Thematische Bibliographien / Image processing Digital techniques

Auswahl der wissenschaftlichen Literatur zum Thema „Image processing Digital techniques“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 7. September 2023

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Zeitschriftenartikel zum Thema "Image processing Digital techniques"

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Maria Riasat. „Research on various image processing techniques“. Open Access Research Journal of Chemistry and Pharmacy 1, Nr. 1 (30.12.2021): 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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Sweeta, J. Anto Germin, und Dr Sivagami B. „Contemporary Techniques in Digital Image Processing“. International Journal of Computer Science and Engineering 6, Nr. 11 (25.11.2019): 3–46. http://dx.doi.org/10.14445/23488387/ijcse-v6i11p109.

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Raghavendra, V., N. Vinay kumar und Manish Kumar. „Latest advancement in image processing techniques“. International Journal of Engineering & Technology 7, Nr. 2.12 (03.04.2018): 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 processing is widely used. In the field of bio technology, computer science, in medical field, envi-ronmental areas etc., image processing is being used for mankind benefits. The following steps are the basics of image processing:Image is taken as an inputImage will be processed (manipulation, analyzing the image, or as per requirement)Altered image will be the outputImage processing is of two typesAnalog Image Processing:As the name implies, analog image processing is applied on analog signals. Television image is best example of analog signal processing [1].(DIP) Digital Image Processing:DIP techniques are used on images, which are in the format of digital for processing them, and get the required output as per the application. Operations were applied on the digital images for processing [1].In this paper, we will discuss about the technologies or tools for image processing especially by using Open CV. With the help of Open CV image processing will be very easy and efficient. When Open CV is collaborated or integrated with python the results are mind blowing. We will discuss about the process of using python and Open CV.
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Mohanty, Sumant Sekhar, und Sushreeta Tripathy. „Application of Different Filtering Techniques in Digital Image Processing“. Journal of Physics: Conference Series 2062, Nr. 1 (01.11.2021): 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 the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.
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Iqbal, Saima, Wilayat Khan, Abdulrahman Alothaim, Aamir Qamar, Adi Alhudhaif und Shtwai Alsubai. „Proving Reliability of Image Processing Techniques in Digital Forensics Applications“. Security and Communication Networks 2022 (31.03.2022): 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 techniques and hence the forensic results loose their legal significance. Furthermore, counter-forensics can impede or mislead the forensic analysis of the digital images. To ensure that any image transformation meet high standards of safety and reliability, more rigorous methods should be applied to image processing applications. To verify the reliability of these transformations, we propose to use formal methods based on theorem proving that can fulfil high standards of safety. To formally investigate binary image processing, in this paper, a reversible formal model of the binary images is defined in the Proof Assistant Coq. Multiple image transformation methods are formalized and their reliability properties are proved. To analyse real-life RGB images, a prototype translator is developed that reads RGB images and translate them to Coq definitions. As the formal definitions and proof scripts can be validated automatically by the computer, this raises the reliability and legal significance of the image forensic applications.
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A, Suguna, Dinesh B V, Nithin S C und Adarsh N S. „Image Processing“. International Journal of Innovative Research in Information Security 09, Nr. 03 (23.06.2023): 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 processing image data for storage, transmission, and representation for autonomous machine perception. Boundaries are described by edges, and as edge detection i s one of the most challenging image processing tasks, it is an issue of fundamental significance .
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P., Himali, Hardik Modi, Manoj Pandya und M. B. Potdar. „Leukemia Detection using Digital Image Processing Techniques“. International Journal of Applied Information Systems 10, Nr. 1 (04.11.2015): 43–51. http://dx.doi.org/10.5120/ijais2015451461.

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Kumar, Arvind, und Ashutosh Sharma. „Multiresolution Transform Techniques in Digital Image Processing“. International Journal of Computer Applications 123, Nr. 12 (18.08.2015): 44–49. http://dx.doi.org/10.5120/ijca2015905530.

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McKenna, S. P., und W. R. McGillis. „Performance of digital image velocimetry processing techniques“. Experiments in Fluids 32, Nr. 1 (01.01.2002): 106–15. http://dx.doi.org/10.1007/s003480200011.

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Döler, W., N. Steinhöfel und A. Jäger. „Digital image processing techniques for cephalometric analysis“. Computers in Biology and Medicine 21, Nr. 1-2 (Januar 1991): 23–33. http://dx.doi.org/10.1016/0010-4825(91)90032-5.

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Dissertationen zum Thema "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
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 fish farm and locating fish within these images using computer vision techniques. Using underwater images leads to some serious problems when compared to images from a clearer environment. Visibility in an underwater environment is poor, even when using state of the art equipment. After reviewing the broad field of computer vision and current underwater projects, an image pre-processing system was developed in MATLAB using suitable image processing and analysis techniques. The application developed was able to successfully locate an acceptable number of fish within the underwater images. The project demonstrated that automated analysis of underwater video images is needed and is possible. Automatic processing of large quantities of video image sequences will be of great benefit in the future. It will allow scientific researchers to study the ocean environment and its species more effectively. Pre-processing is an essential component of the overall process that will lead to automation of underwater video data analysis for marine science applications.
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Yau, Chin-ko, und 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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Bücher zum Thema "Image processing Digital techniques"

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Castleman, Kenneth R. Digital image processing. Englewood Cliffs, N.J: Prentice Hall, 1996.

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Pratt, William K. Digital image processing. 2. Aufl. New York: Wiley, 1991.

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

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

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Teuber, Jan. Digital image processing. New York: Prentice Hall, 1993.

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Lewis, Rhys. Practical digital image processing. New York: Ellis Horwood, 1990.

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

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

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

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

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Buchteile zum Thema "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., und 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., und 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 und 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, und 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, und 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, und 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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Konferenzberichte zum Thema "Image processing Digital techniques"

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CARLOS, BARONETTO, BEGLIARDO HUGO, FORNARI JAVIER, OSHIRO ANGEL und 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 und Stuart E. Clark. „Image processing techniques for digital breast imaging“. In SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, herausgegeben von Andrew G. Tescher. SPIE, 1996. http://dx.doi.org/10.1117/12.258251.

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Dixiang Chen, Mengchun Pan und 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 und Zukang Lu. „Bronchoscopic fluorescence image enhancement using digital image processing techniques“. In Multispectral Image Processing and Pattern Recognition, herausgegeben von Jayaram K. Udupa und Aaron Fenster. SPIE, 2001. http://dx.doi.org/10.1117/12.440273.

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Fujita, Kosaku, Masayuki Kobayashi und 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., und 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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Khalaj, Babak H., Hamid K. Aghajan und Thomas Kailath. „Digital image processing techniques for patterned-wafer inspection“. In SPIE'S 1993 Symposium on Microlithography, herausgegeben von Michael T. Postek. SPIE, 1993. http://dx.doi.org/10.1117/12.148982.

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M B, Suresh, und Abhishek M R. „Kidney Stone Detection Using Digital Image Processing Techniques“. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2021. http://dx.doi.org/10.1109/icirca51532.2021.9544610.

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Berichte der Organisationen zum Thema "Image processing Digital techniques"

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Bhatt, Parth, Curtis Edson und 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 developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS.
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Cathey, W. T., E. R. Dowski, Sara Bradburn und Greg Johnson. Matched Image Formation/Digital Processing Systems. Fort Belvoir, VA: Defense Technical Information Center, Mai 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, Dezember 1999. http://dx.doi.org/10.21236/ada380026.

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Cross, James E., und 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, Juni 1999. http://dx.doi.org/10.21236/ada367774.

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Jones, David W., und 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, Juni 2000. http://dx.doi.org/10.21236/ada388059.

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