5-591-2017.

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The paper reports some comparisons between commercial software able to automatically process image datasets for 3D reconstruction purposes. The main aspects investigated in the work are the capability to correctly orient large sets of image of complex environments, the metric quality of the results, replicability and redundancy. Different datasets are employed, each one featuring a diverse number of images, GSDs at cm and mm resolutions, and ground truth information to perform statistical analyses of the 3D results. A summary of (photogrammetric) terms is also provided, in order to provide rigorous terms of reference for comparisons and critical analyses.
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Kim, Yoon-Ki y Yongsung Kim. "DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference". Electronics 9, n.º 10 (13 de octubre de 2020): 1664. http://dx.doi.org/10.3390/electronics9101664.

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Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.
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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 applied to flow visualisation images to educe a range of length scales made apparent by the flow visualisation equipment The results obtained with the structure tracking technique included the instantaneous velocity of the structures and a measure of their length scales. The instantaneous velocity measurements were used to calculate a turbulence characteristic associated with the structures. Further analysis revealed subsets of this turbulence characteristic involving the variation in average velocity of individual structures as well as variations in the instantaneous velocity of individual structures. Where possible, the results of the structure tracking technique were compared to those achieved by hot wire anemometry and good correspondence was found between the mean flow characteristics measured by both techniques. The results of the two techniques began to diverge in the regions of the flow where conventional hot-wire anemometry was unable to discriminate between the flow associated with the jet and that associated with the crossflow. In such regions, time-averaged hot-wire anemometry produced results which combined the measurements in both flow regimes and therefore attenuated any characteristics of the jet which were significantly different from those of the crossflow. In the same flow regions the structure tracking technique was able to measure those characteristics specifically associated with the jet, producing results, which reflected the behaviour of the jet more accurately.
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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.
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 processor in the array. The embedding is performed such that data can be processed in a pipeline fashion as they are received. The result is a machine which exploits functional parallelism and data pipelining simultaneously. The presentation is divided into three parts: the first discusses graphical representation for general image processing algorithms, taking into account the nature of the data flow in real-time systems. The conditions for pipelining the processing of the graph are derived. Next the logical design of a class of VLSI arrays is considered. These arrays can be configured to embed arbitrary problem graphs. The discussion involves the architecture of the array, the architecture of its processing elements and an efficient programming scheme. Finally, static embedding of the dependency graphs into the proposed array is considered. Lower and upper bounds on the area needed to embed any graph are found. Three heuristic procedures to embed the graph at minimum cost are developed, implemented and tested.
Ph. D.
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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 the scene instead of the total amount of light measured at each point of the photosensor, contain 4 dimensions, i.e. spatial width and height and angular width and height, as opposed to only 2 dimensions of images taken from the traditional DSLR cameras, i.e. spatial width and height only. Satellite images, on the other hand, have the same number of dimensions as the traditional images but contain far greater size per dimension. The spatial width and height of a satellite image can be over 3200 by 3200 pixels. Moreover, there can be more than 16 bands from the short-wave infrared (1195-2365nm) range, instead of the 3 RGB channels of the traditional images. Both the light field images and the satellite images contain more information compared to the traditional images because of their huge image size. However, it is problematic to analyse due to the exact same reason. This problem is particularly important to the recently popular deep learning based technique. Deep learning based methods rely on feeding a large sample size dataset to a deep neural network which is trained through back-propagation. The training process for this method is extremely time consuming. Given the same amount of compute, the training time depends on the complexity of the network and the size of the input data. In the case of training a model for high dimensional and large volume images, we will run into a trade-off between training time and data exploitation in the design of the neural networks. Specifically, building a neural network that utilises all 4 dimensions of the light field image can easily result in a structure that takes over a month to train. Owing to this, many researchers resort to methods for handling the image by separating it into parts that reduce training time but reduce the exploitation of correlation within data, thus hampering model performance. This thesis aims to provide efficient designs in handling the high dimensional light field images and the large volume satellite images on problems such as spatial super-resolution, light field reconstruction, classification and segmentation. We will design networks that utilise all available information when building the neural network and are efficiently connected for learning a good feature representation. Some of our solutions achieve state-of-the-art results at the time of publication.
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Libros sobre el tema "Large image processing"

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

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

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

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

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Jonker, Petrus Paulus. Morphological image processing: Architecture and VLSI design. [Deventer?]: Kluwer, 1992.

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

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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. Berlin: Springer, 2001.

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Reinhard, Koch, Gool Luc van y European Conference on Computer Vision (5th : 1998 : Freiburg im Breisgau, Germany), eds. 3D structure from multiple images of large-scale environments: European workshop, SMILE '98, Freiburg, Germany, June 6-7, 1998 : proceedings. Berlin: Springer, 1998.

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W, Klein William y National Institute of Standards and Technology (U.S.), eds. Creating and validating a large image database for METTREC. Gaithersburg, MD: 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. New York, NY: Springer New York, 2013.

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Capítulos de libros sobre el tema "Large image processing"

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Zhou, Rong y Liqing Zhang. "Contour-Based Large Scale Image Retrieval". En Neural Information Processing, 565–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_64.

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Marques, Oge y Gustavo Benvenutti Borba. "Recipe 29: Processing very large images". En Image Processing Recipes in MATLAB®, 223–28. New York: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003170198-38.

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Kosydor, Paweł, Ewa Warchala y Adam Piórkowski. "Impact of ICT Infrastructure on the Processing of Large Raster Datasets". En Image Processing and Communications, 134–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_17.

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Lu, Jun y Li Zhang. "Cascaded Deep Hashing for Large-Scale Image Retrieval". En Neural Information Processing, 419–29. Cham: 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 y Alessandro Verri. "Large Baseline Matching of Scale Invariant Features". En Image Analysis and Processing – ICIAP 2005, 794–801. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_97.

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Štajduhar, Ivan, Teo Manojlović, Franko Hržić, Mateja Napravnik, Goran Glavaš, Matija Milanič, Sebastian Tschauner, Mihaela Mamula Saračević y Damir Miletić. "Analysing Large Repositories of Medical Images". En Bioengineering and Biomedical Signal and Image Processing, 179–93. Cham: 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 y Shin’ichi Satoh. "Learning Balanced Trees for Large Scale Image Classification". En Image Analysis and Processing — ICIAP 2015, 3–13. Cham: 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". En Image Processing and Communications Challenges 10, 87–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03658-4_11.

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Sun, Zhanquan, Feng Li y Huifen Huang. "Large Scale Image Classification Based on CNN and Parallel SVM". En Neural Information Processing, 545–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_57.

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Grossi, Giuliano, Raffaella Lanzarotti y Jianyi Lin. "A Selection Module for Large-Scale Face Recognition Systems". En Image Analysis and Processing — ICIAP 2015, 529–39. Cham: 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 y Kirk Martinez. "VIPS: an image processing system for large images". En Electronic Imaging: Science & Technology, editado por V. Ralph Algazi, Sadayasu Ono y Andrew G. Tescher. SPIE, 1996. http://dx.doi.org/10.1117/12.233043.

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Hering, Alessa y Stefan Heldmann. "Unsupervised learning for large motion thoracic CT follow-up registration". En Image Processing, editado por Elsa D. Angelini y 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 y Mina Rezaei. "Deep variational clustering framework for self-labeling large-scale medical images". En Image Processing, editado por Ivana Išgum y 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". En 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 direction from a central location is usually problematic in the outer areas of a wide angle lens. Instead of using a wide angle lens, there is still the possibility to capture the large image scene with several images. This can be done either by moving the camera or by using several cameras that are positioned accordingly. In case of multiple image captures, the single use of the required image is a simple way to evaluat e a limited area of a large image scene with image processing. For example, it can be determined whether a feature limited by the size is present in the image scene. The use of this simple variant of a moving camera system or the use of single images makes it difficult or even impossible to use some image processing options. For example, determining the positions and dimensions of features that exceed a single image is difficult. With moving camera systems, the required mechanics add to the effort, which is subject to wear and tear and introduces a time factor. Image stitching techniques can reduce many of these problems in large image scenes. Here, single images are captured (by one or more cameras) and stitched together to fit. The original smaller single images are merged into a larger coherent image scene. Difficulties that arise here and are problematic for the use in industrial image processing are, among others: the exact positioning of the single images to each other and the actual joining of the imag es, if possible without creating disturbing artifacts. This publication is intended to make a contribution to this.
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van der Zant, Tijn, Lambert Schomaker y Edwin Valentijn. "Large scale parallel document image processing". En Electronic Imaging 2008, editado por Berrin A. Yanikoglu y Kathrin Berkner. SPIE, 2008. http://dx.doi.org/10.1117/12.765482.

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

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

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Gao, Yurui, Dylan R. Lawless, Muwei Li, Yu Zhao, Kurt G. Schilling, Lyuan Xu, Andrea T. Shafer et al. "Automatic preprocessing pipeline for white matter functional analyses of large-scale databases". En Image Processing, editado por Ivana Išgum y 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 y Mattias P. Heinrich. "Highly accurate deep registration networks for large deformation estimation in compression ultrasound". En Image Processing, editado por Ivana Išgum y Olivier Colliot. SPIE, 2023. http://dx.doi.org/10.1117/12.2653870.

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Nilsback, Maria-Elena y Andrew Zisserman. "Automated Flower Classification over a Large Number of Classes". En 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"

1

Bhatt, Parth, Curtis Edson y Ann MacLean. Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS). Michigan Technological University, septiembre de 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 developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS.
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Delwiche, Michael, Yael Edan y Yoav Sarig. An Inspection System for Sorting Fruit with Machine Vision. United States Department of Agriculture, marzo de 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%, respectively. Comparable results for bruise, scar, and cut peach clases were 48%, 22%, and 58%, respectively. Acquiring more than two images of each fruit and using more than six lines of structured illumination per fruit would reduce sorting errors. Doing so, potential sorting error rates for bruise, crack, and cut apple classes were estimated to be 38%, 38%, and 33%, respectively. Similarly, potential error rates for the bruitse, scar, and cut peach classes were 9%, 3%, and 30%, respectively. Date size classification results were good: 68% within one size class and 98% within two size classes. Date quality classification results were not adequate due to the problem of blistering. Improved features were discussed. The most significant contribution of this research was the on-going collaboration with producers and equipment manufacturers, and the resulting transfer of research ideas to expedite the commercial application of machine vision for postharvest inspection and grading of agricultural products.
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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, octubre de 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 experience, and feasibility of VDI remote access. Primarily a case study, this project centres around delivery of papers in the New Zealand Diploma of Architectural Technology programme at Unitec | Te Pūkenga that employ Archicad. Software efficiency and performance was monitored throughout teaching across numerous semesters. Incidents were logged and VDI operation tracked, especially during complex tasks such as image rendering. Load testing was also carried out to assess the implications of large user numbers simultaneously performing such complex tasks. Project findings indicate that Archicad performance depends on the design and specification of the virtual platform. Factors such as processing power, RAM allocation and ratio of users to virtual machines (VM)s proved crucial. Tasks executed by the software and how software itself uses hardware are other considerations. This research is important, as its findings could influence the information technology strategies of both academic institutions and industry in coming years. Virtual computing provides many benefits, and this project could provide the confidence for stakeholders to adopt new strategies using VDI instead of the traditional approach of computers with locally installed software applications.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 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 detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale y 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), septiembre de 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 tremendous scope for quicker and informed conflict management actions, including automated response, which can help minimise human wildlife conflict management costs across countries in the region.
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Author, Unknown. DTRS56-02-T-0005 Digital Mapping of Buried Pipelines with a Dual Array System. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), marzo de 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 digital form. This goal requires the development of new geophysical remote sensing technologies to create underground images down to the depths of most buried utilities in the United States and the development of software to extract features from the images to create digital maps that can be archived electronically - for example, in Geographic Information Systems. Key components of each of these goals were developed and demonstrated during the Dual-Array Project.
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Yan, Yujie y Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, mayo de 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-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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