"Ch. 2. MULTI-HASH: Learning Object Attributes and Hash Tables for Fast 3-D Object Recognition"

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Book	Author(s)	Year	Publisher
Fast learning and invariant object recognition: the sixth-generation breakthrough	Branko Souček	1992	Wiley
Deep Learning in Object Detection and Recognition	Xiaoyue Jiang; Abdenour Hadid; Yanwei Pang; Eric Granger; Xiaoyi Feng	1st ed. 2019	Springer Nature Singapore
Indoor Scene Recognition by 3-D Object Search: For Robot Programming by Demonstration	Pascal Meißner	1st ed. 2020	Springer International Publishing
On recognition of 3-D objects from 2-D images	Yehezkel Lamdan	1987	Courant Institute of Mathematical Sciences, New York University
Object Recognition	Tam Phuong Cao	2011-04-01	IntechOpen
Object recognition by 3-dimensional curve matching.	C. Marc Bastuscheck	1986	Courant Institute of Mathematical Sciences, New York University
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	Himanshu Singh	1st ed.	Apress
Object and face recognition	Bruce/Hump	1994	Lawrence Erlbaum Associates
Integrating Graphics and Vision for Object Recognition	Mark R. Stevens; J. Ross Beveridge	2001	Springer US
Visual Object Recognition	Kristen Grauman; Bastian Leibe		Springer International Publishing
Visual object recognition	Kristen Lorraine Grauman	2011	Morgan & Claypool
Natural object recognition	Thomas M. Strat	1992	Springer-Verlag
Natural Object Recognition	Thomas M. Strat	1992	Springer New York
View-based 3-D Object Retrieval	Yue Gao and Qionghai Dai	2015	Morgan Kaufmann
Automatic model builder for object recognition.	C. Marc Bastuscheck	1987	Courant Institute of Mathematical Sciences, New York University
Fast accessLotus 1-2-3	Rhyder McClure	1988	Simon & Schuster;Distributed by Prentice Hall Trade
Object Recognition, Attention, and Action	Naoyuki Osaka; Ingo Rentschler; Irving Biederman	2007	Springer Japan
Fast Ideas for Busy Teachers, Grades 2-3	School Specialty Publishing	January 15, 2002	Frank Schaffer
Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python	Jason Brownlee	2019	Independently Published
Graduate Attributes, Learning and Employability	Paul Hager; Susan Holland	2006	Springer Netherlands
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Ch. 2. MULTI-HASH: Learning Object Attributes and Hash Tables for Fast 3-D Object Recognition	Lynne Grewe and Avi Kak	Symbolic visual learning /	Ikeuchi, Katsushi.; Veloso, Manuela M.	1997.	Oxford University Press,
Using Hash Object Attributes		SAS 9. 2 Language Reference: Concepts	SAS Publishing	2009	Sas Inst
Ch. 3. Learning Control Strategies for Object Recognition	Bruce A. Draper	Symbolic visual learning /	Ikeuchi, Katsushi.; Veloso, Manuela M.	1997.	Oxford University Press,
3-D Object Creation for Recognition	M. Bennamoun; G. J. Mamic	Object Recognition: Fundamentals and Case Studies	M. Bennamoun; G.J. Mamic	2002	Springer London
Ch. 3. Object recognition		Cognitive psychology: a student's handbook /	Eysenck, Michael W.; Keane, Mark T.,	[1995], ©1995.	Erlbaum,
Multi-Table Reinforcement Learning for Visual Object Recognition	Monica Piñol; Angel D. Sappa; Ricardo Toledo	Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012): Volume 1	Mohan S; S Suresh Kumar	2013	Springer India
Ch. 9. Memorization Learning for Object Recognition	Randal C. Nelson	Early visual learning /	Nayar, Shree K.; Poggio, Tomaso.	1996.	Oxford University Press,
3-D Object Recognition Systems	M. Bennamoun; G. J. Mamic	Object Recognition: Fundamentals and Case Studies	M. Bennamoun; G.J. Mamic	2002	Springer London
Learning Features for Object Recognition	Yingqiang Lin; Bir Bhanu	Genetic and Evolutionary Computation — GECCO 2003: Genetic and Evolutionary Computation Conference Chicago, IL, USA, July 12–16, 2003 Proceedings, Part II	Erick Cantú-Paz; James A. Foster; Kalyanmoy Deb; Lawrence David Davis; Rajkumar Roy; Una-May O'Reilly; Hans-Georg Beyer; Russel Standish; Graham Kendall; Stewart Wilson; Joachim Wegener; Dipankar Dasgupta; Mitchell A. Potter; Alan C. Schultz	2003	Springer Berlin Heidelberg
Deep Learning for Object Recognition		Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python	Jason Brownlee	2019	Independently Published
Ch. 9. Hash Tables		Data structure programming: with the standard template library in C++ /	Bergin, Joseph.	[1998], ©1998.	Springer,
Ch. 20. Hash tables		Data structures & problem solving using Java /	Weiss, Mark Allen.	[2006], ©2006.	Pearson/Addison-Wesley,
Local Representations for Multi-object Recognition	Thomas Deselaers; Daniel Keysers; Roberto Paredes; Enrique Vidal; Hermann Ney	Pattern Recognition: 25th DAGM Symposium, Magdeburg, Germany, September 10-12, 2003, Proceedings	Bernd Michaelis; Gerard Krell	2003	Springer Berlin Heidelberg
Ch. 5. A Self-Organizing Architecture for Invariant 3-D Object Learning and Recognition from Multiple 2-D Views	S. Grossberg and G. Bradski	Industrial applications of neural networks /	Jain, L. C.; Vemuri, V. Rao.	[1999], ©1999.	CRC Press,
Keypoint Signatures for Fast Learning and Recognition	Michael Calonder; Vincent Lepetit; Pascal Fua	Computer Vision - ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I	David Forsyth; Philip Torr; Andrew Zisserman	2008	Springer Berlin Heidelberg
Unsupervised Feature Learning for RGB-D Based Object Recognition	Liefeng Bo; Xiaofeng Ren; Dieter Fox	Experimental Robotics: The 13th International Symposium on Experimental Robotics	Jaydev P. Desai; Gregory Dudek; Oussama Khatib; Vijay Kumar	2013	Springer International Publishing
3-D Modeling for Robotic Tactile Object Recognition	Peter K. Allen	CAD/CAM Robotics and Factories of the Future: Volume III: Robotics and Plant Automation	Birendra Prasad; S. N. Dwivedi; R. Mahajan	1989	Springer Berlin Heidelberg
3-D Modeling for Robotic Tactile Object Recognition	Peter K. Allen	CAD/CAM Robotics and Factories of the Future: Volume III: Robotics and Plant Automation	Birendra Prasad	1989	Springer Berlin Heidelberg
Ensemble Learning for Object Recognition and Tracking	Mehrtash Harandi; Javid Taheri; Brian C. Lovell	Pattern Recognition, Machine Intelligence and Biometrics	Patrick S. P. Wang	2011	Springer Berlin Heidelberg
Semi–supervised Learning with Constraints for Multi–view Object Recognition	Stefano Melacci; Marco Maggini; Marco Gori	Artificial Neural Networks – ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II	Cesare Alippi; Marios M. Polycarpou; Christos Panayiotou; Georgios Ellinas	2009	Springer Berlin Heidelberg
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