Relevant bibliographies by topics / Adaptive image processing

Academic literature on the topic 'Adaptive image processing'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Adaptive image processing"

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Debayle, Johan, and Jean-Charles Pinoli. "General Adaptive Neighborhood Image Processing:." Journal of Mathematical Imaging and Vision 25, no. 2 (August 14, 2006): 245–66. http://dx.doi.org/10.1007/s10851-006-7451-8.

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Debayle, Johan, and Jean-Charles Pinoli. "General Adaptive Neighborhood Image Processing." Journal of Mathematical Imaging and Vision 25, no. 2 (August 14, 2006): 267–84. http://dx.doi.org/10.1007/s10851-006-7452-7.

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Aleksander, I., and M. J. Dobree Wilson. "Adaptive windows for image processing." IEE Proceedings E Computers and Digital Techniques 132, no. 5 (1985): 233. http://dx.doi.org/10.1049/ip-e.1985.0034.

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Zhertunova, T. V., and E. S. Yanakova. "ADAPTIVE ALGORITHM BASED ON NONLOCAL MEANS IN IMAGE PROCESSING." Issues of radio electronics, no. 8 (August 20, 2018): 79–86. http://dx.doi.org/10.21778/2218-5453-2018-8-79-86.

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This article describes the existing problem situation associated with the absence of resource-lights denoising algorithms, capable to produce good-quality output images in the different intensity noise conditions without blurring the boundaries, contours and basic structure. The adaptive algorithm proposed in the article allows to solve this problem due to the developed algorithms of splitting the search region into two sets of similar and points different from the pixel and adapting of the kernel type to the image region, depending on the presence or detection of structural and smooth pixels. The results of the proposed algorithm and the standard method of nonlocal means are compared with the metrics of the peak signal-to-noise ratio and structural similarity. It is found out that the developed adaptive algorithm is surpass by far than the standard method both on numerical results and on the quality of the image processing.
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Zhang, Lei, Guiping Zheng, Kai Zhang, Yongfeng Wang, Changming Chen, Liting Zhao, Jiquan Xu, et al. "Study on the Extraction of CT Images with Non-Uniform Illumination for the Microstructure of Asphalt Mixture." Materials 15, no. 20 (October 20, 2022): 7364. http://dx.doi.org/10.3390/ma15207364.

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An adaptive image-processing method for CT images of asphalt mixture is proposed in this paper. Different methods are compared according to the error analysis calculated between the real gradation and 3D reconstruction gradation. As revealed by the test results, the adaptive image-processing method was effective in carrying out different brightness homogenization processes for each image. The Wiener filter with 7 × 7 size filter was able to produce a better noise reduction effect without compromising image sharpness. Among the three methods, the adaptive image-processing method performed best in the accuracy of coarse aggregate recognition, followed by the ring division method and the global threshold segmentation method. The error of the gradation extracted by the adaptive image-processing method was found to be lowest compared with the real gradation. For a variety of engineering applications, the developed method helps to improve the analysis of CT images of asphalt mixtures.
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Teuner, A., and B. J. Hosticka. "Adaptive Gabor transformation for image processing." IEEE Transactions on Image Processing 2, no. 1 (1993): 112–17. http://dx.doi.org/10.1109/83.210872.

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McLean, G. F., and M. E. Jernigan. "Indicator functions for adaptive image processing." Journal of the Optical Society of America A 8, no. 1 (January 1, 1991): 141. http://dx.doi.org/10.1364/josaa.8.000141.

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Papanikolaou, V., K. N. Plataniotis, and A. N. Venetsanopoulos. "Adaptive filters for color image processing." Mathematical Problems in Engineering 4, no. 6 (1999): 529–38. http://dx.doi.org/10.1155/s1024123x98000957.

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The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to knowa priorithe type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf) (B.D. Jeffs and W.H. Pun,IEEE Transactions on Image Processing,4(10), 1451–1456, 1995 andProceedings of the Int. Conference on Image Processing,465–468, 1996), is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.
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Richter, G. M., P. Böhm, H. Lorenz, A. Priebe, and M. Capaccioli. "Adaptive filtering in astronomical image processing." Astronomische Nachrichten: A Journal on all Fields of Astronomy 312, no. 6 (1991): 345–49. http://dx.doi.org/10.1002/asna.2113120602.

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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-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.
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Dissertations / Theses on the topic "Adaptive image processing"

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Yakoubian, Jeffrey Scott. "Adaptive histogram equalization for mammographic image processing." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16387.

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Hadhoud, M. M. "The adaptive LMS alogrithm in image processing." Thesis, University of Southampton, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380631.

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Wong, Hau San. "Adaptive image processing using computational intelligence techniques." Thesis, The University of Sydney, 1998. https://hdl.handle.net/2123/27658.

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In this thesis, we illustrate the essential aspects of the adaptive image processing problem in terms of two applications: the adaptive assignment of the regularization parameters in image restoration, and the adaptive characterization of edges in feature detection applications. These two problems are representative of the general adaptive image processing paradigm in that the three requirements for its successive implementation: namely the segmentation of an image into its main feature types, the characterization of each of these features, and the optimization of the image model parameters corresponding to the individual features, are present. In view of these requirements, we have adopted the three main approaches within the class of computational intelligence algorithms, namely neu— ral network techniques, fuzzy set theory, and evolutionary computation, for solving the adaptive image processing problem. This is in view of the direct correspondence between some of the above requirements with the particular capabilities of specific computational intelligence approaches. We first applied neural network techniques to the adaptive regularization problem in image restoration. Instead of the usual approach of selecting the regularization parameter values by trial and error, we adopt a learning approach by treating the parameters in various local image regions as network weights of a model—based neural network with hierarchical architecture (HMBNN), such that they are adjustable through the supply of training examples specifying the desired image quality. In addition, we also applied the HMBNN to the problem
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Riehle, Thomas J. "Adaptive bilateral extensor for image interpolation." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4555.

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Thesis (M.S.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (February 23, 2007) Includes bibliographical references.
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Shen, Liang. "Region-based adaptive image processing techniques for mammography." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0011/NQ34701.pdf.

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Coleman, Sonya. "Scalable operators for adaptive processing of digital images." Thesis, University of Ulster, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270447.

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Podd, Frank J. W. "Medical X-ray dose reduction including adaptive image processing." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/842724/.

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This thesis investigates possible methods for dose reduction for one of the main contributors to medical x-ray dose, that of fluoroscopic examinations. Background information is provided on the subjects of radiation interaction mechanisms, radiation dose measures, and the health risk from medical x-rays. This illuminates a running theme of the thesis, namely the compromise between image quality and low patient dose. Possible dose reduction methods using both spatial and temporal image processing techniques are investigated. Edge detection is one of the most important sub-components of the spatial image processing system. The commonly used edge detectors are investigated from a theoretical viewpoint and their performances under Poisson noise conditions are compared using receiver operating characteristic analysis. A new metric is suggested for the quantitative comparison of the edge operators under high detection and low false alarm probability conditions. An adaptive pulse dropping control system is created in order to use the image processing sub-systems with low-dose examinations. This control system determines the best x-ray tube pulse-rate based on the amount of movement present in the image. A method of distributing the dose so that areas of high clinical importance have a higher image quality than less important regions is discussed. This method uses a wedge-shaped x-ray beam filter. The problem of varying pixel intensity due to the differing filter thickness is countered by rescaling the image. The various image processing techniques are combined to create a low-dose imaging system. This system achieves a dose reduction of an order of magnitude.
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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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Moussa, Badi M. S. "Adaptive transform coding for digital image communication." Thesis, Loughborough University, 1985. https://dspace.lboro.ac.uk/2134/27360.

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The performance of transform image coding schemes can be improved substantially by adapting to changes in image statistics. Essentially, this is accomplished through adaptation of the transform, bit allocation, and/or quantization parameters according to time-varying image statistics. Additionally adaptation can be used to achieve transmission rate reduction whilst maintaining a given picture quality.
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Price, Jeffery Ray. "A framework for adaptive image interpolation." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13718.

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Books on the topic "Adaptive image processing"

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Safonov, Ilia V., Ilya V. Kurilin, Michael N. Rychagov, and Ekaterina V. Tolstaya. Adaptive Image Processing Algorithms for Printing. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6931-4.

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Hui, Yap Kim, and Perry Stuart William, eds. Adaptive image processing: A computational intelligence perspective. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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Yap, Kim Hui. Adaptive image processing: A computational intelligence perspective. 2nd ed. Boca Raton: Taylor & Francis, 2010.

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Hau-San, Wong, and Guan Ling, eds. Adaptive image processing: A computational intelligence perspective. Boca Raton, FL: CRC Press, 2002.

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Brady, Martin. Adaptive digital multiprocessor systems for image processing applications. Preston: Lancashire Polytechnic, 1986.

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Bhanu, Bir. Genetic learning for adaptive image segmentation. Boston: Kluwer Academic Publishers, 1994.

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Sekihara, Kensuke. Adaptive spatial filters for electromagnetic brain imaging. Berlin: Springer, 2008.

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Cichocki, Andrzej. Adaptive blind signal and image processing: Learning algorithms and applications. Chichester: J. Wiley, 2002.

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Kani, Bijan. Enhanced logical adaptive systems for image processing and pattern recognition. Uxbridge: Brunel University, 1993.

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Titarenko, Larysa. Methods of Signal Processing for Adaptive Antenna Arrays. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Adaptive image processing"

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Adaptive Image Filters." In Digital Signal Processing, 107–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_3.

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Mironov, Rumen. "Local Adaptive Image Processing." In New Approaches in Intelligent Image Analysis, 295–330. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32192-9_9.

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Safonov, V. Ilia, V. Ilya Kurilin, N. Michael Rychagov, and V. Ekaterina Tolstaya. "Image Upscaling." In Adaptive Image Processing Algorithms for Printing, 195–215. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6931-4_8.

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Safonov, Ilia V., Ilya V. Kurilin, Michael N. Rychagov, and Ekaterina V. Tolstaya. "Adaptive Sharpening." In Adaptive Image Processing Algorithms for Printing, 85–104. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6931-4_4.

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Debayle, Johan, and Jean-Charles Pinoli. "Spatially Adaptive Color Image Processing." In Lecture Notes in Computational Vision and Biomechanics, 195–222. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7584-8_6.

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Giacinto, Giorgio, and Fabio Roli. "Adaptive selection of image classifiers." In Image Analysis and Processing, 38–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_182.

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Serra, J. R., and J. Brian Subirana. "Adaptive non-cartesian networks for vision." In Image Analysis and Processing, 324–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_139.

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Booth, Martin J. "Adaptive Optics in Microscopy." In Optical and Digital Image Processing, 295–322. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527635245.ch14.

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Semmlow, John L., and Benjamin Griffel. "Optimal and Adaptive Filters." In BIOSIGNAL and MEDICAL IMAGE PROCESSING, 247–72. 3rd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/b16584-8.

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Camacho, Pelegrín, Fabián Arrebola, and Francisco Sandoval. "Adaptive fovea structures for space-variant sensors." In Image Analysis and Processing, 422–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_228.

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Conference papers on the topic "Adaptive image processing"

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Currie, Douglas G., Petras V. Avizonis, Daniel M. Dowling, Dianne P. O'Leary, James G. Nagy, and Robert Q. Fugate. "Approaches for Image Processing Supporting Adaptive Optics." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.tua50.

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Image processing methods, highlighting specific hardware systems, will be demonstrated. Correcting spatial dependence and instrumental artifacts of the Point-Spread-Function significantly improves image guality.
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Paranjape, Raman B., Rangaraj M. Rangayyan, William M. Morrow, and H. N. Nguyen. "Adaptive-neighborhood image processing." In Applications in Optical Science and Engineering, edited by Petros Maragos. SPIE, 1992. http://dx.doi.org/10.1117/12.131438.

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Lee, James S., Paul V. Budak, Charlotte R. Lin, and Robert M. Haralick. "Adaptive Image Processing Techniques." In Robotics and IECON '87 Conferences, edited by David P. Casasent and Ernest L. Hall. SPIE, 1988. http://dx.doi.org/10.1117/12.942745.

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Lukin, Igor P. "Atmosphere Potentialities of the Methods of the Posteriory Processing of Incoherently Illuminated Objects through the Turbulent." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/adop.1995.tua22.

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Optical transfer function (OTF) and the integral resolution of the optical system "turbulent atmosphere - telescope" are treated theoretically by different methods of posteriory processing of incoherently illuminated objects observed through the turbulent atmosphere. The following processing methods are examined: the averaged image recording ("very long" averaging times) and short-exposure images ("very short" averaging times), namely, Labeyrie, Knox-Thompson, and triple correlation of image intensity methods. The influence of the finite value of the turbulent atmosphere outer and inner scales on the OTF's under consideration is also estimated.
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Christou, Julian C., E. Keith Hege, and Stuart M. Jefferies. "Post-Processing of Adaptive Optics Images: Blind Deconvolution Analysis." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.awa.1.

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Adaptive Optics (AO) has the capability of providing diffraction-limited images from ground-based astronomical telescopes through the turbulent atmosphere. Because of limitations in the AO system, the point spread functions (PSF’s) of the AO system suffers from incomplete compensation and variability. Depending on the observation wavelength (λ), the spatial coherence length of the atmosphere (r0), the sub-aperture size (d), the correlation time of the atmosphere (τ0), the sample time of the wavefront sensing (t s ), and the signal strength of the source, the Strehl ratios of the compensated images can vary considerably (between 2% – 90%). In addition, residual errors in tilt compensation due to the source signal strength can further degrade the image quality. Thus, AO compensated imaging genearally requires some post-processing to extract the maximum possible information. As long as the PSF for the imaging process is stationary, then standard deconvolution algorithms can be applied. These algorithms have been recently developed and applied to Hubble Space Telescope imaging and include maximum-likelihood, maximum-entropy and pixon-based algorithms, etc.[1].
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Kolchaev, Dmitry A., Yevgeniy R. Muratov, Michael B. Nikiforov, and Victor S. Gurov. "Adaptive system of image processing." In 2017 6th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2017. http://dx.doi.org/10.1109/meco.2017.7977181.

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Yonga, Franck, Christophe Bobda, Jakob Anders, and Abdelaziz Benshair. "Adaptive Video Streaming." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.759-086.

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Yonga, Franck, Christophe Bobda, Jakob Anders, and Abdelaziz Benshair. "Adaptive Video Streaming." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.759-086.

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Kronemeijer, Pieter S., Efstratios Gavves, Jan-Jakob Sonke, and Jonas Teuwen. "Tumor tracking in 4D CT images for adaptive radiotherapy." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2022. http://dx.doi.org/10.1117/12.2612954.

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Gonsalves, Robert A., Steven M. Ebstein, Peter Nisenson, and Chris Shelton. "Phase Diversity Imaging: Report of Experiments and Simulations." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.atub.6.

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We report research results on phase diversity imaging, a technique for pre and post processing of atmospherically degraded images. The method uses an in-focus image and an out-of-focus Image to deduce the aberrating wavefront introduced by the atmosphere and telescope; and deconvolves the measured images to produce a near-diffraction-limited estimate of the object.
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Reports on the topic "Adaptive image processing"

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Janni, Joseph, and Stuart Jefferies. Consortium for Adaptive Optics and Image Post-Processing. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada483613.

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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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CAMERON, STEWART M. Adaptive Sensor Optimization and Cognitive Image Processing Using Autonomous Optical Neuroprocessors. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/789525.

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Boehm, Wim, Bruce Draper, and Ross Beveridge. Cameron - Optimized Compilation of Visual Programs for Image Processing on Adaptive Computing Systems (ACS). Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada407678.

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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