Springer Perception Engineering

1 Introduction.- 1.1 Motivation.- 1.2 Issues.- 1.3 Contribution.- 1.4 Example of results.- 2 Natural Object Recognition.- 2.1 Visual capabilities for autonomous robots.- 2.2 Related research.- 2.2.1 Recognizing objects.- 2.2.2 Recognizing natural scenes.- 2.3 Limitations of current machine-vision technology.- 2.3.1 Shape.- 2.3.2 Universal partitioning.- 2.3.3 Contextual knowledge.- 2.3.4 Computational complexity.- 2.4 Key ideas.- 2.4.1 Context-limited vision.- 2.4.2 Global consistency.- 2.4.3 Candidate comparison to control complexity.- 2.4.4 Layered partitions.- 2.5 Experimental results.- 2.6 Conclusions.- 3 A Vision System for off-Road Navigation.- 3.1 Task scenario.- 3.2 Prior knowledge.- 3.3 The role of geometry.- 3.3.1 Sources and limitations of range data.- 3.3.2 Using three-dimensional geometric information.- 3.4 A vocabulary for recognition.- 3.4.1 Target vocabulary.- 3.4.2 Recognition vocabulary.- 3.5 Contextual information.- 3.5.1 Types of context.- 3.5.2 Using context.- 4 Context-Based Vision.- 4.1 Conceptual Architecture.- 4.1.1 Overview.- 4.1.2 Context sets.- 4.1.3 Candidate generation.- 4.1.4 Clique formation.- 4.1.5 Candidate comparison.- 4.1.6 The recognition process.- 4.2 Implementation of Condor.- 4.2.1 Processing sequence.- 4.2.2 Representation of context.- 4.2.3 Context-set construction.- 4.3 Example of natural-object recognition.- 4.3.1 Candidate generation.- 4.3.2 Candidate comparison.- 4.3.3 Clique formation.- 4.3.4 Clique selection.- 4.4 Automated knowledge acquisition.- 4.4.1 Learning a description of the environment.- 4.4.2 Applying recognition procedures more effectively.- 4.4.3 Refining context sets.- 4.4.4 Discovering new procedures.- 4.5 Complexity analysis.- 4.5.1 Mathematical details.- 4.5.2 The number of cliques.- 4.6 Discussion.- 4.6.1 Reliability.- 4.6.2 Recognition strategies.- 4.6.3 Knowledge-base construction.- 4.6.4 Multiple-image interpretation.- 4 Context-Based Vision.- 5.1 Evaluation scenario.- 5.2 Experimentation.- 5.2.1 Experiment 1.- 5.2.2 Experiment 2.- 5.2.3 Experiment 3.- 5.3 Analysis of results.- 5.3.1 Fixing mistakes.- 5.3.2 Accounting for success.- 5.3.3 Evaluating relevance to the goal.- 6 Conclusion.- 6.1 Contribution.- 6.2 Evaluation.- 6.2.1 Competence.- 6.2.2 Scalability.- 6.2.3 Generality.- 6.2.4 Evaluation plan.- 6.3 Summary.- A The Core Knowledge Structure.- A.1 Introduction.- A.2 Core Knowledge Structure.- A.3 Logical Interpretation of the CKS Database.- A.3.1 Semantics.- A.3.2 Insertions.- A.3.3 Queries.- A.3.4 User-Defined Relations.- A.3.5 Discussion.- A.4 Slot Access.- A.5 Summary.- References.