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000827091 019__ $$a1029662531$$a1029781537$$a1029860488$$a1033637408
000827091 020__ $$a9789811061714$$q(electronic book)
000827091 020__ $$a9811061718$$q(electronic book)
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000827091 0247_ $$a10.1007/978-981-10-6171-4$$2doi
000827091 035__ $$aSP(OCoLC)on1029352614
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000827091 050_4 $$aTJ211.43
000827091 08204 $$a629.8/933$$223
000827091 1001_ $$aLiu, Huaping,$$eauthor.
000827091 24510 $$aRobotic tactile perception and understanding :$$ba sparse coding method /$$cHuaping Liu, Fuchun Sun.
000827091 264_1 $$aSingapore :$$bSpringer,$$c2018.
000827091 300__ $$a1 online resource
000827091 336__ $$atext$$btxt$$2rdacontent
000827091 337__ $$acomputer$$bc$$2rdamedia
000827091 338__ $$aonline resource$$bcr$$2rdacarrier
000827091 347__ $$atext file$$bPDF$$2rda
000827091 504__ $$aIncludes bibliographical references.
000827091 5050_ $$aIntro; Foreword; Preface; Acknowledgements; Contents; Acronyms; Mathematical Notation; Part I Background; 1 Introduction; 1.1 Robotic Manipulation and Grasp; 1.2 Robotic Tactile Perception; 1.3 Tactile Exploratory Procedure; 1.4 Tactile Perception for Shape; 1.5 Tactile Perception for Texture; 1.6 Tactile Perception for Deformable Objects; 1.7 Visual-Tactile Fusion for Object Recognition; 1.8 Public Datasets; 1.8.1 Tactile Dataset; 1.8.2 Visual-Tactile Fusion Datasets; 1.9 Summary; References; 2 Representation of Tactile and Visual Modalities; 2.1 Tactile Modality Representation
000827091 5058_ $$a2.1.1 Tactile Sequence2.1.2 Dynamic Time Warping Distance; 2.1.3 Global Alignment Kernel; 2.2 Visual Modality Representation; 2.3 Summary; References; Part II Tactile Perception; 3 Tactile Object Recognition Using Joint Sparse Coding; 3.1 Introduction; 3.2 Kernel Sparse Coding; 3.3 Joint Kernel Sparse Coding; 3.4 Experimental Results; 3.4.1 Data Collection; 3.4.2 Result Analysis; 3.4.3 Results for the Public Dataset; 3.5 Summary; References; 4 Tactile Object Recognition Using Supervised Dictionary Learning; 4.1 Introduction; 4.2 Tactile Dictionary Learning; 4.3 Extreme Learning Machines
000827091 5058_ $$a4.4 Extreme Kernel Sparse Learning4.5 Reduced Extreme Kernel Sparse Learning; 4.6 Optimization Algorithm; 4.6.1 Calculating the Sparse Coding Vectors; 4.6.2 Calculating the Dictionary Atoms; 4.6.3 Calculating the ELM Coefficients; 4.7 Algorithm Analysis; 4.8 Experimental Results; 4.8.1 Data Description and Experimental Setting; 4.8.2 Parameter Selection; 4.8.3 Accuracy Performance Comparison; 4.8.4 Comparison of Reduced Strategies; 4.9 Summary; References; 5 Tactile Adjective Understanding Using Structured Output-Associated Dictionary Learning; 5.1 Introduction; 5.2 Problem Formulation
000827091 5058_ $$a5.3 Optimization Algorithm5.3.1 Calculating the Sparse Coding Vectors; 5.3.2 Calculating the Dictionary Atoms; 5.3.3 Calculating the Classifier Parameters; 5.3.4 Algorithm Summarization; 5.4 Classifier Design; 5.5 Experimental Results; 5.5.1 Data Description and Experimental Setting; 5.5.2 Performance Comparison; 5.5.3 Parameter Sensitivity Analysis; 5.6 Summary; References; 6 Tactile Material Identification Using Semantics-Regularized Dictionary Learning; 6.1 Introduction; 6.2 Linearized Tactile Feature Representation; 6.3 Motivation and Problem Formulation; 6.4 Proposed Model
000827091 5058_ $$a6.5 Optimization Algorithm6.5.1 Calculating the Sparse Coding Vectors; 6.5.2 Calculating the Dictionary Atoms; 6.5.3 Algorithm Summarization; 6.6 Classifier Design; 6.7 Experimental Results; 6.7.1 Experimental Setting; 6.7.2 Performance Comparison; 6.8 Summary; References; Part III Visual-Tactile Fusion Perception; 7 Visual-Tactile Fusion Object Recognition Using Joint Sparse Coding; 7.1 Introduction; 7.2 Problem Formulation; 7.3 Kernel Sparse Coding for Visual-Tactile Fusion; 7.3.1 Kernel Sparse Coding; 7.3.2 Joint Kernel Group Sparse Coding; 7.4 Experimental Results; 7.4.1 Data Collection
000827091 506__ $$aAccess limited to authorized users.
000827091 520__ $$aThis book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of structured sparse coding models is developed to address the issues of dynamic tactile sensing. The book then proves that the proposed framework is effective in solving the problems of multi-finger tactile object recognition, multi-label tactile adjective recognition and multi-category material analysis, which are all challenging practical problems in the fields of robotics and automation. The proposed sparse coding model can be used to tackle the challenging visual-tactile fusion recognition problem, and the book develops a series of efficient optimization algorithms to implement the model. It is suitable as a reference book for graduate students with a basic knowledge of machine learning as well as professional researchers interested in robotic tactile perception and understanding, and machine learning.
000827091 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 26, 2018).
000827091 650_0 $$aRobot hands.
000827091 650_0 $$aRobots$$xProgramming.
000827091 7001_ $$aSun, Fuchun,$$d1964-$$eauthor.
000827091 77608 $$iPrint version: $$z981106170X$$z9789811061707$$w(OCoLC)994819764
000827091 852__ $$bebk
000827091 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-6171-4$$zOnline Access$$91397441.1
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000827091 980__ $$aEBOOK
000827091 980__ $$aBIB
000827091 982__ $$aEbook
000827091 983__ $$aOnline
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