Academic literature on the topic 'Visual object'
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Journal articles on the topic "Visual object"
Tao Li, Tao Li, Yu Wang Tao Li, Zheng Zhang Yu Wang, Xuezhuan Zhao Zheng Zhang, and Lishen Pei Xuezhuan Zhao. "Visual Object Detection with Score Refinement." 網際網路技術學刊 23, no. 5 (September 2022): 1163–72. http://dx.doi.org/10.53106/160792642022092305025.
Full textSymes, Ed, Rob Ellis, and Mike Tucker. "Visual object affordances: Object orientation." Acta Psychologica 124, no. 2 (February 2007): 238–55. http://dx.doi.org/10.1016/j.actpsy.2006.03.005.
Full textNewell, F. N. "Searching for Objects in the Visual Periphery: Effects of Orientation." Perception 25, no. 1_suppl (August 1996): 110. http://dx.doi.org/10.1068/v96l1111.
Full textZelinsky, Gregory J., and Gregory L. Murphy. "Synchronizing Visual and Language Processing: An Effect of Object Name Length on Eye Movements." Psychological Science 11, no. 2 (March 2000): 125–31. http://dx.doi.org/10.1111/1467-9280.00227.
Full textLogothetis, N. K., and D. L. Sheinberg. "Visual Object Recognition." Annual Review of Neuroscience 19, no. 1 (March 1996): 577–621. http://dx.doi.org/10.1146/annurev.ne.19.030196.003045.
Full textPalmeri, Thomas J., and Isabel Gauthier. "Visual object understanding." Nature Reviews Neuroscience 5, no. 4 (April 2004): 291–303. http://dx.doi.org/10.1038/nrn1364.
Full textCrivelli, Tomas, Patrick Perez, and Lionel Oisel. "Visual object trapping." Computer Vision and Image Understanding 153 (December 2016): 3–15. http://dx.doi.org/10.1016/j.cviu.2016.07.007.
Full textGrauman, Kristen, and Bastian Leibe. "Visual Object Recognition." Synthesis Lectures on Artificial Intelligence and Machine Learning 5, no. 2 (April 19, 2011): 1–181. http://dx.doi.org/10.2200/s00332ed1v01y201103aim011.
Full textIngle, David. "Central Visual Persistences: I. Visual and Kinesthetic Interactions." Perception 34, no. 9 (September 2005): 1135–51. http://dx.doi.org/10.1068/p5408.
Full textGuo, Fei, Yuan Yang, and Yong Gao. "Optimization of Visual Information Presentation for Visual Prosthesis." International Journal of Biomedical Imaging 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/3198342.
Full textDissertations / Theses on the topic "Visual object"
Figueroa, Flores Carola. "Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671964.
Full textEl reconocimiento de objetos para los seres humanos es un proceso instantáneo, preciso y extremadamente adaptable. Además, tenemos la capacidad innata de aprender nuevas categorias de objetos a partir de unos pocos ejemplos. El cerebro humano reduce la complejidad de los datos entrantes filtrando parte de la información y procesando las cosas que captan nuestra atención. Esto, combinado con nuestra predisposición biológica a responder a determinadas formas o colores, nos permite reconocer en una simple mirada las regiones más importantes o destacadas de una imagen. Este mecanismo se puede observar analizando en qué partes de las imágenes los sujetos ponen su atención; por ejemplo donde fijan sus ojos cuando se les muestra una imagen. La forma más precisa de registrar este comportamiento es rastrear los movimientos de los ojos mientras se muestran imágenes. La estimación computacional del ‘saliency’, tiene como objetivo diseñar algoritmos que, dada una imagen de entrada, estimen mapas de ‘saliency’. Estos mapas se pueden utilizar en una variada gama de aplicaciones, incluida la detección de objetos, la compresión de imágenes y videos y el seguimiento visual. La mayoría de la investigación en este campo se ha centrado en estimar automáticamente estos mapas de ‘saliency’, dada una imagen de entrada. En cambio, en esta tesis, nos propusimos incorporar la estimación de ‘saliency’ en un procedimiento de reconocimiento de objeto, puesto que, queremos investigar si los mapas de ‘saliency’ pueden mejorar los resultados de la tarea de reconocimiento de objetos. En esta tesis, identificamos varios problemas relacionados con la estimación del ‘saliency’ visual. Primero, pudimos determinar en qué medida se puede aprovechar la estimación del ‘saliency’ para mejorar el entrenamiento de un modelo de reconocimiento de objetos cuando se cuenta con escasos datos de entrenamiento. Para resolver este problema, diseñamos una red de clasificación de imágenes que incorpora información de ‘saliency’ como entrada. Esta red procesa el mapa de ‘saliency’ a través de una rama de red dedicada y utiliza las características resultantes para modular las características visuales estándar ascendentes de la entrada de la imagen original. Nos referiremos a esta técnica como clasificación de imágenes moduladas por prominencia (SMIC en inglés). En numerosos experimentos realizando sobre en conjuntos de datos de referencia estándar para el reconocimiento de objetos ‘fine-grained’, mostramos que nuestra arquitectura propuesta puede mejorar significativamente el rendimiento, especialmente en conjuntos de datos con datos con escasos datos de entrenamiento. Luego, abordamos el principal inconveniente del problema anterior: es decir, SMIC requiere explícitamente un algoritmo de ‘saliency’, el cual debe entrenarse en un conjunto de datos de ‘saliency’. Para resolver esto, implementamos un mecanismo de alucinación que nos permite incorporar la rama de estimación de ‘saliency’ en una arquitectura de red neuronal entrenada de extremo a extremo que solo necesita la imagen RGB como entrada. Un efecto secundario de esta arquitectura es la estimación de mapas de ‘saliency’. En varios experimentos, demostramos que esta arquitectura puede obtener resultados similares en el reconocimiento de objetos como SMIC pero sin el requisito de mapas de ‘saliency’ para entrenar el sistema. Finalmente, evaluamos la precisión de los mapas de ‘saliency’ que ocurren como efecto secundario del reconocimiento de objetos. Para ello, utilizamos un de conjuntos de datos de referencia para la evaluación de la prominencia basada en experimentos de seguimiento ocular. Sorprendentemente, los mapas de ‘saliency’ estimados son muy similares a los mapas que se calculan a partir de experimentos de seguimiento ocular humano. Nuestros resultados muestran que estos mapas de ‘saliency’ pueden obtener resultados competitivos en mapas de ‘saliency’ de referencia.
For humans, the recognition of objects is an almost instantaneous, precise and extremely adaptable process. Furthermore, we have the innate capability to learn new object classes from only few examples. The human brain lowers the complexity of the incoming data by filtering out part of the information and only processing those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple glance the most important or salient regions from an image. This mechanism can be observed by analyzing on which parts of images subjects place attention; where they fix their eyes when an image is shown to them. The most accurate way to record this behavior is to track eye movements while displaying images. Computational saliency estimation aims to identify to what extent regions or objects stand out with respect to their surroundings to human observers. Saliency maps can be used in a wide range of applications including object detection, image and video compression, and visual tracking. The majority of research in the field has focused on automatically estimating saliency maps given an input image. Instead, in this thesis, we set out to incorporate saliency maps in an object recognition pipeline: we want to investigate whether saliency maps can improve object recognition results. In this thesis, we identify several problems related to visual saliency estimation. First, to what extent the estimation of saliency can be exploited to improve the training of an object recognition model when scarce training data is available. To solve this problem, we design an image classification network that incorporates saliency information as input. This network processes the saliency map through a dedicated network branch and uses the resulting characteristics to modulate the standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive experiments on standard benchmark datasets for fine-grained object recognition, we show that our proposed architecture can significantly improve performance, especially on dataset with scarce training data. Next, we address the main drawback of the above pipeline: SMIC requires an explicit saliency algorithm that must be trained on a saliency dataset. To solve this, we implement a hallucination mechanism that allows us to incorporate the saliency estimation branch in an end-to-end trained neural network architecture that only needs the RGB image as an input. A side-effect of this architecture is the estimation of saliency maps. In experiments, we show that this architecture can obtain similar results on object recognition as SMIC but without the requirement of ground truth saliency maps to train the system. Finally, we evaluated the accuracy of the saliency maps that occur as a side-effect of object recognition. For this purpose, we use a set of benchmark datasets for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human eye-tracking experiments. Our results show that these saliency maps can obtain competitive results on benchmark saliency maps. On one synthetic saliency dataset this method even obtains the state-of-the-art without the need of ever having seen an actual saliency image for training.
Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtica
Fergus, Robert. "Visual object category recognition." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425029.
Full textWallenberg, Marcus. "Embodied Visual Object Recognition." Doctoral thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132762.
Full textEmbodied Visual Object Recognition
FaceTrack
Nguyen, Duong B. T. Carleton University Dissertation Computer Science. "The visual object editing kit." Ottawa, 1993.
Find full textTauber, Zinovi. "Visual object retrieval based on locales." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0013/MQ61504.pdf.
Full textBreuel, Thomas M. "Geometric Aspects of Visual Object Recognition." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/7342.
Full textMeger, David Paul. "Visual object recognition for mobile platforms." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44682.
Full textFu, Huanzhang. "Contributions to generic visual object categorization." Phd thesis, Ecole Centrale de Lyon, 2010. http://tel.archives-ouvertes.fr/tel-00599713.
Full textChoi, Changhyun. "Visual object perception in unstructured environments." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53003.
Full textBuchler, Daniela Martins. "Visual perception of the designed object." Thesis, Staffordshire University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442502.
Full textBooks on the topic "Visual object"
Grauman, Kristen, and Bastian Leibe. Visual Object Recognition. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3.
Full textBastian, Leibe, ed. Visual object recognition. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Find full textE, Shepp Bryan, and Ballesteros Soledad, eds. Object perception: Structure and process. Hillsdale, N.J: Lawrence Erlbaum Associates, 1989.
Find full textAndrews, Mark. Visual C++ object-oriented programming. Carmel, Ind: SAMS Pub., 1993.
Find full textKhairudin, Rozainee. Object and scene visual processing. Birmingham: University of Birmingham, 1999.
Find full textObject orientation in Visual FoxPro. Reading, Mass: Addison-Wesley Developers Press, 1996.
Find full textTkach, Daniel. Visual modeling technique: Object technology using visual programmimg. Menlo Park, Calif: Addison-Wesley, 1996.
Find full textTkach, Daniel. Visual modeling technique: Object technology using visual programming. Menlo Park, Calif: Addison-Wesley, 1996.
Find full textStraley's guide to object-oriented programming with CA-Visual Objects. Reading, Mass: Addison-Wesley Pub. Co., 1996.
Find full textVisual BASIC 5 object-oriented programming. Scottsdale, AZ: Coriolis Group Books, 1997.
Find full textBook chapters on the topic "Visual object"
Humphreys, Glyn W., and Vicki Bruce. "Visual Object Recognition." In Visual Cognition, 51–101. London: Psychology Press, 2021. http://dx.doi.org/10.4324/9781315785141-3.
Full textDonnelly, Kerry. "Visual Object Agnosia." In Encyclopedia of Clinical Neuropsychology, 3630–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_817.
Full textDonnelly, Kerry. "Visual Object Agnosia." In Encyclopedia of Clinical Neuropsychology, 1. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_817-2.
Full textSchauer, Reinhard, and Siegfried Schönberger. "Visual Object Modelling." In Database and Expert Systems Applications, 300–307. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-7557-6_52.
Full textToftness, Alexander R. "Visual Object Agnosia." In Incredible Consequences of Brain Injury, 296–302. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003276937-49.
Full textAspin, Adam. "Object Visual Styles." In Pro Power BI Theme Creation, 87–101. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7068-4_5.
Full textGong, Shengrong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, and Husheng Dong. "Visual Object Recognition." In Advanced Image and Video Processing Using MATLAB, 351–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77223-3_10.
Full textGong, Shengrong, Chunping Liu, Yi Ji, Baojiang Zhong, Yonggang Li, and Husheng Dong. "Visual Object Tracking." In Advanced Image and Video Processing Using MATLAB, 391–428. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77223-3_11.
Full textDonnelly, Kerry. "Visual Object Agnosia." In Encyclopedia of Clinical Neuropsychology, 2642–43. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_817.
Full textGrauman, Kristen, and Bastian Leibe. "Generic Object Detection: Finding and Scoring Candidates." In Visual Object Recognition, 79–86. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_9.
Full textConference papers on the topic "Visual object"
Zeng, Zhen, Adrian Röfer, and Odest Chadwicke Jenkins. "Semantic Linking Maps for Active Visual Object Search (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/667.
Full textTretter, Daniel R., and Charles A. Bouman. "Multiscale stochastic approach to object detection." In Visual Communications '93, edited by Barry G. Haskell and Hsueh-Ming Hang. SPIE, 1993. http://dx.doi.org/10.1117/12.157878.
Full textBurnett, Margaret M. "Visual object-oriented programming." In Addendum to the proceedings. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/260303.261240.
Full textDenoual, Franck, and Henri Nicolas. "Artificial object trajectory modifications for 2D object-based video compositing." In Visual Communications and Image Processing 2000, edited by King N. Ngan, Thomas Sikora, and Ming-Ting Sun. SPIE, 2000. http://dx.doi.org/10.1117/12.386569.
Full textMamic, George J., and Mohammed Bennamoun. "Review of 3D object representation techniques for automatic object recognition." In Visual Communications and Image Processing 2000, edited by King N. Ngan, Thomas Sikora, and Ming-Ting Sun. SPIE, 2000. http://dx.doi.org/10.1117/12.386708.
Full textZhu, Zhuotun, Lingxi Xie, and Alan Yuille. "Object Recognition with and without Objects." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/505.
Full textChatzilari, Elisavet, Spiros Nikolopoulos, Yiannis Kompatsiaris, and Josef Kittler. "Towards modelling visual ambiguity for visual object detection." In the 14th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2637748.2638431.
Full textAhmed, P., and S. Ahmadi. "Extended Visual Object based Intelligent Visual Programming Environment." In 2008 4th International Conference on Information and Automation for Sustainability. Sustainable Development through Effective Man-machine Co-existence (ICIAFS). IEEE, 2008. http://dx.doi.org/10.1109/iciafs.2008.4783972.
Full textInoue, Seiki. "Object extraction method for image synthesis." In Visual Communications, '91, Boston, MA, edited by Kou-Hu Tzou and Toshio Koga. SPIE, 1991. http://dx.doi.org/10.1117/12.50403.
Full textGuanling, Zhou, Wang Yuping, and Dong Nanping. "Graph based visual object tracking." In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM). IEEE, 2009. http://dx.doi.org/10.1109/cccm.2009.5268142.
Full textReports on the topic "Visual object"
Oliger, Joseph, Ramani Pichumani, and Dulce Ponceleon. A Visual Object-Oriented Unification System. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada206228.
Full textBreuel, Thomas M. Geometric Aspects of Visual Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada259454.
Full textEdelman, Shimon, Heinrich H. Buelthoff, and Erik Sklar. Task and Object Learning in Visual Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada259961.
Full textMcKee, Suzanne. Visual Processing of Object Velocity and Acceleration. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada261048.
Full textDavenport, Douglas J. Object-Oriented Visual Programming Language. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada300020.
Full textMcKee, Suzanne P. Visual Processing of Object Velocity and Acceleration. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada341070.
Full textFarah, Martha J. The Functional Architecture of Visual Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada238617.
Full textJacobs, David W. The Use of Grouping in Visual Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada201691.
Full textSerre, Thomas, Lior Wolf, and Tomaso Poggio. Object Recognition with Features Inspired by Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada454604.
Full textSajda, Paul, and Leif H. Finkel. Computer Simulations of Object Discrimination by Visual Cortex,. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada253345.
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