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Relevant bibliographies by topics / Software for image processing

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Academic literature on the topic 'Software for image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Software for image processing"

1

Hanaki, Shin-ichi, Masao Iwashita, and Hirokatsu Terajima. "Image processing software." Journal of the Institute of Television Engineers of Japan 41, no. 1 (1987): 103–11. http://dx.doi.org/10.3169/itej1978.41.103.

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Anonymous. "Acoustic image-processing software." Eos, Transactions American Geophysical Union 70, no. 49 (1989): 1539. http://dx.doi.org/10.1029/eo070i049p01539-02.

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Strauss, Lourens Jochemus, and William ID Rae. "Image quality dependence on image processing software in computed radiography." South African Journal of Radiology 16, no. 2 (2012): 44–48. http://dx.doi.org/10.4102/sajr.v16i2.305.

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Background. Image post-processing gives computed radiography (CR) a considerable advantage over film-screen systems. After digitisation of information from CR plates, data are routinely processed using manufacturer-specific software. Agfa CR readers use MUSICA software, and an upgrade with significantly different image appearance was recently released: MUSICA2. Aim. This study quantitatively compares the image quality of images acquired without post-processing (flatfield) with images processed using these two software packages. Methods. Four aspects of image quality were evaluated. An aluminium step-wedge was imaged using constant mA at tube voltages varying from 40 to 117kV. Signal-to-noise ratios (SNRs) and contrast-to-noise Ratios (CNRs) were calculated from all steps. Contrast variation with object size was evaluated with visual assessment of images of a Perspex contrast-detail phantom, and an image quality figure (IQF) was calculated. Resolution was assessed using modulation transfer functions (MTFs). Results. SNRs for MUSICA2 were generally higher than the other two methods. The CNRs were comparable between the two software versions, although MUSICA2 had slightly higher values at lower kV. The flatfield CNR values were better than those for the processed images. All images showed a decrease in CNRs with tube voltage. The contrast-detail measurements showed that both MUSICA programmes improved the contrast of smaller objects. MUSICA2 was found to give the lowest (best) IQF; MTF measurements confirmed this, with values at 3.5 lp/mm of 10% for MUSICA2, 8% for MUSICA and 5% for flatfield. Conclusion. Both MUSICA software packages produced images with better contrast resolution than unprocessed images. MUSICA2 has slightly improved image quality than MUSICA.

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POWELL, MARK W., and DMITRY GOLDGOF. "SOFTWARE TOOLKIT FOR TEACHING IMAGE PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (2001): 833–44. http://dx.doi.org/10.1142/s0218001401001180.

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We introduce a software framework called the Java Vision Toolkit (JVT) for teaching image processing and computer vision. The toolkit provides over 50 image operations and presents them to the user in a GUI that can render grayscale, color and 3D range images. The software is written in Java, enabling it to be integrated into HTML documents and interactive course materials. The framework is designed for extensibility using a source code template that supports the implementation of any new operation with a minimal amount of supporting code. For students, this framework encapsulates the GUI, file I/O and other trivial programming details and allows them the maximum amount of time to spend on understanding computer vision. We compare the JVT with other computer vision software frameworks that are used for teaching and research. We also discuss the use of the JVT in an undergraduate image processing course at the University of South Florida.

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Kishore, M. Sankar, and M. S. Prasad. "Interactive Image Processing Software Package." IETE Journal of Education 30, no. 2 (1989): 45–55. http://dx.doi.org/10.1080/09747338.1989.11436233.

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PRAKASH, C. V. S., I. C. MATIEDA, D. DHAR SATISHKUMAR, P. GEETA, and H. GARG. "SACIMAGE—image processing software library." International Journal of Remote Sensing 14, no. 17 (1993): 3353–60. http://dx.doi.org/10.1080/01431169308904451.

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Kidode, Masatsugu. "Image processing hardware and software." Journal of the Institute of Television Engineers of Japan 39, no. 6 (1985): 526–33. http://dx.doi.org/10.3169/itej1978.39.526.

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Böhm, P. "The Potsdam image processing software." Astronomische Nachrichten: A Journal on all Fields of Astronomy 318, no. 4 (1997): 213–16. http://dx.doi.org/10.1002/asna.2113180407.

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S V, Uma, and Balasubramanyam V. "MORPHOMETRY OF GLENOID USING DIGITAL PHOTOGRAPHS AND IMAGE PROCESSING SOFTWARE." International Journal of Anatomy and Research 4, no. 3.2 (2016): 2720–24. http://dx.doi.org/10.16965/ijar.2016.316.

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Kaimaris, Dimitris, Petros Patias, and Maria Sifnaiou. "UAV and the comparison of image processing software." International Journal of Intelligent Unmanned Systems 5, no. 1 (2017): 18–27. http://dx.doi.org/10.1108/ijius-12-2016-0009.

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Purpose The purpose of this paper is to discuss unmanned aerial vehicle (UAV) and the comparison of image processing software. Design/methodology/approach Images from a drone are used and processed with new digital image processing software, Imagine UAV® of Erdas imagine 2015®. The products (Digital Surface Model and ortho images) are validated with check points (CPs) measured in the field with Global Positioning System. Moreover, similar products are produced by Agisoft PhotoScan® software and are compared with both the products of Imagine UAV and the CPs. Findings The results reveal that the two software tools are almost equivalent, while the accuracies of their products are similar to the accuracies of the external orientations of drone images. Originality/value Comparison of image processing software.

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Dissertations / Theses on the topic "Software for image processing"

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Lloyd, M. J. B. "Image processing software in OCCAM." Thesis, Cardiff University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382624.

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Fleury, Martin. "Efficient parallel image-processing software." Thesis, University of Essex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361038.

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Justin, Ken. "Development of electrocardiographic image processing software." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/72288.

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Lang, Kathrin. "Software for calibrating a digital image processing." Master's thesis, Pontificia Universidad Católica del Perú, 2014. http://tesis.pucp.edu.pe/repositorio/handle/123456789/5350.

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This work is about learning tool wich provides the necessary parameters for a program controlling robots of type LUKAS at the Faculty of Mechanical Engineering. The robot controlling program needs various parameters depending on its environment, like the light intensity distribution, and camera settings as exposure time and gain raw. These values have to be transmitted from the learning tool to the robot controlling software. Chapter one introduces the robots of type LUKAS which are created for the RoboCup Small Size League. Furthermore, it introduces the camera used for image processing. The second chapter explains the learning process according to Christoph UBfeller and deduces the requirements for this work. In the third chapter theoretical basics concerning image processing, wich are fundamental for this work, are explained. Chapter 4 describes the developed learning tool which is used for the learning process and generates the required parameters for the robot controlling software. In chapter five practical test with two persons are represented. The sixth and last chapter summarizes the results.<br>Tesis

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Levillain, Roland. "Towards a software architecture for generic image processing." Phd thesis, Université Paris-Est, 2011. http://pastel.archives-ouvertes.fr/pastel-00673121.

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In the context of software engineering for image processing (IP), we consider the notion of reusability of algorithms. In many software tools, an algorithm's implementation often depends on the type of processed data. In a broad definition, discrete digital images may have various forms : classical 2D images, 3D volumes, non-regular graphs, cell complexes, and so on : thus leading to a combinatorial explosion of the theoretical number of implementations. Generic programming (GP) is a framework suited to the development of reusable software tools. We present a programming paradigm based on GP designed for the creation of scientific software such as IP tools. This approach combines the benefits of reusability, expressive power, extensibility, and efficiency. We then propose a software architecture for IP using this programming paradigm based on a generic IP library. The foundations of this framework define essential IP concepts, enabling the development of algorithms compatible with many image types. We finally present a strategy to build high-level tools on top of this library, such as bridges to dynamic languages or graphical user interfaces. This mechanism has been designed to preserve the genericity and efficiency of the underlying software tools, while making them simpler to use and more flexible

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Bailey, D. G. "Hardware and software developments for applied digital image processing." Thesis, University of Canterbury. Electrical and Electronic Engineering, 1985. http://hdl.handle.net/10092/7704.

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This thesis discusses some of the problems and practices of applying digital image processing to scientific and industrial problems. An interactive image processing system is described. This system has been found to be useful for the development of image processing commands and algorithms to solve image processing problems. An image capture subsystem based around a 576 x 385 element frame transfer charge coupled device is described. This system has a variable integration time, giving it considerable flexibility over more conventional cameras. The properties of the RANK filter are described. The usefulness of this nonlinear filter is demonstrated by its performance on a number of common image processing tasks. The edge detection properties of RANGE filters, which are the difference between the outputs of two RANK filters, are discussed. This family of filters has considerable flexibility in the type of output response obtained, but was found to be slightly more sensitive to noise than the commonly used SOBEL filter. A new iterative solution to the Fourier phase retrieval problem is described. This solution is less efficient than current solutions, but does prove the optimality of an operation common to existing iterative methods of solution. An approach to developing algorithms to solve image processing problems is described. The various commands that may be used at each stage are discussed. Although this approach does not say which command should be used when, it does provide a guide. The algorithms developed for three applications are presented. These are: the calibration of a photoelastic material used in the measurement of the foot-ground pressure pattern of a standing person; the measurement of areas within stem disk growth rings; and the detection of surface blemishes of kiwifruit being graded for export.

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Naylor, M. J. "Software, silicon chips and structured lighting for image processing." Thesis, University of Canterbury. Electrical and Electronic Engineering, 1990. http://hdl.handle.net/10092/7710.

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This thesis describes three projects, each of which addresses a limitation of current image processing technology. The projects comprise the development of software that provides a user friendly means of manipulating images on an interactive image processing system; the design of a prototype integrated circuit, and the subsequent design of a set of integrated circuits that perform rank filtering; and a way in which lighting can be used to rapidly acquire three dimensional information from a scene. These projects address the need for an easy to use image processing system, the need for a high speed means of filtering images, and the need in robotics applications for rapid three dimensional image acquisition. Conclusions are drawn from these developments, and suggestions are made for future work.

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Morrow, Philip. "Software development tools for parallel image processing on transputers." Thesis, Queen's University Belfast, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359117.

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Kelly, D. E. "KWIK : a software environement for digital picture processing." Thesis, Cardiff University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277032.

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Smith, Craig M. "Efficient software implementation of the JBIG compression standard /." Online version of thesis, 1993. http://hdl.handle.net/1850/11713.

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Books on the topic "Software for image processing"

1

Inter University Software Committee Graphics Working Party. Image processing: Software evaluation report. AGOCG, 1993.

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Laplante, Phillip A. Software Engineering for Image Processing Systems. Taylor and Francis, 2003.

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Software engineering for image processing systems. CRC Press, 2004.

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Paskevich, Valerie F. Woods Hole image processing system software implementation: Using NetCDF as a software interface for image processing. U.S. Geological Survey, 1992.

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Paskevich, Valerie F. Woods Hole image processing system software implementation: Using NetCDF as a software interface for image processing. U.S. Geological Survey, 1992.

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S, Aguado Alberto, ed. Feature extraction and image processing. Newnes, 2002.

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Ranka, Sanjay. Hypercube Algorithms: With Applications to Image Processing and Pattern Recognition. Springer New York, 1990.

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Mizrakian, V. Software acceleration of image processing algorithms using hardware implementation. UMIST, 1997.

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Mizrakian, V. Software acceleration of image processing algorithms using hardware implementation. UMIST, 1997.

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The digital photographer's software guide. Course Technology, 2009.

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Book chapters on the topic "Software for image processing"

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Moore, Patrick, and John Watson. "Image Processing Software." In Patrick Moore's Practical Astronomy Series. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2161-0_7.

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Moore, Patrick, and John Watson. "Image Processing Software." In Astronomy with a Budget Telescope. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3765-8_7.

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Mobberley, Martin. "Image-Processing Software." In The New Amateur Astronomer. Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-0639-5_8.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Companion Image Processing Software." In Digital Signal Processing. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_9.

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Paulus, Dietrich W. R., and Joachim Hornegger. "Software Development." In Pattern Recognition and Image Processing in C++. Vieweg+Teubner Verlag, 1995. http://dx.doi.org/10.1007/978-3-322-87867-0_3.

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Wolf, Ivo. "Toolkits and Software for Developing Biomedical Image Processing and Analysis Applications." In Biomedical Image Processing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15816-2_21.

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Ratledge, David. "CCD Imaging and Image Processing." In Software and Data for Practical Astronomers. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0555-8_8.

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Lindblad, Thomas, and Jason M. Kinser. "Software and Hardware Realisation." In Image Processing using Pulse-Coupled Neural Networks. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3617-0_11.

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Mobberley, Martin. "Image Processing, Planetarium and Telescope Control Software." In Astronomical Equipment for Amateurs. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0583-1_9.

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Rasoul Banaeeyan, K. A., Y. K. Chiam, Z. H. Azizul, T. K. Chiew, S. H. Ab Hamid, and T. Thasaratharajah. "Classification of Image Processing Software Tools for Cardiovascular Image Analysis." In IFMBE Proceedings. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0266-3_15.

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Conference papers on the topic "Software for image processing"

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Frei, Werner. "Digital Image Processing Software." In 1985 Los Angeles Technical Symposium, edited by Andrew G. Tescher. SPIE, 1985. http://dx.doi.org/10.1117/12.946409.

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Bin, Du, Wang Rupeng, and Chen Shiming. "Photocarrier Image Processing Software System." In 1988 International Congress on Optical Science and Engineering, edited by Peter J. Hutzler and Andre J. Oosterlinck. SPIE, 1989. http://dx.doi.org/10.1117/12.950288.

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Zou, Kun, Yueqiao Li, and Chunjian Deng. "Demonstration software for digital image processing." In 2010 International Conference on Educational and Network Technology (ICENT 2010). IEEE, 2010. http://dx.doi.org/10.1109/icent.2010.5532138.

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Mazer, Alan S., Miki Martin, Meemong Lee, and Jerry E. Solomon. "Image Processing Software For Imaging Spectrometry." In 31st Annual Technical Symposium, edited by Gregg Vane. SPIE, 1987. http://dx.doi.org/10.1117/12.942293.

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Martinez, K., and J. Cupitt. "VIPS - a highly tuned image processing software architecture." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530120.

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Liu, Sining, Lin Li, and Wei Wang. "Hardware-Software Codesign for Geological Image Processing." In 2006 International Conference on Communications, Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icccas.2006.285213.

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Bhattacharya, Prabir, and Kai Qian. "Software development: image processing by template polynomials." In the 1990 ACM annual conference. ACM Press, 1990. http://dx.doi.org/10.1145/100348.100391.

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McLean, G. F., E. D. Graham, and M. E. Jernigan. "A software environment for teaching image processing." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226476.

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Mendi, Deniz, Fatih Kahraman, and Cengiz Huroglu. "IYON: Forensic image investigation software." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204846.

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Cozens, S. "A software image coding system." In Fifth International Conference on Image Processing and its Applications. IEE, 1995. http://dx.doi.org/10.1049/cp:19950615.

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Reports on the topic "Software for image processing"

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Rasure, J., S. Hallett, and R. Jordan. A comprehensive software system for image processing and programming. Final report. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/446387.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Reconition and Image Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada289153.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada295580.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada315017.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada310034.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada305420.

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Henry, Wendell A. High Performance Hardware and Software for Pattern Recognition and Image Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada276405.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.

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Meitzler, Thomas, Grant Gerhart, Harpreet Singh, et al. Image Processing. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada597230.

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

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