Academic literature on the topic 'Near-infrared Image'
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Journal articles on the topic "Near-infrared Image"
Yakno, Marlina, Junita Mohamad-Saleh, Mohd Zamri Ibrahim, and W. N. A. W. Samsudin. "Camera-projector calibration for near infrared imaging system." Bulletin of Electrical Engineering and Informatics 9, no. 1 (February 1, 2020): 160–70. http://dx.doi.org/10.11591/eei.v9i1.1697.
Full textYu, Jae Taeg, Sung Woong Ra, Sungmin Lee, and Seung-Won Jung. "Image Dehazing Algorithm Using Near-infrared Image Characteristics." Journal of the Institute of Electronics and Information Engineers 52, no. 11 (November 25, 2015): 115–23. http://dx.doi.org/10.5573/ieie.2015.52.11.115.
Full textKil, Taeho, and Nam Ik Cho. "Image Fusion using RGB and Near Infrared Image." Journal of Broadcast Engineering 21, no. 4 (July 30, 2016): 515–24. http://dx.doi.org/10.5909/jbe.2016.21.4.515.
Full textKang, You Sun, and Duk Shin. "Multiband Camera System Using Color and Near Infrared Images." Applied Mechanics and Materials 446-447 (November 2013): 922–26. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.922.
Full textSon, Dong-Min, Hyuk-Ju Kwon, and Sung-Hak Lee. "Visible and Near Infrared Image Fusion Using Base Tone Compression and Detail Transform Fusion." Chemosensors 10, no. 4 (March 25, 2022): 124. http://dx.doi.org/10.3390/chemosensors10040124.
Full textZhuge, Jing Chang, Zhi Jing Yu, and Jian Shu Gao. "Ice Detection Based on Near Infrared Image Analysis." Applied Mechanics and Materials 121-126 (October 2011): 3960–64. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3960.
Full textNagata, Masaki, Toshiki Hirogaki, Eiichi Aoyama, Takahiro Iida, Yasuhiro Uenishi, Masami Matsubara, and Yoshitaka Usui. "Quality Control Method Based on Gear Tooth Contact Evaluation Using Near-Infrared Ray Imagery." Key Engineering Materials 447-448 (September 2010): 569–73. http://dx.doi.org/10.4028/www.scientific.net/kem.447-448.569.
Full textKwon, Hyuk-Ju, and Sung-Hak Lee. "Visible and Near-Infrared Image Acquisition and Fusion for Night Surveillance." Chemosensors 9, no. 4 (April 8, 2021): 75. http://dx.doi.org/10.3390/chemosensors9040075.
Full textChoi, Janghoon, Jun-Geun Shin, Yoon-Oh Tak, Youngseok Seo, and Jonghyun Eom. "Single Camera-Based Dual-Channel Near-Infrared Fluorescence Imaging system." Sensors 22, no. 24 (December 13, 2022): 9758. http://dx.doi.org/10.3390/s22249758.
Full textTang, Rongxin, Hualin Liu, and Jingbo Wei. "Visualizing Near Infrared Hyperspectral Images with Generative Adversarial Networks." Remote Sensing 12, no. 23 (November 24, 2020): 3848. http://dx.doi.org/10.3390/rs12233848.
Full textDissertations / Theses on the topic "Near-infrared Image"
Font, Aragonès Xavier. "Visible, near infrared and thermal hand-based image biometric recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/117685.
Full textEl Reconeixement Biomètric fa referència a la identi cació automàtica de persones fent us d'alguna característica o modalitat anatòmica (empremta digital) o d'alguna característica de comportament (signatura). És un aspecte fonamental en qualsevol procés relacionat amb la seguretat, la compartició de recursos o les transaccions electròniques entre d'altres. És converteix en un pas imprescindible abans de concedir l'autorització. Aquesta autorització, s'entén que protegeix recursos clau, permeten així, que aquests siguin utilitzats pels usuaris que han estat autoritzats a utilitzar-los o a tenir-hi accés. Els sistemes biomètrics poden funcionar en veri cació, on es resol la pregunta: Soc jo qui dic que soc? O en identi cació on es resol la qüestió: Qui soc jo? La comunitat cientí ca ha incrementat els seus esforços per millorar el rendiment dels sistemes biomètrics. En funció de l'aplicació, diverses solucions s'adrecen a treballar amb múltiples modalitats o combinant diferents mètodes de classi cació. Donat que incrementar el número de modalitats, representa a la vegada problemes pels usuaris, moltes d'aquestes aproximacions no arriben mai al mercat. La tesis contribueix principalment en tres grans àrees, totes elles amb el denominador comú següent: Reconeixement biometric a través de les mans. i) La primera d'elles constitueix la base de qualsevol estudi, les dades. Per poder interpretar, i establir un sistema de reconeixement biomètric prou robust amb un clar enfocament a múltiples fonts d'informació, però amb el mínim esforç per part de l'usuari es construeix aquesta Base de Dades de mans multi espectral. Les bases de dades biomètriques constitueixen un recurs molt preuat per a la recerca; sobretot si ofereixen algun element nou com es el cas. Imatges de mans en diferents espectres electromagnètics: en visible (VIS), en infraroig (NIR) i en tèrmic (TIR). Amb un total de 100 usuaris, i 10 mostres per usuari, constitueix un bon punt de partida per estudiar i posar a prova sistemes multi biomètrics enfocats a les mans. ii) El segon bloc s'adreça a les dues aproximacions existents en la literatura per a tractar les dades en brut. Aquestes dues aproximacions, anomenades Holística (tracta la imatge com un tot) i Geomètrica (utilitza càlculs geomètrics) de neixen el focus alhora d'extreure el vector de característiques. Abans de tractar alguna d'aquestes dues aproximacions, però, és necessària l'aplicació de diferents tècniques de preprocessat digital de la imatge per obtenir les regions d'interès desitjades. Diferents problemes presents a les imatges s'han hagut de solucionar de forma original per a cadascuna de les tipologies de les imatges presents: VIS, NIR i TIR. VIS: imatges sobre exposades, anells, mànigues, braçalets. NIR: Ungles pintades, distorsió en forma de soroll en les imatges TIR: Dits freds La segona àrea presenta aspectes innovadors, ja que a part de segmentar la imatge de la ma, es segmenten tots i cadascun dels dits (feature-based approach). Així aconseguim contrastar la seva capacitat de reconeixement envers la ma de forma completa. Addicionalment es presenta un conjunt de procediments geomètrics amb la idea de comparar-los amb els provinents de l'extracció holística. La tercera i última àrea contrasta el procediment de classi cació anomenat Biometric Dispersion Matcher (BDM) amb diferents situacions. La primera relacionada amb l'efectivitat respecte d'altres mètode de reconeixement, com ara l'Anàlisi Lineal Discriminant (LDA) o bé mètodes com KNN o la regressió logística. Les altres situacions que s'analitzen tenen a veure amb múltiples fonts d'informació, quan s'apliquen tècniques de normalització i/o estratègies de combinació (fusió) per millorar els resultats. Els resultats obtinguts no deixen lloc per a la confusió, i són certament prometedors en el sentit que posen a la llum la importància de combinar informació complementària per obtenir rendiments superiors.
Karlsson, Jonas. "FPGA-Accelerated Dehazing by Visible and Near-infrared Image Fusion." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28322.
Full textClarke, Fiona Catherine. "Near-infrared microscopy and image analysis for pharmaceutical process control." Thesis, University College London (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432883.
Full textFlowerdew, Roland John. "Atmospheric correction for the visible and near-infrared channels of ATSR-2." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283392.
Full textTouse, Michael P. "Demonstration of a near and mid-infrared detector using multiple step quantum wells." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FTouse.pdf.
Full textCarson, Kathryn Jane. "Contributions towards image reconstruction for functional imaging using time-resolved near-infrared measurements." Thesis, Keele University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240034.
Full textWong, Gerald. "Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2615.
Full textTeresi, Michael Bryan. "Multispectral Image Labeling for Unmanned Ground Vehicle Environments." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/53998.
Full textMaster of Science
Khalaf, Reem. "Image reconstruction for optical tomography using photon density waves." Thesis, University of Hertfordshire, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302304.
Full textEdjlali, Ehsan. "Fluorescence diffuse optical tomographic iterative image reconstruction for small animal molecular imaging with continuous-wave near infrared light." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10673.
Full textAbstract : The simplified spherical harmonics (SPN) approximation to the radiative transfer equation has been proposed as a reliable model of light propagation in biological tissues. However, few analytical solutions have been found for this model. Such analytical solutions are of great value to validate numerical solutions of the SPN equations, which must be resorted to when dealing with media with complex curved geometries. In the first part of this thesis, analytical solutions for two curved geometries are presented for the first time, namely for the sphere and for the cylinder. For both solutions, the general refractiveindex mismatch boundary conditions, as applicable in biomedical optics, are resorted to. These solutions are validated using mesh-based Monte Carlo simulations. So validated, these solutions allow in turn to rapidly validate numerical code, based for example on finite differences or on finite elements, without requiring lengthy Monte Carlo simulations. provide reliable tool for validating numerical simulations. In the second part, iterative reconstruction for fluorescence diffuse optical tomography imaging is proposed based on an Lq-Lp framework for formulating an objective function and its regularization term. To solve the imaging inverse problem, the discretization of the light propagation model is performed using the finite difference method. The framework is used along with a multigrid mesh on a digital mouse model. The inverse problem is solved iteratively using an optimization method. For this, the gradient of the cost function with respect to the fluorescent agent’s concentration map is necessary. This is calculated using an adjoint method. Quantitative metrics resorted to in medical imaging are used to evaluate the performance of the framework under different conditions. The results obtained support this new approach based on an Lq-Lp formulation of cost functions in order to solve the inverse fluorescence problem with high quantified performance.
Books on the topic "Near-infrared Image"
(Society), SPIE, Optical Society of America, and European Optical Society, eds. Diffuse optical imaging II: 14-17 June 2009, Munich, Germany. Bellingham, Wash: SPIE, 2009.
Find full textHielscher, Andreas H. Diffuse optical imaging III: 22-24 May 2011, Munich, Germany. Edited by SPIE (Society), Optical Society of America, Deutsche Gesellschaft für Lasermedizin, German Biophotonics Research Program, Photonics4Life (Group), and United States. Air Force. Office of Scientific Research. Bellingham, Wash: SPIE, 2011.
Find full textThe search for extended infrared emission near interacting and active galaxies. [Washington, D.C.?: National Aeronautics and Space Administration, 1991.
Find full textK, Matthews, and United States. National Aeronautics and Space Administration., eds. The first diffraction-limited images from the W.M. Keck Telescope. [Washington, D.C: National Aeronautics and Space Administration, 1996.
Find full textBook chapters on the topic "Near-infrared Image"
Taini, Matti, Guoying Zhao, and Matti Pietikäinen. "Weight-Based Facial Expression Recognition from Near-Infrared Video Sequences." In Image Analysis, 239–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_25.
Full textKang, Jinwoo, David V. Anderson, and Monson H. Hayes. "Direct Image Alignment for Active Near Infrared Image Differencing." In Advanced Concepts for Intelligent Vision Systems, 334–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25903-1_29.
Full textLarsen, Rasmus, Morten Arngren, Per Waaben Hansen, and Allan Aasbjerg Nielsen. "Kernel Based Subspace Projection of Near Infrared Hyperspectral Images of Maize Kernels." In Image Analysis, 560–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_57.
Full textDrury, S. A. "Digital processing of images in the visible and near infrared." In Image Interpretation in Geology, 118–48. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-010-9393-4_5.
Full textGao, Renwu, Siting Zheng, Jia He, and Linlin Shen. "CycleGAN-Based Image Translation for Near-Infrared Camera-Trap Image Recognition." In Pattern Recognition and Artificial Intelligence, 453–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_39.
Full textMatsui, Sosuke, Takahiro Okabe, Mihoko Shimano, and Yoichi Sato. "Image Enhancement of Low-Light Scenes with Near-Infrared Flash Images." In Computer Vision – ACCV 2009, 213–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12307-8_20.
Full textGatley, Ian, R. Joyce, A. Fowler, D. DePoy, and R. Probst. "A Large Near-Infrared Image of the Galactic Center." In The Center of the Galaxy, 361–64. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2362-1_47.
Full textSalamati, Neda, Diane Larlus, Gabriela Csurka, and Sabine Süsstrunk. "Semantic Image Segmentation Using Visible and Near-Infrared Channels." In Computer Vision – ECCV 2012. Workshops and Demonstrations, 461–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33868-7_46.
Full textMao, Kai, Meng Yang, and Haijian Wang. "Infrared and Near-Infrared Image Generation via Content Consistency and Style Adversarial Learning." In Pattern Recognition and Computer Vision, 618–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_48.
Full textXu, Zizhou, and Yueli Hu. "Image Enhancement Based on the Fusion of Visible and Near-Infrared Images." In Advances in Intelligent Automation and Soft Computing, 814–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_93.
Full textConference papers on the topic "Near-infrared Image"
Fertala, Remi A., and Georges Couderc. "Near-ultraviolet/near-infrared image mixing." In Aerospace Sensing, edited by Sankaran Gowrinathan and James F. Shanley. SPIE, 1992. http://dx.doi.org/10.1117/12.138082.
Full textSuarez, Patricia L., Angel D. Sappa, Boris X. Vintimilla, and Riad I. Hammoud. "Near InfraRed Imagery Colorization." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451413.
Full textHuang, Dingliang, Bin Hu, Yinna Chen, Yu Chen, Liangchen Sui, Zhaoyang Wang, Yijun Jiang, et al. "Autonomous Robotic Subcutaneous Injection Under Near-Infrared Image Guidance." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-69087.
Full textKim, Hyeongyu, Jonghyun Kim, and Joongkyu Kim. "Image-to-Image Translation for Near-Infrared Image Colorization." In 2022 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2022. http://dx.doi.org/10.1109/iceic54506.2022.9748773.
Full textFeng, Chen, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, and Sabine Susstrunk. "Near-infrared guided color image dehazing." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738487.
Full textLindmayer, Joseph, and David McGuire. "Extended-range near-infrared image intensifier." In SC - DL tentative, edited by Illes P. Csorba. SPIE, 1990. http://dx.doi.org/10.1117/12.19470.
Full textHong, Yuchen, Youwei Lyu, Si Li, and Boxin Shi. "Near-Infrared Image Guided Reflection Removal." In 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020. http://dx.doi.org/10.1109/icme46284.2020.9102937.
Full textKudo, Yuhei, and Akira Kubota. "Image dehazing method by fusing weighted near-infrared image." In 2018 International Workshop on Advanced Image Technology (IWAIT). IEEE, 2018. http://dx.doi.org/10.1109/iwait.2018.8369744.
Full textWu, Fangyu, Weihang You, Jeremy S. Smith, Wenjin Lu, and Bailing Zhang. "Image-Image Translation to Enhance Near Infrared Face Recognition." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8804414.
Full textSimanovski, Dmitrii M., Daniel V. Palanker, Philip Huie, and Todd I. Smith. "Image formation in near-field infrared microscopy." In BiOS 2000 The International Symposium on Biomedical Optics, edited by Shuming Nie, Eiichi Tamiya, and Edward S. Yeung. SPIE, 2000. http://dx.doi.org/10.1117/12.383347.
Full textReports on the topic "Near-infrared Image"
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.
Full textBecker, Sarah, Craig Daughtry, and Andrew Russ. Robust forest cover indices for multispectral images. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42760.
Full textCohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.
Full textEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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