Bibliografie tematiche / Image processin

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Letteratura scientifica selezionata sul tema "Image processin"

Autore: Grafiati
Pubblicato: 19 aprile 2025

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Articoli di riviste sul tema "Image processin"

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Jung, Sun Ho. "Study on CCTV based Sensor applied SURF Image Processin System". KOREA SCIENCE & ART FORUM 16 (30 giugno 2014): 403. http://dx.doi.org/10.17548/ksaf.2014.06.16.403.

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Bhuvaneswari, M. "Gaussian mixture model: An application to parameter estimation and medical image classification". Journal of Scientific and Innovative Research 5, n. 3 (25 giugno 2016): 100–105. http://dx.doi.org/10.31254/jsir.2016.5308.

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Abstract (sommario):
Gaussian mixture model based parameter estimation and classification has recently received great attention in modelling and processin g data. Gaussian Mixture Model (GMM) is the probabilistic model for representing the presence of subpopulations and it works well with the classification and parameter estimation strategy. Here in this work Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques. The performance of the classification strategy of both the classifiers used has a better proficiency when compared to the other classifiers. By combining the SVM and GMM we co uld be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques. In this the GMM classifier and the SVM classifier are trained usin g the parameters and they are to be compared.
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Legland, David, e Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab". F1000Research 10 (30 aprile 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.

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Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.
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Pariyar, Kopila. "Process of Detecting Barcodes Using Image Processing". International Journal of Scientific Engineering and Research 2, n. 7 (27 luglio 2014): 18–19. https://doi.org/10.70729/j2013326.

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Oguma, Ryuichiro, Syo Matsumura e Tetsuo Eguchi. "Mesoscopic TDGL Model for Formation of Domain Structures in D019 Type Ordering". Solid State Phenomena 172-174 (giugno 2011): 602–7. http://dx.doi.org/10.4028/www.scientific.net/ssp.172-174.602.

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The present authors recently presented the time-dependent Ginzburg-Landau (TDGL) formulation for L12 type ordering processin binary alloys, taking into account the symmetrical relationships of these ordered phases. Extending the formulation, the authors have developed the TDGL model for microstructural evolution of D019 type ordering. The D019 structure based on hcp is divided into four equivalent sublattices. The site occupation probabilities are given as a function of three order parameters and a composition parameter. Multiple types of variants of the structures are represented by the order parameters. Mean-field free energies are defined in a form of Landau type expansion with the order parameters and the composition parameter. Interfacial energies due to local variations of degrees of order and composition are given in a gradient square approximation. Kinetic equations for time-evolution of the order parameters and the composition one are derived from the Ginzburg-Landau type potential consisting of the mean-field free energies and the interfacial energy terms. Three-dimensional numerical simulations based on the kinetic equations have been performed, and the domain structures obtained are compared with a TEM image of Cu3Sn alloy.
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Mohamed Y Abdallah, Yousif, Mohamed MO Yousef e Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods". International Journal of Science and Research (IJSR) 10, n. 7 (27 luglio 2021): 557–64. https://doi.org/10.21275/sr21709185141.

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Patel, Bindiya, Dr Pankaj Kumar Mishra e Prof Amit Kolhe. "Lung Cancer Detection on CT Images by using Image Processing". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 aprile 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.

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x, Priyanka. "Review Paper on Identifying Fake Images by Digital Image Processing". International Journal of Scientific Engineering and Research 5, n. 5 (27 maggio 2017): 169–71. https://doi.org/10.70729/ijser151486.

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Tetard, Martin, Ross Marchant, Giuseppe Cortese, Yves Gally, Thibault de Garidel-Thoron e Luc Beaufort. "Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow". Climate of the Past 16, n. 6 (2 dicembre 2020): 2415–29. http://dx.doi.org/10.5194/cp-16-2415-2020.

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Abstract. Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new workflow has been developed for automated radiolarian image acquisition, stacking, processing, segmentation and identification. The protocol includes a newly proposed methodology for preparing radiolarian microscopic slides. We mount eight samples per slide, using a recently developed 3D-printed decanter that enables the random and uniform settling of particles and minimizes the loss of material. Once ready, slides are automatically imaged using a transmitted light microscope. About 4000 specimens per slide (500 per sample) are captured in digital images that include stacking techniques to improve their focus and sharpness. Automated image processing and segmentation is then performed using a custom plug-in developed for the ImageJ software. Each individual radiolarian image is automatically classified by a convolutional neural network (CNN) trained on a Neogene to Quaternary radiolarian database (currently 21 746 images, corresponding to 132 classes) using the ParticleTrieur software. The trained CNN has an overall accuracy of about 90 %. The whole procedure, including the image acquisition, stacking, processing, segmentation and recognition, is entirely automated via a LabVIEW interface, and it takes approximately 1 h per sample. Census data count and classified radiolarian images are then automatically exported and saved. This new workflow paves the way for the analysis of long-term, radiolarian-based palaeoclimatic records from siliceous-remnant-bearing samples.
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Arti Verma. "Digital Image Processing of SEM image of Polymer Nanocomposite Thin Film Using Java Based Program Image J." Power System Technology 43, n. 2 (30 giugno 2019): 44–46. https://doi.org/10.52783/pst.1135.

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Abstract (sommario):
Various images obtain from TEM; SEM can be analyzed with the help of computer image analysis software. Image processing is used to describe the size, shape, surface topography of micro or nano structure materials. In the present paper the characterization analysis is being reported qualitatively by using digital image processing of SEM/TEM image of some nanomaterials. This novel technique is an effective experimental tool for the detailed structural characterization. For image processing Java based software ImageJ is used in the present study.
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Tesi sul tema "Image processin"

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Bergström, Britt, e Erica Burlin. "Bildens godkännandeprocessi katalogproduktion : The image approval processin a catalog production". Thesis, Högskolan Dalarna, Grafisk teknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4210.

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The objective of this thesis has been to investigate the approval process for an image. This investigation has been carriedout at four catalog-producing companies and three companies working with repro or printing. The information wasgathered through interviews and surveys and later used for evaluation. The result of the evaluation has shown that allbusinesses are very good at technical aspects but also that the biggest problem they have is with the communication. Theconclusion is that businesses need a clear construction for the image process. This will minimize the communicationproblems and make the process effective.
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Murphy, Brian P. "Image processing techniques for acoustic images". Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26585.

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Approved for public release; distribution is unlimited
The primary goal of this research is to test the effectiveness of various image processing techniques applied to acoustic images generated in MATLAB. The simulated acoustic images have the same characteristics as those generated by a computer model of a high resolution imaging sonar. Edge Detection and Segmentation are the two image processing techniques discussed in this study. The two methods tested are a modified version of the Kalman filtering and median filtering
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Khan, Preoyati. "Cluster Based Image Processing for ImageJ". Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1492164847520322.

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Yallop, Marc Richard. "Image processing techniques for passive millimetre wave images". Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409545.

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Baabd, A., M. Y. Tymkovich e О. Г. Аврунін. "Image Processing of Panoramic Dental X-Ray Images". Thesis, ХГУ, 2018. http://openarchive.nure.ua/handle/document/6204.

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Abstract (sommario):
The panoramic image allows to clearly see the state of the teeth, the dental rudiments, which are located in the jaw, temporomandibular joints, as well as the maxillary sinuses. It is noted that this type of study has a small dose of radiation. Indications for this type of study are dental implantation, bite correction, suspicion of bone tissue inflammation, control of the growth and development of the teeth, as well as the diagnosis of other dental problems.
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AbouRayan, Mohamed. "Real-time Image Fusion Processing for Astronomical Images". University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1461449811.

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Kim, Younhee. "Towards lower bounds on distortion in information hiding". Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3403.

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Abstract (sommario):
Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 133. Thesis directors: Zoran Duric, Dana Richards. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. Title from PDF t.p. (viewed Mar. 17, 2009). Includes bibliographical references (p. 127-132). Also issued in print.
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Tummala, Sai Virali, e Veerendra Marni. "Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images". Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15360.

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Acosta, Edward Kelly. "A programmable processor for the Cheops image processing system". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36557.

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Abdulla, Ghaleb. "An image processing tool for cropping and enhancing images". Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-12232009-020207/.

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Libri sul tema "Image processin"

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Kayyali, Mohamed S. Digital image processing. Lima: Facultad de Ingenieria y Arquitectura de la Universidad de San Martin de Porres, 2002.

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Dougherty, Edward R. Random processes for image and signal processing. Bellingham, Wash: SPIE Optical Engineering Press, 1999.

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Goudail, François, e Philippe Réfrégier. Statistical Image Processing Techniques for Noisy Images. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-8855-3.

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El-Sharkawy, Mohamed. Signal processing, image processing, and graphics applications with Motorola's DSP96002 processor. Englewood Cliffs, N.J: PTR Prentice Hall, 1994.

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Acharya, Tinku, e Ajoy K. Ray. Image Processing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471745790.

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Petrou, Maria, e Pedro García Sevilla. Image Processing. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/047003534x.

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E, Pearson D., a cura di. Image processing. London: McGraw-Hill, 1991.

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National Institutes of Health (U.S.). Division of Computer Research and Technology., a cura di. Image processing. [Bethesda, Md: Division of Computer Research and Technology, National Institutes of Health, 1985.

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Henri, Maître, a cura di. Image processing. London: ISTE Ltd., 2008.

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Acharya, Tinku. Image Processing. New York: John Wiley & Sons, Ltd., 2005.

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Capitoli di libri sul tema "Image processin"

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Klemm, Anna, e Kota Miura. "Batch Processing Methods in ImageJ". In Bioimage Data Analysis Workflows ‒ Advanced Components and Methods, 7–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76394-7_2.

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Abstract (sommario):
AbstractIn this chapter you will learn how to execute a workflow on not only one image but on several images in ImageJ – a technique that is called “Batch Processing”. Various ways of doing this are possible in the Fiji distribution of ImageJ, and the characteristics of each and how-to are explained.
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Wolff, Robert S., e Larry Yaeger. "Images and Image Processing". In Visualization of Natural Phenomena, 1–26. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0646-7_1.

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Corke, Peter. "Images and Image Processing". In Springer Tracts in Advanced Robotics, 359–411. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54413-7_12.

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Corke, Peter, Witold Jachimczyk e Remo Pillat. "Images and Image Processing". In Springer Tracts in Advanced Robotics, 435–91. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-07262-8_11.

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Corke, Peter. "Images and Image Processing". In Springer Tracts in Advanced Robotics, 417–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-06469-2_11.

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Corke, Peter. "Images and Image Processing". In Robotic Vision, 103–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79175-9_4.

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Cree, Michael J., e Herbert F. Jelinek. "Image Analysis of Retinal Images". In Medical Image Processing, 249–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_11.

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Arheit, Marcel, Daniel Castaño-Díez, Raphaël Thierry, Bryant R. Gipson, Xiangyan Zeng e Henning Stahlberg. "Image Processing of 2D Crystal Images". In Methods in Molecular Biology, 171–94. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-176-9_10.

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Hamann, Sabine, Hauke Hell, Detlef Pankow e Robert Wunderer. "Image Processing". In DigiScript™, 321–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-46829-2_39.

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Ortega-Alcalde, D. "Image processing". In Developments in Cardiovascular Medicine, 29–42. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1984-9_2.

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Atti di convegni sul tema "Image processin"

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Jariwala, Devanshi, e Ruchi Gajjar. "Image Processing Based Texture Transfer Between Images". In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739041.

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Alu, Andrea. "Metasurfaces for Image Processing and Analog Computing". In Laser Science, LW7F.2. Washington, D.C.: Optica Publishing Group, 2024. https://doi.org/10.1364/ls.2024.lw7f.2.

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I discuss our recent progress in demonstrating ultrathin engineered surfaces that perform image processing and computing on optical signals and images, and the opportunities for photonic technologies. Full-text article not available; see video presentation
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Smuleac, Adrian, Laura Smuleac, Raul Pascalau, George Popescu e Adina Horablaga. "USING GROUND CONTROL POINTS (GCP) AND UAV POIND CLOUD PROCESSIN� IN WATER MANAGEMENT". In 22nd International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022v/3.2/s12.27.

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Using aerial vehicles without pilot (UAV-unmanned aerial vehicle) or Drone has seen a rapid development, over the last decade, in order to obtain spatial information of the Earth's surface. This scientific paper was realized for the Hydrotechnical Node of Costei, from Timis County and has as purpose the processing of aerial images, obtained from a Phantom4 Pro device, which is capable to capture video at 4K resolution at 30 frames per second and Full HD 1080p at 120 frames per second for a slow motion with a Sony EXMOR camera that can take photos at 12 megapixels, with a maximum flight speed of 20m/s. The device is equipped with positioning equipment, which connects to both GPS and GLONASS, allowing it to connect faster to satellites and position itself with high accuracy in the air. Phantom 4 automatically records the details of each flight made, so you can check your previous flights. In order to achieve the 3D model, were used oblique and vertical images with the highest accuracy. Nadir imaging was performed at an average height above ground (AGL�Above Ground Level) of approx. 140m. The imaging data was processed with the AgiSoft PhotoScan program using a number of 112 aerial images. For image processing, the software proposes for each processing stage, different parameters that determine the precision and time of the final processing of the Costei Hydrotechnical Node.
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Zhang, Chenhao, Yongzhi Jing, Jie Kong, Tao Peng, Zhenzhen Liao e Jianhua Hao. "Method for Measuring Levitation Gap of Magnetic Levitation Ball Based on Image Processin". In 2019 3rd International Conference on Robotics and Automation Sciences (ICRAS). IEEE, 2019. http://dx.doi.org/10.1109/icras.2019.8808955.

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Taylor, T. S., M. M. Wilson, R. L. Hartman e K. B. Farr. "Optical Disk Spatial Light Modulator". In Symposium on Optical Memory. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/isom.1996.otub.6.

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Advances in technology have made optical disk technology for optical signal processing realizable. The limiting factor in optical image processing is the spatial light modulator (liquid crystal or similar devices). Higher resolution and faster filter access makes optical disk technology an attractive alternative. Images are represented onto a CD by a single stream of 1's and 0's read by the optical pickup system. Digital processing is then performed on the image. We propose to write 2-dimensional images directly to a CD as shown in Figure 1. The 2-D images can then be accessed by our optical processor so that we can perform optical image processing.
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Shagalova, P. A., E. S. Sokolova e S. N. Ryndov. "Automation of Blood Microscopy Image Processing". In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-1157-1164.

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Automation of medical image processing aims to create a universal tool for the purposes of medical diagnostics in the field of microscopy of cells and tissues. The authors of the paper have collected large databases of various digital microscopy images that allow to automate the processes of medical research using computer vision technologies, improve the quality of image analysis and provide a set of diagnostic information for decision making. For this purpose, a software package with a user-friendly interface has been developed that allows visualizing the results of detection of microobjects in images, determining their number and size, calculating the values of universal numerical parameters of the detected objects, creating a distribution of numerical parameter values in the form of histograms and diagrams, displaying the results in a user-friendly form, and saving the analysis results for further research. It is possible to analyze the dynamics of biomedical processes using a set of images. The modular architecture of the developed software package allows to extend its functionality, add new modules to solve biomedical problems and visualize the results of image processing. The paper presents the results of image processing of blood microscopy to determine its parameters, which include the characteristics of erythrocytes, platelets, and blood cell aggregation processes. In addition, the automated image processing system is suitable for solving problems of microscopy image analysis in other application areas.
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Boothroyd, S. A., P. H. Beckwith, L. Chan e J. Chrostowski. "Multiple Grating Optical Processing in Barium Titanate". In Photorefractive Materials, Effects, and Devices II. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/pmed.1991.wc18.

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Nonlinear optical image subtraction and Boolean XOR operations have been demonstrated in Michelson interferometers with phase conjugate mirrors [1-3], The two interferometer arms each contain a light modulating transmission or reflection element which must be carefully registered and imaged at the interferometer output. Destructive interference between the output images of the two arms arises because one image undergoes a extra π phase change on reflection from a dielectric interface. Image subtraction has also been achieved in photorefractives via double exposure holography [4] where two gratings are formed by displacing the reference beam in phase by π. A π phase shift on one of the writing beams in four wave mixing has also been used to selectively erase volume holograms in photoreffactive media [5-7].
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Wong, Chan, Carlos Montes, Laine Mears e John Ziegert. "A New Position Feedback Method for Manufacturing Equipment". In ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing. ASMEDC, 2008. http://dx.doi.org/10.1115/msec_icmp2008-72222.

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This paper presents an innovative real time 2-dimensional position feedback method, which processes visual input data from a target image on an actively-controlled planar pixel matrix. The objective is to demonstrate the ability to position an X-Y stage with high resolution, using direct position sensing of a dynamically controlled image. In order to achieve high spatial resolution using a pixel array as a target, an algorithm that processes both the geometric shape and the grayscale intensities of the image is implemented. The test platform consists of an X-Y stage carrying a Liquid Crystal Display (LCD) screen that is imaged by a stationary digital camera. The pixel intensities on the LCD screen are modified dynamically to provide 2-dimensional position command inputs that translate to the desired stage motion. The digital images acquired by the camera are used to provide position error feedback to the controller. Experimental results show that direct position sensing is possible to a certain degree of accuracy. However, in order to match today’s CNC machines’ accuracy levels further processing of the digital images is required. A noise reduction algorithm to filter the fluctuations of the measurements in the digital images is proposed as future work, as well as other considerations.
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Stojancic, M., e G. Eichmann. "Superresolving image restoration using an associative memory processor". In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wt10.

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Abstract (sommario):
The issue of superresolving signal and image restoration appears in many areas, such as the restoration of diffraction-limited images, geophysical prospecting, radar and sonar signal processing, biomedical imaging, image and signal bandwidth compression, reconstruction of signals and images from partial and incomplete information, etc. There is a large repertoire of different techniques and procedures available to reconstruct such images. However, most of these techniques work well only in the absence of noise. Because this is an ill-posed problem, the actual reconstruction is sensitive to noise. Recently, new optical signal and image processing techniques have been suggested that are based on associative memory processing concepts. The purpose of this paper is to explore various associative memory processor concepts applied to the superresolving image restoration problem. Results of computer simulation will be presented.
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Lohman, Gary E., e K. H. Brenner. "Optical Morphological Image Processor". In Optical Computing. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/optcomp.1991.tub4.

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Images in the natural sciences often posses distinctive topologies, thus rendering order statistics better suited for image processing than more traditional linear filtering. A useful subclass of order statistics based on binary images is mathematical morphology./1/ Mathematical morphology is also well suited to an optical implementation. /2-5/ Optical mathematical morphology can be performed at a frame rate of 10-100 kHz., thus permitting real-time non-linear image processing in many applications. Our proposed optical architecture also allows for programmable parallel processing of very large images, under control of a small electronic micro-processor.
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Rapporti di organizzazioni sul tema "Image processin"

1

Bickel, V. T., B. Moseley, E. Hauber, M. Shirley, J. P. Williams e D. A. Kring. CHARACTERIZATION OF SHADOWED REGIONS AT THE LUNAR SOUTH POLE. Frontier Development Lab, luglio 2022. http://dx.doi.org/10.56272/gqkz6227.

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This image repository contains a total of 576 non map-projected HORUS (Hyper-effective nOise Removal Unet Software) post-processed images of permanently shadowed regions (PSRs) located across the Artemis exploration zone at the lunar south pole (144 individual images).
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2

Allard, Anthony, Edward Dugan e Alan Jacobs. Image Processing Techniques for Lateral Migration Radiography Land Mine Images. Fort Belvoir, VA: Defense Technical Information Center, giugno 2000. http://dx.doi.org/10.21236/ada384546.

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3

Mbani, Benson, Timm Schoening e Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, maggio 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in AI-SCW have been implemented using the python programming language, specifically using libraries such as scikit-image for image processing, scikit-learn for machine learning and dimensionality reduction, keras for computer vision with deep learning, and matplotlib for generating visualizations. Therefore, AI-SCW modularized implementation allows users to accomplish a variety of underwater computer vision tasks, which include: detecting laser points from the underwater images for use in scale determination; performing contrast enhancement and color normalization to improve the visual quality of the images; semi-automated generation of annotations to be used downstream during supervised classification; training a convolutional neural network (Inception v3) using the generated annotations to semantically classify each image into one of pre-defined seafloor habitat categories; evaluating sampling strategies for generation of balanced training images to be used for fitting an unsupervised k-means classifier; and visualization of classification results in both feature space view and in map view geospatial co-ordinates. Thus, the workflow is useful for a quick but objective generation of image-based seafloor habitat maps to support monitoring of remote benthic ecosystems.
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4

Meitzler, Thomas, Grant Gerhart, Harpreet Singh, S. Bhama, Y. Hamzeh, S. Talahmeh, L. Anneberg e D. Kaur. Image Processing. Fort Belvoir, VA: Defense Technical Information Center, marzo 1999. http://dx.doi.org/10.21236/ada597230.

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5

Rosenfeld, Azriel. Parallel Image Processing and Image Understanding. Fort Belvoir, VA: Defense Technical Information Center, marzo 1986. http://dx.doi.org/10.21236/ada183223.

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6

Rosenfeld, A. Parallel Image Processing and Image Understanding. Fort Belvoir, VA: Defense Technical Information Center, luglio 1985. http://dx.doi.org/10.21236/ada159029.

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7

Van Eeckhout, E., P. Pope e L. Balick. Image processing technology. Office of Scientific and Technical Information (OSTI), luglio 1996. http://dx.doi.org/10.2172/262986.

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8

Chartrand, Rick. Image processing and reconstruction. Office of Scientific and Technical Information (OSTI), giugno 2012. http://dx.doi.org/10.2172/1044085.

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9

Khoury, Jehad, e Mark Cronin-Golomb. Real Time Holographic Image Processing. Fort Belvoir, VA: Defense Technical Information Center, novembre 1996. http://dx.doi.org/10.21236/ada408111.

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10

Giardina, Charles R., e Edward R. Dougherty. Image Processing Language. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, maggio 1988. http://dx.doi.org/10.21236/ada204232.

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