Literatura académica sobre el tema "Face and Object Recognition"
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Artículos de revistas sobre el tema "Face and Object Recognition"
Gülbetekin, Evrim, Seda Bayraktar, Özlenen Özkan, Hilmi Uysal y Ömer Özkan. "Face Perception in Face Transplant Patients". Facial Plastic Surgery 35, n.º 05 (20 de agosto de 2019): 525–33. http://dx.doi.org/10.1055/s-0038-1666786.
Texto completoBiederman, Irving y Peter Kalocsais. "Neurocomputational bases of object and face recognition". Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, n.º 1358 (29 de agosto de 1997): 1203–19. http://dx.doi.org/10.1098/rstb.1997.0103.
Texto completoGauthier, Isabel, Marlene Behrmann y Michael J. Tarr. "Can Face Recognition Really be Dissociated from Object Recognition?" Journal of Cognitive Neuroscience 11, n.º 4 (julio de 1999): 349–70. http://dx.doi.org/10.1162/089892999563472.
Texto completoCampbell, Alison y James W. Tanaka. "Inversion Impairs Expert Budgerigar Identity Recognition: A Face-Like Effect for a Nonface Object of Expertise". Perception 47, n.º 6 (24 de abril de 2018): 647–59. http://dx.doi.org/10.1177/0301006618771806.
Texto completoMoscovitch, Morris, Gordon Winocur y 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, n.º 5 (octubre de 1997): 555–604. http://dx.doi.org/10.1162/jocn.1997.9.5.555.
Texto completoMcGugin, Rankin W., Ana E. Van Gulick y Isabel Gauthier. "Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance". Journal of Cognitive Neuroscience 28, n.º 2 (febrero de 2016): 282–94. http://dx.doi.org/10.1162/jocn_a_00891.
Texto completoDuchaine, Brad y Ken Nakayama. "Dissociations of Face and Object Recognition in Developmental Prosopagnosia". Journal of Cognitive Neuroscience 17, n.º 2 (febrero de 2005): 249–61. http://dx.doi.org/10.1162/0898929053124857.
Texto completoStevanović, Dušan. "OBJECT DETECTION USING VIOLA-JONES ALGORITHM". Knowledge International Journal 28, n.º 4 (10 de diciembre de 2018): 1349–54. http://dx.doi.org/10.35120/kij28041349d.
Texto completoYuille, Alan L. "Deformable Templates for Face Recognition". Journal of Cognitive Neuroscience 3, n.º 1 (enero de 1991): 59–70. http://dx.doi.org/10.1162/jocn.1991.3.1.59.
Texto completoJiang, Hairong, Juan P. Wachs y Bradley S. Duerstock. "Integrated vision-based system for efficient, semi-automated control of a robotic manipulator". International Journal of Intelligent Computing and Cybernetics 7, n.º 3 (5 de agosto de 2014): 253–66. http://dx.doi.org/10.1108/ijicc-09-2013-0042.
Texto completoTesis sobre el tema "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.
Texto completoTitle 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.
Texto completoBanarse, 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.
Texto completoCollin, 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.
Texto completoA 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.
Texto completoMian, 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.
Texto completoMian, 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.
Texto completoHolub, 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.
Texto completoVilaplana, 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.
Texto completoUn 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/.
Texto completoLibros sobre el tema "Face and Object Recognition"
Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.
Buscar texto completoBennamoun, M. y G. J. Mamic. Object Recognition. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1.
Texto completoGrauman, Kristen y Bastian Leibe. Visual Object Recognition. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3.
Texto completoStrat, Thomas M. Natural Object Recognition. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2932-2.
Texto completoDawson, K. M. Object recognition techniques. Dublin: Trinity College, Department of Computer Science, 1991.
Buscar texto completoNatural object recognition. New York: Springer-Verlag, 1992.
Buscar texto completoBastian, Leibe, ed. Visual object recognition. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Buscar texto completoWilkes, David. Active object recognition. Toronto: University of Toronto, 1994.
Buscar texto completoStrat, Thomas M. Natural Object Recognition. New York, NY: Springer New York, 1992.
Buscar texto completoWechsler, Harry, P. Jonathon Phillips, Vicki Bruce, Françoise Fogelman Soulié y Thomas S. Huang, eds. Face Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1.
Texto completoCapítulos de libros sobre el tema "Face and Object Recognition"
Biederman, Irving y Peter Kalocsai. "Neural and Psychophysical Analysis of Object and Face Recognition". En Face Recognition, 3–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_1.
Texto completoKalocsai, Peter y Irving Biederman. "Differences of Face and Object Recognition in Utilizing Early Visual Information". En Face Recognition, 492–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_29.
Texto completoGriffin, Jason W. y Natalie V. Motta-Mena. "Face and Object Recognition". En 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.
Texto completoGriffin, Jason W. y Natalie V. Motta-Mena. "Face and Object Recognition". En Encyclopedia of Evolutionary Psychological Science, 2876–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-19650-3_2762.
Texto completoCootes, Timothy F., David Cristinacce y Vladimir Petrović. "Statistical Models of Shape and Texture for Face Recognition". En Toward Category-Level Object Recognition, 525–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_27.
Texto completoOsadchy, Margarita, Yann Le Cun y Matthew L. Miller. "Synergistic Face Detection and Pose Estimation with Energy-Based Models". En Toward Category-Level Object Recognition, 196–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_10.
Texto completoLi, Lei y Xiaoyi Feng. "Face Anti-spoofing via Deep Local Binary Pattern". En 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.
Texto completoBashbaghi, Saman, Eric Granger, Robert Sabourin y Mostafa Parchami. "Deep Learning Architectures for Face Recognition in Video Surveillance". En 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.
Texto completoKanan, Christopher, Arturo Flores y Garrison W. Cottrell. "Color Constancy Algorithms for Object and Face Recognition". En Advances in Visual Computing, 199–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_20.
Texto completoJiang, Xiaoyue, Yaping Hou, Dong Zhang y Xiaoyi Feng. "Deep Learning in Face Recognition Across Variations in Pose and Illumination". En 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.
Texto completoActas de conferencias sobre el tema "Face and Object Recognition"
Zhang, Yuxuan, Chen Yang y Qiaodan Zhao. "Face mask recognition based on object detection". En International Conference on Signal Image Processing and Communication (ICSIPC 2021), editado por Siting Chen y Wei Qin. SPIE, 2021. http://dx.doi.org/10.1117/12.2600460.
Texto completoYamasaki, Toshihiko y Tsuhan Chen. "Face Recognition Challenge: Object Recognition Approaches for Human/Avatar Classification". En 2012 Eleventh International Conference on Machine Learning and Applications (ICMLA). IEEE, 2012. http://dx.doi.org/10.1109/icmla.2012.188.
Texto completoZhang, Lei y Guo-Fang Tu. "Scalable reduced dimension face object segmentation and tracking". En Third International Symposium on Multispectral Image Processing and Pattern Recognition, editado por Hanqing Lu y Tianxu Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.539029.
Texto completoWu, Yiming, Xiuwen Liu y Washington Mio. "Scalable optimal linear representation for face and object recognition". En Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.110.
Texto completoBurt, Peter J. "Dynamic analysis strategies for real-time object recognition". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mee2.
Texto completoFachrurrozi, Muhammad, Erwin, Saparudin y Mardiana. "Multi-object face recognition using Content Based Image Retrieval (CBIR)". En 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167132.
Texto completoSanyal, Soubhik, Devraj Mandal y Soma Biswas. "Aligned discriminative pose robust descriptors for face and object recognition". En 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296395.
Texto completoMeng Meng, Hassen Drira, Mohamed Daoudi y Jacques Boonaert. "Human-object interaction recognition by learning the distances between the object and the skeleton joints". En 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.
Texto completoTanaka, H. T. y M. Ikeda. "Curvature-based face surface recognition using spherical correlation-principal directions for curved object recognition". En Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.547024.
Texto completoAlzahrani, T. y W. Al-Nuaimy. "Face segmentation based object localisation with deep learning from unconstrained images". En 10th International Conference on Pattern Recognition Systems (ICPRS-2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.0247.
Texto completoInformes sobre el tema "Face and Object Recognition"
Wells, III y William M. Statistical Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, enero de 1993. http://dx.doi.org/10.21236/ada270887.
Texto completoSocolinsky, Diego A. y Andrea Selinger. Thermal Face Recognition Over Time. Fort Belvoir, VA: Defense Technical Information Center, enero de 2006. http://dx.doi.org/10.21236/ada444423.
Texto completoBeymer, David J. Face Recognition Under Varying Pose. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1993. http://dx.doi.org/10.21236/ada290205.
Texto completoPhillips, P. Jonathon, Patrick Grother, Ross J. Micheals, Duane M. Blackburn, Elham Tabassi y 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.
Texto completoGrother, Patrick. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7083.
Texto completoNgan, M. y 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.
Texto completoGrother, Patrick y 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.
Texto completoWeiss, Isaac. Geometric Invariants and Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1992. http://dx.doi.org/10.21236/ada255317.
Texto completoMahmood, S. T. y Tanveer F. Syeda-Tanveer. Attentional Selection in Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1993. http://dx.doi.org/10.21236/ada271004.
Texto completoBragdon, Sophia, Vuong Truong y Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), noviembre de 2022. http://dx.doi.org/10.21079/11681/45902.
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