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Relevant bibliographies by topics / Image processing Digital techniques

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Academic literature on the topic 'Image processing Digital techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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Journal articles on the topic "Image processing Digital techniques"

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Kumar Saini, Mukesh, Arun Saini, and Sachin Gupta. "Digital Image Processing Techniques for Leukemia Detection." International Journal of Science and Research (IJSR) 13, no. 6 (June 5, 2024): 1710–19. http://dx.doi.org/10.21275/sr24627090927.

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Maria Riasat. "Research on various image processing techniques." Open Access Research Journal of Chemistry and Pharmacy 1, no. 1 (December 30, 2021): 005–12. http://dx.doi.org/10.53022/oarjcp.2021.1.1.0029.

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Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-pro

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Raghavendra, V., N. Vinay kumar, and Manish Kumar. "Latest advancement in image processing techniques." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 390. http://dx.doi.org/10.14419/ijet.v7i2.12.11357.

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Image processing is method of performing some operations on an image, for enhancing the image or for getting some information from that image, or for some other applications is nothing but Image Processing [1]. Image processing is one sort of signal processing, where input is an image and output may be an image, characteristics of that image or some features that image [1]. Image will be taken as a two dimensional signal and signal processing techniques will be applied to that two dimensional image. Image processing is one of the growing technologies [1]. In many real time applications image p

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Sweeta, J. Anto Germin, and Dr Sivagami B. "Contemporary Techniques in Digital Image Processing." International Journal of Computer Science and Engineering 6, no. 11 (November 25, 2019): 3–46. http://dx.doi.org/10.14445/23488387/ijcse-v6i11p109.

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Pradhan, Manini Monalisa. "Elimination Noise from Image Using Machine Learning Techniques." Oct-Nov 2023, no. 36 (October 20, 2023): 27–36. http://dx.doi.org/10.55529/jipirs.36.27.36.

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The Image Processing system is mostly used because of their easy accessibility of powerful personal computers, bulk memory machines with graphics software and others visual application. Of “Image Processing” is applied in a number of applications. These include in area of Remote Sensing in GIS application, Medical Imaging Processing for patient care application, Forensic Studies, Textiles engineering and design, Material science, Military Research, Film industry application, and Document processing, Graphic arts. An image is defined as an array, or a matrix, square pixel arranged in rows and c

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Mohanty, Sumant Sekhar, and Sushreeta Tripathy. "Application of Different Filtering Techniques in Digital Image Processing." Journal of Physics: Conference Series 2062, no. 1 (November 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2062/1/012007.

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Abstract Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without

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Iqbal, Saima, Wilayat Khan, Abdulrahman Alothaim, Aamir Qamar, Adi Alhudhaif, and Shtwai Alsubai. "Proving Reliability of Image Processing Techniques in Digital Forensics Applications." Security and Communication Networks 2022 (March 31, 2022): 1–17. http://dx.doi.org/10.1155/2022/1322264.

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Binary images have found its place in many applications, such as digital forensics involving legal documents, authentication of images, digital books, contracts, and text recognition. Modern digital forensics applications involve binary image processing as part of data hiding techniques for ownership protection, copyright control, and authentication of digital media. Whether in image forensics, health, or other fields, such transformations are often implemented in high-level languages without formal foundations. The lack of formal foundation questions the reliability of the image processing te

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Durai Anand Thangarajan and Sivasangari Rajeswaran. "Bacteria identification using digital image processing." International Journal of Science and Research Archive 12, no. 2 (July 30, 2024): 818–20. http://dx.doi.org/10.30574/ijsra.2024.12.2.1268.

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The identification of bacteria is an important and unavoidable task in medical disciplines and nutritional hygiene. But in the field of microbiology, No direct method is available for determination of bacterial species. The common manual technique is the microscopic sample analysis combined with more than 20 biochemical tests to identify the bacterium. These tests are more time consuming processes and required the training person for conducting these tests. To overcome the above problems the digital image processing can be used. The primary objective of the proposed work is to use the digital

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A, Suguna, Dinesh B V, Nithin S C, and Adarsh N S. "Image Processing." International Journal of Innovative Research in Information Security 09, no. 03 (June 23, 2023): 79–83. http://dx.doi.org/10.26562/ijiris.2023.v0903.06.

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Image processing entails altering an image's composition to enhance its graphical content for human interpretation and autonomous machine perception. Digital image processing is a subset of the electronic domain in which an image is transformed into a collection of tin y numbers, or pixels, that reflect a physical property, like s cene radiance, are stored in a digital memory, and are then processed by a computer or other digital hardware. Two key application areas have sparked interest in digital i mage processing techniques: improving pictorial information for human interpretation and proces

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P., Himali, Hardik Modi, Manoj Pandya, and M. B. Potdar. "Leukemia Detection using Digital Image Processing Techniques." International Journal of Applied Information Systems 10, no. 1 (November 4, 2015): 43–51. http://dx.doi.org/10.5120/ijais2015451461.

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Dissertations / Theses on the topic "Image processing Digital techniques"

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Bibby, Geoffrey Thomas. "Digital image processing using parallel processing techniques." Thesis, Liverpool John Moores University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304539.

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Cloete, Eric. "Nonlinear smoothers for digital image processing." Thesis, Cape Technikon, 1997. http://hdl.handle.net/20.500.11838/2073.

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Thesis (DTech(Business Informatics))--Cape Technikon, Cape Town, 1997<br>Modem applications in computer graphics and telecommunications command high performance filtering and smoothing to be implemented. The recent development of a new class of max-min selectors for digital image processing is investigated with special emphasis on the practical implications for hardware and software design.

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Seemann, Torsten 1973. "Digital image processing using local segmentation." Monash University, School of Computer Science and Software Engineering, 2002. http://arrow.monash.edu.au/hdl/1959.1/8055.

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Marokkey, Sajan Raphael. "Digital techniques for dynamic visualization in photomechanics." Thesis, Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14670896.

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Musoke, David. "Digital image processing with the Motorola 56001 digital signal processor." Scholarly Commons, 1992. https://scholarlycommons.pacific.edu/uop_etds/2236.

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This report describes the design and testing of the Image56 system, an IBM-AT based system which consists of an analog video board and a digital board. The former contains all analog and video support circuitry to perform real-time image processing functions. The latter is responsible for performing non real-time, complex image processing tasks using a Motorola DSP56001 digital signal processor. It is supported by eight image data buffers and 512K words of DSP memory (see Appendix A for schematic diagram).

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Chen, Shuangli. "Application of digital image processing techniques to multiphase flowmeasurements." Thesis, Heriot-Watt University, 1993. http://hdl.handle.net/10399/1462.

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Hertz, Lois. "Robust image thresholding techniques for automated scene analysis." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15050.

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Lambert, T. "Digital Enhancement Techniques for Underwater Video Image Sequences." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/253/1/tristanlthesis.pdf.

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Due to concern about the current state of the world's oceans, several large scale scientific projects have begun to investigate the condition of our oceans. These projects are making use of underwater video sequences to monitor marine species. The move to using underwater video monitoring introduces labour intensive manual processing techniques. This leads to the need for an automated system capable of processing the data at a much greater speed. This project investigated whether the development of suitable image processing techniques could be used for pre-processing underwater images from a f

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Yau, Chin-ko, and 游展高. "Super-resolution image restoration from multiple decimated, blurred and noisy images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30292529.

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Arrowood, Joseph Louis Jr. "Theory and application of adaptive filter banks." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15369.

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Books on the topic "Image processing Digital techniques"

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Henri, Maître, ed. Image processing. London: ISTE Ltd., 2008.

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Rama, Chellappa, ed. Digital image processing. Los Alamitos, Calif: IEEE Computer Society Press, 1992.

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R, Dougherty Edward, ed. Digital image processing methods. New York: M. Dekker, 1994.

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Rabbani, Majid. Digital image compression techniques. Bellingham, Wash., USA: Spie Optical Engineering Press, 1991.

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Koschan, Andreas. Digital color image processing. Hoboken, N.J: Wiley-Interscience, 2008.

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Collet, Christophe. Multivariate image processing. London, UK: ISTE, 2009.

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Gonzalez, Rafael C. Digital image processing. 2nd ed. Reading, Mass: Addison-Wesley, 1987.

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1954-, Woods Richard E., ed. Digital image processing. 3rd ed. Upper Saddle River, N.J: Prentice Hall, 2008.

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1954-, Woods Richard E., ed. Digital image processing. 2nd ed. Upper Saddle River, N.J: Prentice Hall, 2002.

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1954-, Woods Richard E., ed. Digital image processing. Reading, Mass: Addison-Wesley Pub. Co, 1993.

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Book chapters on the topic "Image processing Digital techniques"

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Ramesh, K. "Colour Image Processing Techniques." In Digital Photoelasticity, 221–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59723-7_7.

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Dalrymple, Brian E., and E. Jill Smith. "Multiple Image Techniques." In Forensic Digital Image Processing, 97–129. Boca Raton, FL : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.4324/9781351112239-4.

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Dalrymple, Brian E., and E. Jill Smith. "Contrast Adjustment Techniques." In Forensic Digital Image Processing, 155–91. Boca Raton, FL : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.4324/9781351112239-6.

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Goldbaum, Michael H., Shankar Chatterjee, Subhasis Chaudhuri, and Norman Katz. "Digital Image Processing for Ophthalmology." In Noninvasive Diagnostic Techniques in Ophthalmology, 548–68. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4613-8896-8_29.

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Marion, André. "Basic techniques of digital image processing." In Introduction to Image Processing, 193–230. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-3186-3_7.

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Burger, Wilhelm, and Mark James Burge. "Introduction to Spectral Techniques." In Principles of Digital Image Processing, 1–32. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-195-4_7.

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Nakamura, Akira, and Azriel Rosenfeld. "Topology-Preserving Deformations of Fuzzy Digital Pictures." In Fuzzy Techniques in Image Processing, 394–404. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1847-5_15.

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Richards, John A. "Geometric Processing and Enhancement: Image Domain Techniques." In Remote Sensing Digital Image Analysis, 127–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30062-2_5.

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Richards, John A. "Geometric Processing and Enhancement: Image Domain Techniques." In Remote Sensing Digital Image Analysis, 135–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82327-6_5.

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Chatzis, Vassilios, and Ioannis Pitas. "Fuzzy Techniques in Digital Image Processing and Shape Analysis." In Fuzzy Filters for Image Processing, 273–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36420-7_12.

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Conference papers on the topic "Image processing Digital techniques"

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CARLOS, BARONETTO, BEGLIARDO HUGO, FORNARI JAVIER, OSHIRO ANGEL, and POSITIERI MARIA. "Concrete Characterization through digital image processing techniques." In Sixth International Conference on Advances in Civil Structural and Mechanical Engineering CSM 2018. Institute of Research Engineers and Doctors, 2018. http://dx.doi.org/10.15224/978-1-63248-150-4-32.

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Otto, Gregory P., Douglas A. Palmer, Jean-Marie Tran, Brett A. Spivey, and Stuart E. Clark. "Image processing techniques for digital breast imaging." In SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew G. Tescher. SPIE, 1996. http://dx.doi.org/10.1117/12.258251.

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Dixiang Chen, Mengchun Pan, and Jianguang Xin. "Image processing techniques of digital scanning pen." In 2008 International Conference on Information and Automation (ICIA). IEEE, 2008. http://dx.doi.org/10.1109/icinfa.2008.4608083.

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Li, Buhong, Shusen Xie, and Zukang Lu. "Bronchoscopic fluorescence image enhancement using digital image processing techniques." In Multispectral Image Processing and Pattern Recognition, edited by Jayaram K. Udupa and Aaron Fenster. SPIE, 2001. http://dx.doi.org/10.1117/12.440273.

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Fujita, Kosaku, Masayuki Kobayashi, and Tomoharu Nagao. "Data Augmentation using Evolutionary Image Processing." In 2018 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2018. http://dx.doi.org/10.1109/dicta.2018.8615799.

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Cooke, Tristrom. "Two Applications of Graph-Cuts to Image Processing." In 2008 Digital Image Computing: Techniques and Applications. IEEE, 2008. http://dx.doi.org/10.1109/dicta.2008.32.

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Udawant, Sandip R., and Satyawati S. Magar. "Digital image processing by using GMSK." In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016. http://dx.doi.org/10.1109/icacdot.2016.7877676.

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Silvestre, G. C. M. "Image watermarking using digital communication techniques." In 6th International Conference on Image Processing and its Applications. IEE, 1997. http://dx.doi.org/10.1049/cp:19970933.

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Ye, Jing, Liang Zhao, Xin Zhao, Han Gao, and Shan Liu. "Advancements in intra prediction techniques beyond AV1 codec." In Applications of Digital Image Processing XLVI, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2023. http://dx.doi.org/10.1117/12.2681783.

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Khalaj, Babak H., Hamid K. Aghajan, and Thomas Kailath. "Digital image processing techniques for patterned-wafer inspection." In SPIE'S 1993 Symposium on Microlithography, edited by Michael T. Postek. SPIE, 1993. http://dx.doi.org/10.1117/12.148982.

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Reports on the topic "Image processing Digital techniques"

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

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Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we dev

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Cathey, W. T., E. R. Dowski, Sara Bradburn, and Greg Johnson. Matched Image Formation/Digital Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada328217.

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Benedetto, John J. New Techniques in Signal and Image Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada380026.

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Cross, James E., and Jiecai Luo. Advanced Image Processing Techniques for Maximum Information Recovery. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada464688.

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Lee, Roger. Radar Signal/Image Processing Enhancements using Alpha-Stable Techniques. Fort Belvoir, VA: Defense Technical Information Center, June 1999. http://dx.doi.org/10.21236/ada367774.

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Jones, David W., and Scott A. Sandgathe. Automated Verification of Mesoscale Forecasts using Image Processing Techniques. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629993.

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E. B. Cummings. Image processing, adaptive gridding, and optimal nonlinear filtering techniques for particle-image velocimetry. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/750922.

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Alverson, James R. Characterization of Periodically Poled Nonlinear Materials Using Digital Image Processing. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada486654.

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Wang, Lei. Simulation, Measurements and Image Processing for Capillary Optical Digital Mammography. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada360959.

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Murenzi, Romain. The Workshop on Image Processing Applications for the Digital Battlefield. Fort Belvoir, VA: Defense Technical Information Center, June 2000. http://dx.doi.org/10.21236/ada388059.

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