Artificial Vision for Robots - Aleksander, I.

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Sommaire:
1 Introduction.- The Front End 12; Image Processing 13; Pattern Recognition 13; Applications 14; Structure of the Book 16; Future Directions for Robot Vision 21.- I: Techniques.- 2 Software or Hardware for Robot Vision?.- 25; Direct versus Iterative Implementation 26; Sequential versus Parallel Algorithms 28; Computation versus Use of Look-up Tables 31; Computer Addressing versus Raster Scanning 34; Structural versus Correlative Comparisons 36; Conclusion 38.- 3 Comparison of Five Methods for the Recognition of Industrial Parts.- 43; Discussion of This Method 49; Conclusion 55.- 4 Syntactic Techniques in Scene Analysis.- 58; Acquisition of Images and Extraction of Features 60; Edges 61; Lines 62; Formal Description of a Scene 66; Figures 68; A Computational Model: The Pandemonium 71.- II: Applications.- 5 Recognition of Overlapping Workpieces by Model-Directed Construction of Object Contours.- 77; The Scene 78; The Model 78; The Scene Representation 80; Production Rules for Reference Objects 81; The Recognition Procedure 86; Results 90.- 6 Simple Assembly Under Visual Control.- The Experimental System 93; Picture Processing 99; The Parallel Projection Optical System 105; Object Location Using Parallel Optics 107; Calibration 109; Distributed Processing 111; Conclusion 112.- 7 Visually Interactive Gripping of Engineering Parts from Random Orientation.- 114; Problem Definition 115; Manipulative Techniques 118; The Integrated System 136; Concluding Remarks 143.- 8 An Interface Circuit for a Linear Photodiode Array Camera.- Circuit Operation 148; Programming 152; Components Used 152.- III: Adaptive Processing for Vision.- 9 Networks of Memory Elements: A Processor for Industrial Automation.- 155; Character Recognition - A Deterministic Problem to a Greater or Lesser Extent 156; Some Aspects of Industrial Pattern Recognition 159; Networks of Memory Elements for Pattern Recognition 161; Applications 168; Recognition of Piece-parts 170; Unconstrained Piece-parts 171; Identification of Highly Constrained Piece-parts 172; Pattern Detection in Unspecifiable Data 175; Summary 177.- 10 Computer Vision Systems for Industry: Comparisons.- Is it Difficult for Computers to Recognize Patterns? 179; Who Needs Pattern Recognition? 180; What the Automatic Eye Needs to See 182; What the Automatic Brain Needs to Compute and How 183; Special Architectures 191; Fixed Implementations 192; Conclusion: Pattern Recognition Comes of Age 192; Appendix 1 194.- 11 Memory Networks for Practical Vision Systems: Design Calculations.- 197; The RAM/Neuron Analogy 198; Single Layer, Randomly Connected Nets: Parameters and Definitions 200; Analysis of System Training and Responses 202; Analysis of Responses to Shapes 207; Alternative Structures for Future Development 212.- 12 Emergent Intelligence from Adaptive Processing Systems.- 215; Emergent Intelligent Properties 216; Level 0 Structures: Image Classifiers 218; Level 1 Structures: Decision Feedback 221; Level 2 Structures: a Preamble 223; Level 2 Structures: Emergent Properties 227; Level 3 Structures: the Attention Mechanism 230; Conclusion 231.