Relevant bibliographies by topics / Optical pattern recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Optical pattern recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Optical pattern recognition"

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Solus, Dávid, Ľuboš Ovseník, and Ján Turán. "Microchip Pattern Recognition Based on Optical Correlator." Acta Electrotechnica et Informatica 17, no. 2 (June 1, 2017): 38–42. http://dx.doi.org/10.15546/aeei-2017-0014.

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Kumar, Virendra. "Guest Editorial: Optical Pattern Recognition." Optical Engineering 29, no. 9 (1990): 993. http://dx.doi.org/10.1117/12.150767.

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Refregier, Ph. "Optical pattern recognition: optimal trade-off circular harmonic filters." Optics Communications 86, no. 2 (November 1991): 113–18. http://dx.doi.org/10.1016/0030-4018(91)90544-n.

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Mahlab, Uri, H. John Caulfield, and Joseph Shamir. "Genetic algorithm for optical pattern recognition." Optics Letters 16, no. 9 (May 1, 1991): 648. http://dx.doi.org/10.1364/ol.16.000648.

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Tozer, B. "Optical pattern recognition using holographic techniques." Optics & Laser Technology 20, no. 5 (October 1988): 274. http://dx.doi.org/10.1016/0030-3992(88)90032-1.

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Mahlab, Uri, Michael Fleisher, and Joseph Shamir. "Error probability in optical pattern recognition." Optics Communications 77, no. 5-6 (July 1990): 415–22. http://dx.doi.org/10.1016/0030-4018(90)90137-i.

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Parrish, E. A., A. O. Anyiwo, and T. E. Batchman. "Integrated optical processors in pattern recognition." Pattern Recognition 18, no. 3-4 (January 1985): 227–40. http://dx.doi.org/10.1016/0031-3203(85)90048-2.

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Carhart, Gary W., Bret F. Draayer, and Michael K. Giles. "Optical pattern recognition using bayesian classification." Pattern Recognition 27, no. 4 (April 1994): 587–606. http://dx.doi.org/10.1016/0031-3203(94)90039-6.

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Chang, Shoude, Philippe Gagné, and Henri H. Arsenault. "Optical Intensity Filters for Pattern Recognition." Journal of Modern Optics 42, no. 10 (October 1995): 2041–50. http://dx.doi.org/10.1080/09500349514551771.

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Liu, Hua-Kuang. "Self-amplified optical pattern-recognition technique." Applied Optics 31, no. 14 (May 10, 1992): 2568. http://dx.doi.org/10.1364/ao.31.002568.

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Dissertations / Theses on the topic "Optical pattern recognition"

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An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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Manivannan, Nadarajah. "Multiplexed matched filters for optical pattern recognition." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363782.

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Mankoff, Jennifer C. "An architecture and interaction techniques for handling ambiguity in recognition-based input." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8214.

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Wong, Vincent. "Human face recognition /." Online version of thesis, 1994. http://hdl.handle.net/1850/11882.

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Lam, Yat-kin, and 林日堅. "Intelligent lexical access based on Chinese/English text queries." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B30445474.

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Calvo-Zaragoza, Jorge. "Pattern Recognition for Music Notation." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/63415.

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Fujinaga, Ichiro. "Optical music recognition using projections." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61870.

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Biles, Jonathan R. "Optical analysis of microscope images /." Full text open access at:, 1986. http://content.ohsu.edu/u?/etd,120.

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Qi, Ying. "Novel Optical Technique for Real-Time Pattern/Image Recognition." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/36446.

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We propose a novel real-time joint-Transform correlation (JTC) technique for optical pattern recognition. To replace the film recording aspect of performing optical correlation, conventional real-time joint-Transform correlation (JTC) optical systems make use of a spatial light modulator (SLM) located in the Fourier plane to record the interference intensity to achieve real-time processing. However, the use of a SLM in the Fourier plane, is a major drawback in these systems since SLMs are limited in resolution, phase uniformity and contrast ratio. Thus, they are not desirable for robust applications. In this thesis, we developed a hybrid (optical/electronic) processing technique to achieve real-time joint-Transform correlation (JTC). The technique employs acousto-optic heterodyning scanning. The proposed real-time JTC system does not require a SLM in the Fourier plane as in conventional real-time JTC systems. This departure from the conventional scheme is extremely important, as the proposed approach does not depend on SLM issues. We have developed the theory of the technique and substantiated it with optical experimental as well as computer simulation results.
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Nadar, Mariappan Srirangam 1965. "Hybrid phase-only matched filter for optical pattern recognition." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/278105.

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Optical matched filters have been used for the recognition of patterns in a noisy background. Different types of matched filters have been proposed since the introduction of the VanderLugt matched spatial filter. A novel filter, the hybrid phase-only matched filter, is proposed which shows promise for better signal to noise ratio, correlation peak intensity and light efficiency compared to the recently proposed optimal phase-only filter. A neural technique for the design of space-domain binary filter for pattern recognition applications is developed. The method takes advantage of the similarity in the structure of the minimum squared error criterion for the construction of linear discriminant functions and the Lyapunov function of the Hopfield Neural Model.
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Books on the topic "Optical pattern recognition"

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Yu, Francis T. S., 1932- and Jutamulia Suganda, eds. Optical pattern recognition. Cambridge, U.K: Cambridge University Press, 1998.

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Paul, Casasent David, Chao Tien-Hsin, and Society for Photo-optical Instrumentation Engineers., eds. Optical pattern recognition V. Bellingham, Wash: The Society, 1994.

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Paul, Casasent David, Chao Tien-Hsin, and Society for Photo-optical Instrumentation Engineers., eds. Optical pattern recognition III. Bellingham, Wash: SPIE, 1992.

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Paul, Casasent David, Chao Tien-Hsin, and Society for Photo-optical Instrumentation Engineers., eds. Optical pattern recognition XI. Bellingham, Wash: SPIE, 2000.

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National Institute of Standards and Technology (U.S.), ed. Optical pattern recognition with microlasers. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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1967-, Koutroumbas Konstantinos, and ScienceDirect (Online service), eds. Pattern recognition. 4th ed. Amsterdam: Elsevier/Academic Press, 2009.

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Dawson, K. M. Object recognition techniques. Dublin: Trinity College, Department of Computer Science, 1991.

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Miller, Richard Kendall. Optical pattern recognition for machine vision. Madison, GA: SEAI Technical Publications, 1986.

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Impedovo, Sebastiano. Fundamentals in Handwriting Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994.

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Bahram, Javidi, Réfrégier Philippe, and European Optical Society, eds. Euro-American workshop on optical pattern recognition. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1994.

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Book chapters on the topic "Optical pattern recognition"

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Lebreton, G., E. Marom, N. Konforti, and D. Mendlovic. "Invariant Pattern Recognition: Towards Neural Network Classifiers." In Optical Information Technology, 76–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78140-7_9.

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Amin, Adnan, and Sameer Singh. "Optical character recognition: Neural network analysis of hand-printed characters." In Advances in Pattern Recognition, 492–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033271.

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Ding, Jianpeng, Jinhong Deng, Yanru Zhang, Shaohua Wan, and Lixin Duan. "Unsupervised Domain Adaptation for Optical Flow Estimation." In Pattern Recognition and Computer Vision, 44–56. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8435-0_4.

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Chamorro-Martínez, Jesús, Javier Martínez-Baena, Elena Galán-Perales, and Beén Prados-Suárez. "Dealing with Multiple Motions in Optical Flow Estimation." In Pattern Recognition and Image Analysis, 52–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492429_7.

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Lucena, Manuel, Nicolás Pérez de la Blanca, José Manuel Fuertes, and Manuel Jesús Marín-Jiménez. "Human Action Recognition Using Optical Flow Accumulated Local Histograms." In Pattern Recognition and Image Analysis, 32–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02172-5_6.

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Sánchez, Javier, Agustín Salgado, and Nelson Monzón. "An Efficient Algorithm for Estimating the Inverse Optical Flow." In Pattern Recognition and Image Analysis, 390–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_46.

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Lucena, Manuel J., José M. Fuertes, Nicolas Perez de la Blanca, Antonio Garrido, and Nicolás Ruiz. "Probabilistic Observation Models for Tracking Based on Optical Flow." In Pattern Recognition and Image Analysis, 462–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_54.

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Xu, Leyang, and Zongqing Lu. "ReFlowNet: Revisiting Coarse-to-fine Learning of Optical Flow." In Pattern Recognition and Computer Vision, 429–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88004-0_35.

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Hellwich, Olaf, Cornelius Wefelscheid, Jakub Lukaszewicz, Ronny Hänsch, M. Adnan Siddique, and Adam Stanski. "Integrated Matching and Geocoding of SAR and Optical Satellite Images." In Pattern Recognition and Image Analysis, 798–807. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_95.

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Atienza, Vicente, Ángel Rodas, Gabriela Andreu, and Alberto Pérez. "Optical Flow-Based Segmentation of Containers for Automatic Code Recognition." In Pattern Recognition and Data Mining, 636–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_70.

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Conference papers on the topic "Optical pattern recognition"

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Casasent, David. "Optical pattern recognition." In Critical Review Collection. SPIE, 1992. http://dx.doi.org/10.1117/12.161585.

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Lu, Jianyi, and T. William Lin. "Adaptive pattern recognition for binary images." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.thx4.

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It is understood that the correlation process between similar patterns generally introduces ambiguity in decision making. This problem is much more profound in the binary pattern recognition process. For example, the cross-correlation between letters E and F exhibits the same main peak intensity as that in the autocorrelation of letter F. Many attempts have been made to overcome this difficulty, including the recognition technique based on a comparison of pattern’s perimeters, and the recognition of patterns in moment spaces or feature spaces. However, the phenomenon that causes the ambiguity can be converted and utilized as a feedback parameter when an adaptive process scheme is chosen in pattern recognition. In this paper, the relationship between pattern shapes and correlation results is analyzed first, followed by a proposed hybrid optical-electronic adaptive joint transform correlator. The adaptive capability of the system is achieved through interfacing between an optical correlator and a computer. The intensity distribution of correlation peaks detected in the optical correlator serves as a feedback to update reference images in the input plane of the correlator, so that an optimal decision can be made for the recognition process through adaptive iterations. In the iteration process, the saturating phenomenon in the intensity of correlation peaks is used as a guideline in designing a proper feedback scheme. Several uses in binary pattern recognition are demonstrated with results obtained from computer simulations as well as experimentally.
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Mendlovic, D., E. Marom, and N. Konforti. "Scale Invariant Pattern Recognition." In Optical Computing '88, edited by Pierre H. Chavel, Joseph W. Goodman, and Gerard Roblin. SPIE, 1989. http://dx.doi.org/10.1117/12.947906.

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Liu, Hua-Kuang. "Bifurcative optical pattern recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.wo3.

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A new bifurcative optical pattern recognizer (BOPAR) for image and/or data classification using the noise fan-out phenomenon in nonlinear photorefractive crystals is described. The inhomogeneities in photorefractive crystals naturally exits consequential to the doping procedure in making the crystals photorefractive. Scattering occurs when light impinges on the inhomogeneities. The scattered light is in general random in intensity distribution and angular orientation. Therefore such scattered light is considered as annoying noise that tends to reduce the strength of the signal. In this paper, a new and exciting discovery is reported. Contrary to the conventional conviction, we found that certain portions of the fan-out noise can be made useful for achieving bifurcative optical holographic pattern recognition and data classification. The BOPAR has a neuromorphic or brainlike nature.
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Caulfield, H. John. "Smart optical pattern recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.tuw3.

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Artificial intelligence applied to hardware such as an optical pattern recognition system is the method such a system uses to optimize its preference in view of its endowments and limitations. Human vision, for example, has strong learned components. Two people with physiologically identical vision systems would undoubtedly see differently because they have self-programmed themselves differently. Likewise humans learn to accommodate for injuries, imperfections, degradation, etc.
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Hong, John H., and Pochi Yeh. "Trainable Optical Network for Pattern Recognition." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/optcomp.1989.tui22.

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Harrer, Thomas, and Azad Siahmakoun. "Hybrid pattern recognition system." In Optical Engineering Midwest 1992, edited by Robert J. Heaston. SPIE, 1992. http://dx.doi.org/10.1117/12.140952.

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Haken, H. "Pattern Formation, Pattern Recognition, and Associative Memory." In Nonlinear Dynamics in Optical Systems. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nldos.1992.wc2.

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Spatial and temporal patterns can be spontaneously formed in a variety of systems treated in physics, chemistry, biology and other disciplines. Such patterns may be coherent oscillations in the laser and their interactions with each other, spatio-temporal patterns in fluids, chemical reactions and a great variety of morphogenetic processes in biology.
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Mahlab, Uri, Michael Fleisher, and Joseph Shamir. "Entropy-Optimized Filter for Pattern Recognition." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/optcomp.1989.tui17.

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One of the procedures in the generation of spatial filters for pattern recognition starts from the correlation plane and defines a desired output pattern such as the Synthetic Discriminant Function (SDF) and the desired filter is generated to yield that output for a given input.
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Refregier, Ph. "A Figure of Merit for Pattern Recognition Filters." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/optcomp.1991.me27.

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The design of filters for optical pattern recognition is extensively studied for more than ten years [1] [2] [3] [4] [5]. Indeed, optical correlation implementation leads to specific constraints in comparisons with classical signal processing technics [6]. In particular, although the spatial matched filter is optimal for noise robustness, its limitations such as broad correlation peaks and low diffraction efficiency [3] are well known. Many different approaches have improved some of these characteristics and a lot of work is still devoted to find trade-offs between them [7] [8] [9] [10] [11] [12].
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Reports on the topic "Optical pattern recognition"

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Paek, Eung Gi. Optical pattern recognition with microlasers. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6017.

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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, December 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This system has the improved chick holding cups and motion mechanisms that enable chicks to open wings through the view section. The mechanical system has achieved the speed of 4 chicks per second which exceeds the design specs of 3 chicks per second. In the center of the conveyor, a high-speed camera with UV sensitive optical system, shown in Fig.2, was installed that captures chick images at multiple frames (45 images and system selectable) when the chick passing through the view area. Through intensive discussions and efforts, the PIs of Maryland and ARO have created the protocol of joint hardware and software that uses sequential images of chick in its fall motion to capture opening wings and extract the optimal opening positions. This approached enables the reliable feather feature extraction in dynamic motion and pattern recognition. Improving of Chick Wing Deployment The mechanical system for chick conveying and especially the section that cause chicks to deploy their wings wide open under the fast video camera and the UV light was investigated along the third study year. As a natural behavior, chicks tend to deploy their wings as a mean of balancing their body when a sudden change in the vertical movement was applied. In the latest two years, this was achieved by causing the chicks to move in a free fall, in the earth gravity (g) along short vertical distance. The chicks have always tended to deploy their wing but not always in wide horizontal open situation. Such position is requested in order to get successful image under the video camera. Besides, the cells with checks bumped suddenly at the end of the free falling path. That caused the chicks legs to collapse inside the cells and the image of wing become bluer. For improving the movement and preventing the chick legs from collapsing, a slowing down mechanism was design and tested. This was done by installing of plastic block, that was printed in a predesign variable slope (Fig. 3) at the end of the path of falling cells (Fig.4). The cells are moving down in variable velocity according the block slope and achieve zero velocity at the end of the path. The slop was design in a way that the deacceleration become 0.8g instead the free fall gravity (g) without presence of the block. The tests showed better deployment and wider chick's wing opening as well as better balance along the movement. Design of additional sizes of block slops is under investigation. Slops that create accelerations of 0.7g, 0.9g, and variable accelerations are designed for improving movement path and images.
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Kurt Beran, John Christenson, Dragos Nica, and Kenny Gross. Development of a Pattern Recognition Methodology for Determining Operationally Optimal Heat Balance Instrumentation Calibration Schedules. Office of Scientific and Technical Information (OSTI), December 2002. http://dx.doi.org/10.2172/806854.

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Glass, R., and L. Hendler. Defect Recognition In Regularly Patterned Substrates Using Optical Fourier Transform Techniques Final Report CRADA No. TSB-1164-95. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1430944.

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Glass, R. Defect Recognition In Regularly Patterned Substrates Using Optical Fourier Transform Techniques Final Report CRADA No. TSB-1164-95. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/759917.

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Gribok, Andrei V. Performance of Advanced Signal Processing and Pattern Recognition Algorithms Using Raw Data from Ultrasonic Guided Waves and Fiber Optics Transducers. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1495185.

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Psuty, Norbert, Tanya Silveira, Andrea Habeck, Dennis Skidds, Sara Stevens, Katy Ames, and Glenn Liu. Northeast Coastal and Barrier Network geomorphological monitoring protocol: Part II ? coastal topography, version 2. National Park Service, 2024. http://dx.doi.org/10.36967/2301966.

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Coastal topography was ranked as one of the most important variables for monitoring following a review of potential vital signs in the coastal parks of the Northeast Coastal and Barrier Network (NCBN). Changes in coastal topography, whether caused by erosion or accretion, vary both spatially and temporally. Understanding these variations is key to early recognition of potential problems affecting natural and cultural resources in coastal parks. For managers, understanding spatial and temporal patterns of geomorphologic change is basic to optimal management of any coastal park because the interface of marine and land systems 1) is highly dynamic and driven by multiple forcing mechanisms, 2) results in alterations to resource patterns and dynamics at habitat and ecosystem levels, and 3) can eventually result in the loss of static resources. The establishment of local, long-term monitoring programs help us to understand the processes that are driving coastal change of beaches, dunes, and bluffs within the parks. This Coastal Topography Monitoring Protocol has been developed for use in the Northeast Coastal and Barrier Network parks. Monitoring is accomplished with survey-grade Global Positioning System (GPS)/Global Navigation Satellite Systems (GNSS) equipment that collects topographic data along pre-established transects spaced at regular intervals, augmented by more intense data-collection in areas of special concern to the parks. A network of high-quality survey control monuments (often referred to as benchmarks), used as accuracy assessment reference is located within each of the NCBN parks, providing a robust basis for long-term monitoring. Spring and/or fall surveys conducted in accordance with standard operating procedures generate coastal topography datasets that are organized and assembled by the NCBN data manager into a database for analysis and archival purposes. Dimensional parameters are measured to describe the beach-dune-bluff system, and attributes are compared and analyzed in a cross-shore and alongshore perspective, providing information about the temporal and spatial changes on beach-dune-bluff morphologies in the parks. The overall goal is to create a replicable means of data gathering that is efficient, adheres to scientific principles, and meets the management needs of the coastal parks.
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