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Peng, Er Bao, and Guo Tong Zhang. "Image Processing Technology Research of On-Line Thread Processing." Advanced Materials Research 908 (March 2014): 555–58. http://dx.doi.org/10.4028/www.scientific.net/amr.908.555.
The paper introduced image processing technology based on image segmentation about on-line threads images, and describes in detail image processing technology from image preprocessing, image gmentation,and threaded parameter test. Threaded images of on-line processing parts obtained are introduced as the key technology, Target edge extraction process from the segmented image are also recounted. At last, this article shows a comparison between actual machining parameters of screw thread and the standard parameter , provides the criterion for error compensation.
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Goto, Mitsunori. "8. Image Processing Using ImageJ." Japanese Journal of Radiological Technology 75, no. 7 (2019): 688–92. http://dx.doi.org/10.6009/jjrt.2019_jsrt_75.7.688.
Patel, Bindiya, Dr Pankaj Kumar Mishra, and 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 (April 30, 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.
Legland, David, and Marie-Françoise Devaux. "ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab." F1000Research 10 (April 30, 2021): 333. http://dx.doi.org/10.12688/f1000research.51732.1.
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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Bardhan, Yash, Tejas A. Fulzele, and Prabhat Ranjan Shekhar Upadhyay Prof V. D. Bharate. "Emotion Recognition using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1523–26. http://dx.doi.org/10.31142/ijtsrd10995.
G., Shete S., and Ghadge Nagnath G. "Image Processing in MATLAB 9.3." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 925–29. http://dx.doi.org/10.31142/ijtsrd9545.
Gaikwad, Anil P., and Bhagyashri R. More. "Digital Watermarking for Image Processing." Paripex - Indian Journal Of Research 2, no. 1 (January 15, 2012): 65–67. http://dx.doi.org/10.15373/22501991/jan2013/24.
A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image en-hancement algorithms, thus facilitating easier determi-nation of interesting solar burst activities from recorded images/movies.
Digital processing and subsequent picture identification are one of the scientific fields that is now experiencing rapid development. Currently, a lot of technology is focused on developing systems that use graphical images as information, including receiving, processing, storing, and transmitting information. Two primary areas of use for digital image processing methods are of interest: 1. Increasing image quality to enhance human visual perception. 2. Image processing for use in autonomous machine vision systems, including storage, transmission, and presentation. The fundamentals of digital image processing are covered in the first portion, and image operations including quantization, sampling, and alpha compositing are covered in the second. Bitmap storage is covered in the fourth part, and image compression algorithms such as RLE and LZW are covered in the third. The topic of enhancing image quality is covered in the fifth part. The concepts of grouping, segmenting, and object search in an image are covered in the sixth section. The Radon transform is used in the seventh section to discover grid structures and straight lines in an image. Keywords: image processing, quantization, segmenting
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Naz, Najia, Abdul Haseeb Malik, Abu Bakar Khurshid, Furqan Aziz, Bader Alouffi, M. Irfan Uddin, and Ahmed AlGhamdi. "Efficient Processing of Image Processing Applications on CPU/GPU." Mathematical Problems in Engineering 2020 (October 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/4839876.
Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.
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.
In todays heterogeneous network environment, there is a growing demand for distrusted parties to jointly execute distributed algorithms on private data whose secrecy needed to be safeguarded. Platforms that support such computation on image processing purposes are called secure image processing protocols. In this thesis, we propose a new security model, called quasi information theoretic (QIT) security. Under the proposed model efficient protocols on two basic image processing algorithms linear filtering and thresholding are developed. For both problems we consider two situations: 1) only two parties are involved where one holds the data and the other possesses the processing algorithm; 2) an additional non-colluding third party exists. Experiments show that our proposed protocols improved the computational time significantly compared with the classical cryptographical couterparts as well as providing reasonable amount of security as proved in the thesis
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Zhang, Yi. "Blur Image Processing." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1448384360.
Kim, Younhee. "Towards lower bounds on distortion in information hiding." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3403.
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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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.
Baabd, A., M. Y. Tymkovich, and О. Г. Аврунін. "Image Processing of Panoramic Dental X-Ray Images." Thesis, ХГУ, 2018. http://openarchive.nure.ua/handle/document/6204.
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.
National Institutes of Health (U.S.). Division of Computer Research and Technology., ed. Image processing. [Bethesda, Md: Division of Computer Research and Technology, National Institutes of Health, 1985.
Wolff, Robert S., and 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.
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.
Corke, Peter, Witold Jachimczyk, and 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.
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.
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.
van der Heijden, Ferdi, and Luuk Spreeuwers. "Image Processing." In Multimedia Retrieval, 125–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_5.
Тези доповідей конференцій з теми "Image processing":
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Meinel, Edward S. "Origins of linear and nonlinear recursive restoration algorithms." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/qlip.1986.wb2.
In a recent paper Frieden1 unified the theory behind many nonlinear image restoration methods. He proposed that these methods be implemented using the Newton-Raphson iterative approach. Although this approach is fast, exhibiting quadratic convergence, it requires complex programming and large core storage capabilities even for modest image sizes, unless the problem can be reduced to line-by-line processing2. By contrast, recursive or replacement formulas permit interactive processing with modest storage requirements, albeit with slower convergence. They can also be implemented on a specialized image processing computer since the formulas use only simple algebraic operations. These recursive algorithms also have the capability of immediate display of intermediate images, which is especially useful in interactive processing.
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Prachetaa, R., and B. P. C. Rao. "Image processing for NDT images." In 2010 International Conference on Signal and Image Processing (ICSIP). IEEE, 2010. http://dx.doi.org/10.1109/icsip.2010.5697463.
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.
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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Shagalova, P. A., E. S. Sokolova, and 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.
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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"Image processing." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377338.
Belousov, O. I., and Z. M. Albekova. "Image processing." In Научные тенденции: Вопросы точных и технических наук. ЦНК МОАН, 2018. http://dx.doi.org/10.18411/spc-12-08-2018-03.
Perlovsky, Leonid I. "Neural Network for Adaptive Processing of Quantum Limited Images." In Quantum-Limited Imaging and Image Processing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/qlip.1989.mc3.
Processing of quantum limited images requires optimal utilization of all the available information: all the registered photons, prior information such as geometrical and intensity model of imaged objects formulated in terms of relatively small number of parameters, and real time and a posteriori information that could be available from experts or from other sensors. Such an approach to adaptive information fusion image processing is presented here based on maximum likelihood neural network developed previously by the author for adaptive classification and sensor fusion[1,2,3].
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Tokumitsu, J., H. Matsuoka, and K. Iijima. "Optical Parallel Image Processing Using CCD Image Sensor." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/optcomp.1987.me10.
Image processing is one of the most promising application fields of optical computing in which parallelism inherent in optics well matches two dimensional nature of images. Convolution is a basic operation in preprocessing images, and various kinds of methods for optically implementing it have been proposed1–3.
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Skelton, Jerry P., Anthony P. Cavallo, and Julie Peternick. "Document Image Processing The New Image Processing Frontier." In 33rd Annual Techincal Symposium, edited by Andrew G. Tescher. SPIE, 1990. http://dx.doi.org/10.1117/12.962346.
Blake, Richard E. "Digital image processing of underwater images." In 2006 IEEE US/EU Baltic International Symposium on Integrated Ocean Observation Syst. for Managing Global & Regional Ecosys.Marine Resch. IEEE, 2006. http://dx.doi.org/10.1109/baltic.2006.7266195.
Meitzler, Thomas, Grant Gerhart, Harpreet Singh, S. Bhama, Y. Hamzeh, S. Talahmeh, L. Anneberg, and D. Kaur. Image Processing. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada597230.
Rosenfeld, Azriel. Parallel Image Processing and Image Understanding. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada183223.
Rosenfeld, A. Parallel Image Processing and Image Understanding. Fort Belvoir, VA: Defense Technical Information Center, July 1985. http://dx.doi.org/10.21236/ada159029.
Van Eeckhout, E., P. Pope, and L. Balick. Image processing technology. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/262986.
Chartrand, Rick. Image processing and reconstruction. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1044085.
Khoury, Jehad, and Mark Cronin-Golomb. Real Time Holographic Image Processing. Fort Belvoir, VA: Defense Technical Information Center, November 1996. http://dx.doi.org/10.21236/ada408111.
Giardina, Charles R., and Edward R. Dougherty. Image Processing Language. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada204232.
George, Nicholas, and David M. Berfanger. Halftoning and Image Processing Algorithms. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada369915.
Winkler, Robert P., and Chris Schlesiger. Image Processing REST Web Services. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada577345.
Allard, Anthony, Edward Dugan, and Alan Jacobs. Image Processing Techniques for Lateral Migration Radiography Land Mine Images. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada384546.
