Relevant bibliographies by topics / Body recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Body recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Body recognition"

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Rasberry, C. N., T. M. Adams, P. A. Finnicum, and M. B. Dean. "BODY IMAGE RECOGNITION." Medicine & Science in Sports & Exercise 34, no. 5 (2002): S172. http://dx.doi.org/10.1097/00005768-200205001-00957.

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Knoblich, Günther. "Self-recognition: body and action." Trends in Cognitive Sciences 6, no. 11 (2002): 447–49. http://dx.doi.org/10.1016/s1364-6613(02)01995-2.

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Nakajima, Chikahito, Massimiliano Pontil, Bernd Heisele, and Tomaso Poggio. "Full-body person recognition system." Pattern Recognition 36, no. 9 (2003): 1997–2006. http://dx.doi.org/10.1016/s0031-3203(03)00061-x.

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Ahmed, Ferdous, A. S. M. Hossain Bari, and Marina L. Gavrilova. "Emotion Recognition From Body Movement." IEEE Access 8 (2020): 11761–81. http://dx.doi.org/10.1109/access.2019.2963113.

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Reed, Catherine L., Valerie E. Stone, Senia Bozova, and James Tanaka. "The Body-Inversion Effect." Psychological Science 14, no. 4 (2003): 302–8. http://dx.doi.org/10.1111/1467-9280.14431.

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Researchers argue that faces are recognized via the configuration of their parts. An important behavioral finding supporting this claim is the face-inversion effect, in which inversion impairs recognition of faces more than nonface objects. Until recently, faces were the only class of objects producing the inversion effect for untrained individuals. This study investigated whether the inversion effect extends to human body positions, a class of objects whose exemplars are structurally similar to each other. Three experiments compared the recognition of upright and inverted faces, houses, and b
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Zhang, Jian, and Wan Juan Song. "Body Recognition Based on Depth Image." Applied Mechanics and Materials 631-632 (September 2014): 414–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.414.

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The text introduces the research status of depth image in the pattern recognition and the application in the body recognition. Aiming at the problem that the image recognition shot by common camera has declined performance under the factors of illumination, posture, shielding, and the like, the body parts are distinguished and judged by taking Kinect equipment promoted by Microsoft as the platform, analyzing the features of the depth picture obtained by the Kinect camera and putting forwards to the local gradient features of comprehensive point features and the gradient features; and the elbow
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Pilz, Karin S., and Ian M. Thornton. "Idiosyncratic body motion influences person recognition." Visual Cognition 25, no. 4-6 (2016): 539–49. http://dx.doi.org/10.1080/13506285.2016.1232327.

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Bharti, Jyoti, and M. K. Gupta. "Gait Recognition using Shoulder Body Joint." International Journal of Computer Applications 53, no. 4 (2012): 49–52. http://dx.doi.org/10.5120/8413-2053.

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ZHANG, Keye, Mingming ZHANG, Tiantian LIU, Wenbo LUO, and Weiqi HE. "The inversion effect of body recognition." Advances in Psychological Science 27, no. 1 (2019): 27. http://dx.doi.org/10.3724/sp.j.1042.2019.00027.

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WANG, SHOUJIA, WENHUI LI, BO FU, HONGYIN NI, and CONG WANG. "A ROBUST MOVING BODY RECOGNITION METHOD." Journal of Advanced Manufacturing Systems 11, no. 02 (2012): 107–14. http://dx.doi.org/10.1142/s0219686712500096.

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At present, moving body recognition is one of the most active areas of research in the field of computer vision and is used widely in all kinds of videos. But the recognition accuracy of these methods has changed negatively because of the complexity of the background. In this paper, we put forward a robust recognition method. First, we obtain the moving body by tripling the temporal difference method. And then we eliminate noise from these images by mathematical morphology. Finally, we use three-scanning notation method to mark and connect the connected domain. This new method is more accurate
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Dissertations / Theses on the topic "Body recognition"

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Puranam, Muthukumar B. "Towards Full-Body Gesture Analysis and Recognition." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/227.

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With computers being embedded in every walk of our life, there is an increasing demand forintuitive devices for human-computer interaction. As human beings use gestures as importantmeans of communication, devices based on gesture recognition systems will be effective for humaninteraction with computers. However, it is very important to keep such a system as non-intrusive aspossible, to reduce the limitations of interactions. Designing such non-intrusive, intuitive, camerabasedreal-time gesture recognition system has been an active area of research research in the fieldof computer vision.Gestur
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Oberst, Leah. "Facial and Body Emotion Recognition in Infancy." UKnowledge, 2014. http://uknowledge.uky.edu/psychology_etds/48.

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Adults are experts at assessing emotions, an ability essential for appropriate social interaction. The present study, investigated this ability’s development, examining infants’ matching of facial and body emotional information. In Experiment 1, 18 6.5-month-olds were familiarized to angry or happy bodies or faces. Those familiarized to bodies were tested with familiar and novel emotional faces. Those habituated to faces were tested with bodies. The 6.5-month-old infants exhibited a preference for the familiar emotion, matching between faces and bodies. In Experiment 2, 18 6.5-month-olds were
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Hock, Alyson J. "THE WHOLE PICTURE: BODY POSTURE RECOGNITION IN INFANCY." UKnowledge, 2013. http://uknowledge.uky.edu/psychology_etds/35.

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Holistic image processing is tied to expertise and is characteristic of face and body processing by adults. Infants process faces holistically, but it is unknown whether infants process body information holistically. In the present study, we examined whether infants discriminate changes in body posture holistically. Body posture is an important nonverbal cue that signals emotion, intention, and goals of others even from a distance. In the current study, infants were tested for discrimination between body postures that differ in limb orientations in three conditions: in the context of the whole
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Song, Yale. "Multi-signal gesture recognition using body and hand poses." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62429.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 147-154).<br>We present a vision-based multi-signal gesture recognition system that integrates information from body and hand poses. Unlike previous approaches to gesture recognition, which concentrated mainly on making it a signal signal, our system allows a richer gesture vocabulary and more natural human-computer interaction. The system consists of three parts: 3D body pose estimation, hand pose
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Mathi, Krishna Chaithanya. "Augment HoloLens’ Body Recognition and Tracking Capabilities Using Kinect." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1484670493776915.

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Hammerla, Nils Yannick. "Activity recognition in naturalistic environments using body-worn sensors." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/2777.

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The research presented in this thesis investigates how deep learning and feature learning can address challenges that arise for activity recognition systems in naturalistic, ecologically valid surroundings such as the private home. One of the main aims of ubiquitous computing is the development of automated recognition systems for human activities and behaviour that are sufficiently robust to be deployed in realistic, in-the-wild environments. In most cases, the targeted application scenarios are people’s daily lives, where systems have to abide by practical usability and privacy constraints.
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Junker, Holger. "Human activity recognition and gesture spotting with body-worn sensors /." Konstanz : Hartung-Gorre, 2005. http://www.loc.gov/catdir/toc/fy0608/2006356170.html.

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Renna, I. "Upper body tracking and Gesture recognition for Human-Machine Interaction." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00717443.

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Les robots sont des agents artificiels qui peuvent agir dans le monde des humains grâce aux capacités de perception. Dans un contexte d'interaction homme-robot, les humains et les robots partagent le même espace de communication. En effet, les robots compagnons sont censés communiquer avec les humains d'une manière naturelle et intuitive: l'une des façons les plus naturelles est basée sur les gestes et les mouvements réactifs du corps. Pour rendre cette interaction la plus conviviale possible, un robot compagnon doit, donc, être doté d'une ou plusieurs capacités lui permettant de percevoir, de
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Renna, Ilaria. "Upper body tracking and Gesture recognition for Human-Machine Interaction." Paris 6, 2012. http://www.theses.fr/2012PA066119.

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Les robots sont des agents artificiels qui peuvent agir dans le monde des humains grâce aux capacités de perception. Dans un contexte d’interaction homme-robot, les humains et les robots partagent le même espace de communication. En effet, les robots compagnons sont censés communiquer avec les humains d’une manière naturelle et intuitive: l’une des façons les plus naturelles est basée sur les gestes et les mouvements réactifs du corps. Pour rendre cette interaction la plus conviviale possible, un robot compagnon doit, donc, être doté d’une ou plusieurs capacités lui permettant de percevoir, de
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Chu, Cheng-Tse. "Robust Upper Body Pose Recognition in Unconstrained Environments Using Haar-Disparity." Thesis, University of Canterbury. Computer Science and Software Engineering, 2008. http://hdl.handle.net/10092/2165.

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In this research, an approach is proposed for the robust tracking of upper body movement in unconstrained environments by using a Haar- Disparity algorithm together with a novel 2D silhouette projection algorithm. A cascade of boosted Haar classifiers is used to identify human faces in video images, where a disparity map is then used to establish the 3D locations of detected faces. Based on this information, anthropometric constraints are used to define a semi-spherical interaction space for upper body poses. This constrained region serves the purpose of pruning the search space as well as val
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Books on the topic "Body recognition"

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Human activity recognition and gesture spotting with body-worn sensors. Hartung-Gorre Verlag, 2005.

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The nature of the self: Recognition in the form of right and morality. W. de Gruyter, 2009.

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Gender, heterosexuality, and youth violence: The struggle for recognition. Rowman & Littlefield Publishers, 2012.

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New directions in philosophy and cognitive science: Adaptation and cephalic expression. Palgrave Macmillan, 2012.

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Fox-Body Mustang Recognition Guide 1979-1993. Pete's Publishing Co Inc, 2003.

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(Foreword), M. Yacoub, and Guang-Zhong Yang (Editor), eds. Body Sensor Networks. Springer, 2006.

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Guang-Zhong, Yang, ed. Body sensor networks. Springer, 2006.

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Body Sensor Networks. Springer, 2014.

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Yang, Guang-Zhong. Body Sensor Networks. Springer, 2016.

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Whole Body Interaction HumanComputer Interaction. Springer, 2011.

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Book chapters on the topic "Body recognition"

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Yin, Dong, Yu-Qing Miao, Kang Qiu, and An Wang. "Study on Human Body Action Recognition." In Biometric Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_10.

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Mourey, Jannatul, Ava Sehat Niaki, Priyanka Kaplish, and Rupali Gupta. "Human Body Fall Recognition System." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54407-2_31.

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Xiao, Yang, Hui Liang, Junsong Yuan, and Daniel Thalmann. "Body Movement Analysis and Recognition." In Human–Computer Interaction Series. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19947-4_2.

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Langman, Lauren. "The Recognition of No-Body." In Political Philosophy and Public Purpose. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91980-5_4.

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Emering, Luc, and Bruno Herbelin. "Body Gesture Recognition and Action Response." In Handbook of Virtual Humans. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470023198.ch12.

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Vera-Rodriguez, Ruben, Ester Gonzalez-Sosa, Javier Hernandez-Ortega, and Julian Fierrez. "Body Shape-Based Biometric Person Recognition from mmW Images." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_50.

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Perez, Mauricio, Jun Liu, and Alex C. Kot. "Interaction Recognition Through Body Parts Relation Reasoning." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41404-7_19.

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Stathopoulou, Ioanna-Ourania, and George A. Tsihrintzis. "Emotion Recognition from Body Movements and Gestures." In Intelligent Interactive Multimedia Systems and Services. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22158-3_29.

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Liu, Junxiu, Mingxing Li, Yuling Luo, Su Yang, and Senhui Qiu. "Human Body Posture Recognition Using Wearable Devices." In Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30493-5_33.

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Santhoshkumar, R., and M. Kalaiselvi Geetha. "Human Emotion Recognition Using Body Expressive Feature." In Microservices in Big Data Analytics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0128-9_13.

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Conference papers on the topic "Body recognition"

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Cao, Liangliang, Mert Dikmen, Yun Fu, and Thomas S. Huang. "Gender recognition from body." In Proceeding of the 16th ACM international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1459359.1459470.

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Ali, Raza, Louis Atallah, Benny Lo, and Guang-Zhong Yang. "Transitional Activity Recognition with Manifold Embedding." In Implantable Body Sensor Networks Conference (BSN). IEEE, 2009. http://dx.doi.org/10.1109/bsn.2009.42.

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Jokinen, Kristiina, Trung Ngo Trong, and Graham Wilcock. "Body movements and laughter recognition." In ICMI '16: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. ACM, 2016. http://dx.doi.org/10.1145/3011263.3011264.

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Ly, Son Thai, Guee-Sang Lee, Soo-Hyung Kim, and Hyung-Jeong Yang. "Emotion Recognition via Body Gesture." In the 2018 International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3278312.3278313.

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Chakraborty, Bhaskar, Ognjen Rudovic, and Jordi Gonzalez. "View-invariant human-body detection with extension to human action recognition using component-wise HMM of body parts." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813302.

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BenAbdelkader, Chiraz, and Yaser Yacoob. "Statistical body height estimation from a single image." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813453.

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xu, bin, and Jian Cui. "Cost-Effective Activity Recognition on Mobile Devices." In 8th International Conference on Body Area Networks. ACM, 2013. http://dx.doi.org/10.4108/icst.bodynets.2013.253656.

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Yan, Song, Johan Wirta, and Joni-Kristian Kamarainen. "Silhouette Body Measurement Benchmarks." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412708.

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Du, Ming, and Ling Guan. "Human recognition by body shape features." In Electronic Imaging 2005, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly. SPIE, 2005. http://dx.doi.org/10.1117/12.585119.

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Sharma, Naman, Hima Bindu Maringanti, and Krishna Asawa. "Upper body pose recognition and classifier." In the 5th ACM COMPUTE Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2459118.2459126.

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Reports on the topic "Body recognition"

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Maurer, Uwe, Asim Smailagic, Daniel P. Siewiorek, and Michael Deisher. Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada534437.

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