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Статті в журналах з теми "Face and Object Recognition"
Gülbetekin, Evrim, Seda Bayraktar, Özlenen Özkan, Hilmi Uysal, and Ömer Özkan. "Face Perception in Face Transplant Patients." Facial Plastic Surgery 35, no. 05 (August 20, 2019): 525–33. http://dx.doi.org/10.1055/s-0038-1666786.
Повний текст джерелаBiederman, Irving, and Peter Kalocsais. "Neurocomputational bases of object and face recognition." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1358 (August 29, 1997): 1203–19. http://dx.doi.org/10.1098/rstb.1997.0103.
Повний текст джерелаGauthier, Isabel, Marlene Behrmann, and Michael J. Tarr. "Can Face Recognition Really be Dissociated from Object Recognition?" Journal of Cognitive Neuroscience 11, no. 4 (July 1999): 349–70. http://dx.doi.org/10.1162/089892999563472.
Повний текст джерелаCampbell, Alison, and James W. Tanaka. "Inversion Impairs Expert Budgerigar Identity Recognition: A Face-Like Effect for a Nonface Object of Expertise." Perception 47, no. 6 (April 24, 2018): 647–59. http://dx.doi.org/10.1177/0301006618771806.
Повний текст джерелаMoscovitch, Morris, Gordon Winocur, and Marlene Behrmann. "What Is Special about Face Recognition? Nineteen Experiments on a Person with Visual Object Agnosia and Dyslexia but Normal Face Recognition." Journal of Cognitive Neuroscience 9, no. 5 (October 1997): 555–604. http://dx.doi.org/10.1162/jocn.1997.9.5.555.
Повний текст джерелаMcGugin, Rankin W., Ana E. Van Gulick, and Isabel Gauthier. "Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance." Journal of Cognitive Neuroscience 28, no. 2 (February 2016): 282–94. http://dx.doi.org/10.1162/jocn_a_00891.
Повний текст джерелаDuchaine, Brad, and Ken Nakayama. "Dissociations of Face and Object Recognition in Developmental Prosopagnosia." Journal of Cognitive Neuroscience 17, no. 2 (February 2005): 249–61. http://dx.doi.org/10.1162/0898929053124857.
Повний текст джерелаStevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM." Knowledge International Journal 28, no. 4 (December 10, 2018): 1349–54. http://dx.doi.org/10.35120/kij28041349d.
Повний текст джерелаYuille, Alan L. "Deformable Templates for Face Recognition." Journal of Cognitive Neuroscience 3, no. 1 (January 1991): 59–70. http://dx.doi.org/10.1162/jocn.1991.3.1.59.
Повний текст джерелаJiang, Hairong, Juan P. Wachs, and Bradley S. Duerstock. "Integrated vision-based system for efficient, semi-automated control of a robotic manipulator." International Journal of Intelligent Computing and Cybernetics 7, no. 3 (August 5, 2014): 253–66. http://dx.doi.org/10.1108/ijicc-09-2013-0042.
Повний текст джерелаДисертації з теми "Face and Object Recognition"
Gathers, Ann D. "DEVELOPMENTAL FMRI STUDY: FACE AND OBJECT RECOGNITION." Lexington, Ky. : [University of Kentucky Libraries], 2005. http://lib.uky.edu/ETD/ukyanne2005d00276/etd.pdf.
Повний текст джерелаTitle from document title page (viewed on November 4, 2005). Document formatted into pages; contains xi, 152 p. : ill. Includes abstract and vita. Includes bibliographical references (p. 134-148).
Nilsson, Linus. "Object Tracking and Face Recognition in Video Streams." Thesis, Umeå universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-58076.
Повний текст джерелаBanarse, D. S. "A generic neural network architecture for deformation invariant object recognition." Thesis, Bangor University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362146.
Повний текст джерелаCollin, Charles Alain. "Effects of spatial frequency overlap on face and object recognition." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36896.
Повний текст джерелаA second question that is examined concerns the effect of calibration of stimuli on recognition of spatially filtered images. Past studies using non-calibrated presentation methods have inadvertently introduced aberrant frequency content to their stimuli. The effect this has on recognition performance has not been examined, leading to doubts about the comparability of older and newer studies. Examining the impact of calibration on recognition is an ancillary goal of this dissertation.
Seven experiments examining the above questions are reported here. Results suggest that spatial frequency overlap had a strong effect on face recognition and a lesser effect on object recognition. Indeed, contrary to much previous research it was found that the band of frequencies occupied by a face image had little effect on recognition, but that small variations in overlap had significant effects. This suggests that the overlap factor is important in understanding various phenomena in visual recognition. Overlap effects likely contribute to the apparent superiority of certain spatial bands for different recognition tasks, and to the inferiority of line drawings in face recognition. Results concerning the mnemonic representation of faces and objects suggest that these are both encoded in a format that retains spatial frequency information, and do not support certain proposed fundamental differences in how these two stimulus classes are stored. Data on calibration generally shows non-calibration having little impact on visual recognition, suggesting moderate confidence in results of older studies.
Higgs, David Robert. "Parts-based object detection using multiple views /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1000.
Повний текст джерелаMian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0046.
Повний текст джерелаMian, Ajmal Saeed. "Representations and matching techniques for 3D free-form object and face recognition /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0046.
Повний текст джерелаHolub, Alex David Perona Pietro. "Discriminative vs. generative object recognition : objects, faces, and the web /." Diss., Pasadena, Calif. : California Institute of Technology, 2007. http://resolver.caltech.edu/CaltechETD:etd-05312007-204007.
Повний текст джерелаVilaplana, Besler Verónica. "Region-based face detection, segmentation and tracking. framework definition and application to other objects." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/33330.
Повний текст джерелаUn dels problemes més importants en l'àrea de visió artificial és el reconeixement automàtic de classes d'objectes. En particular, la detecció de la classe de cares humanes és un problema que genera especial interès degut al gran nombre d'aplicacions que requereixen com a primer pas detectar les cares a l'escena. A aquesta tesis s'analitza el problema de detecció de cares com un problema conjunt de detecció i segmentació, per tal de localitzar de manera precisa les cares a l'escena amb màscares que arribin a precisions d'un píxel. Malgrat l'objectiu principal de la tesi és aquest, en el procés de trobar una solució s'ha intentat crear un marc de treball general i tan independent com fos possible del tipus d'objecte que s'està buscant. Amb aquest propòsit, la tècnica proposada fa ús d'un model jeràrquic d'imatge basat en regions, l'arbre binari de particions (BPT: Binary Partition Tree), en el qual els objectes s'obtenen com a unió de regions que provenen d'una partició de la imatge. En aquest treball, s'ha optimitzat el model per a les tasques de detecció i segmentació de cares. Per això, es proposen diferents criteris de fusió i de parada, els quals es comparen en un conjunt ampli d'experiments. En el sistema proposat, la variabilitat dins de la classe cara s'estudia dins d'un marc de treball d'aprenentatge automàtic. La classe cara es caracteritza fent servir un conjunt de descriptors, que es mesuren en els nodes de l'arbre, així com un conjunt de classificadors d'una única classe. El sistema està format per dos classificadors forts. Primer s'utilitza una cascada de classificadors binaris que realitzen una simplificació de l'espai de cerca i, posteriorment, s'aplica un conjunt de classificadors més complexes que produeixen la classificació final dels nodes de l'arbre. El sistema es testeja de manera exhaustiva sobre diferents bases de dades de cares, sobre les quals s'obtenen segmentacions precises provant així la robustesa del sistema en front a variacions d'escala, posició, orientació, condicions d'il·luminació i complexitat del fons de l'escena. A aquesta tesi es mostra també que la tècnica proposada per cares pot ser fàcilment adaptable a la detecció i segmentació d'altres classes d'objectes. Donat que la construcció del model d'imatge no depèn de la classe d'objecte que es pretén buscar, es pot detectar i segmentar diferents classes d'objectes fent servir, sobre el mateix model d'imatge, el model d'objecte apropiat. Nous models d'objecte poden ser fàcilment construïts mitjançant la selecció i l'entrenament d'un conjunt adient de descriptors i classificadors. Finalment, es proposa un mecanisme de seguiment. Aquest mecanisme combina l'eficiència de l'algorisme mean-shift amb l'ús de regions per fer el seguiment i segmentar les cares al llarg d'una seqüència de vídeo a la qual tant la càmera com la cara es poden moure. Aquest mètode s'estén al cas de seguiment d'altres objectes deformables, utilitzant una versió basada en regions de la tècnica de graph-cut per obtenir la segmentació final de l'objecte a cada imatge. Els experiments realitzats mostren que les dues versions del sistema de seguiment basat en l'algorisme mean-shift produeixen segmentacions acurades, fins i tot en entorns complicats com ara quan l'objecte i el fons de l'escena presenten colors similars o quan es produeix un moviment ràpid, ja sigui de la càmera o de l'objecte.
Gunn, Steve R. "Dual active contour models for image feature extraction." Thesis, University of Southampton, 1996. https://eprints.soton.ac.uk/250089/.
Повний текст джерелаКниги з теми "Face and Object Recognition"
Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.
Знайти повний текст джерелаBennamoun, M., and G. J. Mamic. Object Recognition. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1.
Повний текст джерелаGrauman, Kristen, and Bastian Leibe. Visual Object Recognition. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3.
Повний текст джерелаStrat, Thomas M. Natural Object Recognition. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2932-2.
Повний текст джерелаDawson, K. M. Object recognition techniques. Dublin: Trinity College, Department of Computer Science, 1991.
Знайти повний текст джерелаNatural object recognition. New York: Springer-Verlag, 1992.
Знайти повний текст джерелаBastian, Leibe, ed. Visual object recognition. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Знайти повний текст джерелаWilkes, David. Active object recognition. Toronto: University of Toronto, 1994.
Знайти повний текст джерелаStrat, Thomas M. Natural Object Recognition. New York, NY: Springer New York, 1992.
Знайти повний текст джерелаWechsler, Harry, P. Jonathon Phillips, Vicki Bruce, Françoise Fogelman Soulié, and Thomas S. Huang, eds. Face Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1.
Повний текст джерелаЧастини книг з теми "Face and Object Recognition"
Biederman, Irving, and Peter Kalocsai. "Neural and Psychophysical Analysis of Object and Face Recognition." In Face Recognition, 3–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_1.
Повний текст джерелаKalocsai, Peter, and Irving Biederman. "Differences of Face and Object Recognition in Utilizing Early Visual Information." In Face Recognition, 492–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_29.
Повний текст джерелаGriffin, Jason W., and Natalie V. Motta-Mena. "Face and Object Recognition." In Encyclopedia of Evolutionary Psychological Science, 1–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-16999-6_2762-1.
Повний текст джерелаGriffin, Jason W., and Natalie V. Motta-Mena. "Face and Object Recognition." In Encyclopedia of Evolutionary Psychological Science, 2876–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-19650-3_2762.
Повний текст джерелаCootes, Timothy F., David Cristinacce, and Vladimir Petrović. "Statistical Models of Shape and Texture for Face Recognition." In Toward Category-Level Object Recognition, 525–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_27.
Повний текст джерелаOsadchy, Margarita, Yann Le Cun, and Matthew L. Miller. "Synergistic Face Detection and Pose Estimation with Energy-Based Models." In Toward Category-Level Object Recognition, 196–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_10.
Повний текст джерелаLi, Lei, and Xiaoyi Feng. "Face Anti-spoofing via Deep Local Binary Pattern." In Deep Learning in Object Detection and Recognition, 91–111. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4_4.
Повний текст джерелаBashbaghi, Saman, Eric Granger, Robert Sabourin, and Mostafa Parchami. "Deep Learning Architectures for Face Recognition in Video Surveillance." In Deep Learning in Object Detection and Recognition, 133–54. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4_6.
Повний текст джерелаKanan, Christopher, Arturo Flores, and Garrison W. Cottrell. "Color Constancy Algorithms for Object and Face Recognition." In Advances in Visual Computing, 199–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_20.
Повний текст джерелаJiang, Xiaoyue, Yaping Hou, Dong Zhang, and Xiaoyi Feng. "Deep Learning in Face Recognition Across Variations in Pose and Illumination." In Deep Learning in Object Detection and Recognition, 59–90. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4_3.
Повний текст джерелаТези доповідей конференцій з теми "Face and Object Recognition"
Zhang, Yuxuan, Chen Yang, and Qiaodan Zhao. "Face mask recognition based on object detection." In International Conference on Signal Image Processing and Communication (ICSIPC 2021), edited by Siting Chen and Wei Qin. SPIE, 2021. http://dx.doi.org/10.1117/12.2600460.
Повний текст джерелаYamasaki, Toshihiko, and Tsuhan Chen. "Face Recognition Challenge: Object Recognition Approaches for Human/Avatar Classification." In 2012 Eleventh International Conference on Machine Learning and Applications (ICMLA). IEEE, 2012. http://dx.doi.org/10.1109/icmla.2012.188.
Повний текст джерелаZhang, Lei, and Guo-Fang Tu. "Scalable reduced dimension face object segmentation and tracking." In Third International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Hanqing Lu and Tianxu Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.539029.
Повний текст джерелаWu, Yiming, Xiuwen Liu, and Washington Mio. "Scalable optimal linear representation for face and object recognition." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.110.
Повний текст джерелаBurt, Peter J. "Dynamic analysis strategies for real-time object recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mee2.
Повний текст джерелаFachrurrozi, Muhammad, Erwin, Saparudin, and Mardiana. "Multi-object face recognition using Content Based Image Retrieval (CBIR)." In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167132.
Повний текст джерелаSanyal, Soubhik, Devraj Mandal, and Soma Biswas. "Aligned discriminative pose robust descriptors for face and object recognition." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296395.
Повний текст джерелаMeng Meng, Hassen Drira, Mohamed Daoudi, and Jacques Boonaert. "Human-object interaction recognition by learning the distances between the object and the skeleton joints." In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 2015. http://dx.doi.org/10.1109/fg.2015.7284883.
Повний текст джерелаTanaka, H. T., and M. Ikeda. "Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.547024.
Повний текст джерелаAlzahrani, T., and W. Al-Nuaimy. "Face segmentation based object localisation with deep learning from unconstrained images." In 10th International Conference on Pattern Recognition Systems (ICPRS-2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.0247.
Повний текст джерелаЗвіти організацій з теми "Face and Object Recognition"
Wells, III, and William M. Statistical Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada270887.
Повний текст джерелаSocolinsky, Diego A., and Andrea Selinger. Thermal Face Recognition Over Time. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444423.
Повний текст джерелаBeymer, David J. Face Recognition Under Varying Pose. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada290205.
Повний текст джерелаPhillips, P. Jonathon, Patrick Grother, Ross J. Micheals, Duane M. Blackburn, Elham Tabassi, and Mike Bone. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.6965.
Повний текст джерелаGrother, Patrick. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7083.
Повний текст джерелаNgan, M., and P. Grother. Face Recognition Vendor Test (FRVT) :. Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.7995.
Повний текст джерелаGrother, Patrick, and Mei Ngan. Face Recognition Vendor Test (FRVT). Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.8009.
Повний текст джерелаWeiss, Isaac. Geometric Invariants and Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada255317.
Повний текст джерелаMahmood, S. T., and Tanveer F. Syeda-Tanveer. Attentional Selection in Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada271004.
Повний текст джерелаBragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45902.
Повний текст джерела