A neural architecture for autonomous visually guided robots: results of the NAMOS project
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Düsseldorf
VDI-Verl.
1995
|
| Ausgabe: | Als Ms. gedr. |
| Schriftenreihe: | Verein Deutscher Ingenieure: [Fortschrittberichte VDI / 10]
388 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | VI, 221 S. Ill., graph. Darst. |
| ISBN: | 3183388103 |
Internformat
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| 245 | 1 | 0 | |a A neural architecture for autonomous visually guided robots |b results of the NAMOS project |c Werner von Seelen ... |
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Datensatz im Suchindex
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|---|---|
| adam_text | Titel: A neural architecture for autonomous visually guided robots
Autor: Seelen, Werner von
Jahr: 1995
CONTENTS HI Contents 1 Introduction 1 2 The Mobile Robot MARVIN 3 2.1 Mobile Platform ................................... 4 2.2 Active Stereo Camera System............................ 4 2.3 Hardware Environment ............................... 5 2.4 Software Environment................................ 5 3 Visual Information Processing 8 3.1 Tracking Approaches for Gaze Holding....................... 8 3.1.1 Gaze Control for Pursuit Camera Movements ............... 8 3.1.1.1 Introduction and Motivation.................... 8 3.1.1.2 The Biological Model........................ 9 3.1.1.3 Concept and Computational Theory............... 11 3.1.1.4 How it Works............................ 15 3.1.1.5 System Description and Performance............... 20 3.1.1.6 Discussion and Future Prospects................. 22 3.1.2 Structure-Based Optical Flow for Gaze Holding.............. 25 3.1.2.1 Correspondence Measurement in Image Sequences........ 25 3.1.2.2 A Modified Correlation Algorithm................ 27 3.1.2.3 Implementation........................... 28 3.1.2.4 Application to a Gaze Holding Task ............... 32 3.1.2.5 Elimination of Background Motion................ 33 3.1.2.6 Results and Conclusion ...................... 35 3.2 Preattentive and Cognitive Gaze Control by Saccades............... 36 3.2.1 Saccadic Object Recognition with a Foveal Vision System ........ 36 3.2.1.1 System Survey........................... 36 3.2.1.2 Preattentive Feature Analysis................... 38 3.2.1.3 Cognitive Object Recognition................... 41 3.2.1.4 Interest Map and Camera Control ................ 43
IV CONTENTS 3.2.1.5 Implementation and Results.................... 44 3.2.2 Visual Attention and Saccadic Scene Recognition ............ 45 3.2.2.1 Introduction............................ 45 3.2.2.2 Visual Saliency .......................... 47 3.2.2.3 Pattern Recognition ....................... 48 3.2.2.4 Models for Saccadic Recognition................. 48 3.2.2.5 A Model for Visual Attention and Saccadic Recognition .... 53 3.2.2.6 Interest Map ........................... 55 3.2.2.7 Some Results ........................... 56 3.2.2.8 Conclusion ............................. 57 3.3 Vergence Guided Disparity Estimation Using a Phase Method.......... 58 3.3.1 Introduction.................................. 58 3.3.2 Two-Dimensional Phase Method...................... 59 3.3.3 Accumulation of Disparity Estimates in Scale Space............ 62 3.3.4 Vergence and Accommodation........................ 64 3.3.5 Vergence Calibration............................. 67 3.3.6 Application to Vergence Control and Disparity Maps........... 67 3.3.7 Discussion of Results............................. 68 3.3.8 Conclusion................................... 7C 3.4 Depth Segmentation and Depth Recovery...................... 71 3.4.1 Depth Segmentation............................. 71 3.4.1.1 Panum’s Fusional Area Segmentation.............. 71 3.4.1.2 Target selection.......................... 7‘ 3.4.1.3 Experiments............................ 7 3.4.2 Depth Recovery from Area-Based Stereo Algorithms........... T- 3.4.2.1 Projective Geometry for Local, Area-Based Stereo Algorithms 8 3.4.2.2 Interpretation of Stereo Data................... 8 3.4.2.3 Experiments..... 8 3.5 Motion Parallax.................................... 9 3.5.1 Viewer-Centered Coordinate System .................... £ 3.5.2 Method to Recover Three-Dimensional Structure by Fixation...... i 3.5.2.1 Fixation............................... ! 3.5.2.2 Estimation of Robot
Motion Parameters............. i 3.5.3 Results..................................... Ì 3.6 Visual Obstacle Detection by Inverse Perspective Mapping............li 3.6.1 Inverse Perspective Mapping.........................1 3.6.2 Design Principles and Assumptions.....................1 3.6.3 A Monocular Optical Flow-Based Obstacle Detection Approach.....1
CONTENTS V 3.6.4 The Correlation-Based Optical Flow Algorithm..............109 3.6.5 Stereo Obstacle Detection Scheme with Inverse Perspective Mapping . . Ill 3.7 Image Recognition Based on Higher Order Autocorrelations ...........113 3.7.1 From Images to Features...........................114 3.7.1.1 Scale Invariance by means of Scale Space Data .........114 3.7.1.2 Features: Autocorrelation Functions...............116 3.7.2 Classification.................................117 3.7.2.1 Optimal Linear Classification...................118 3.7.2.2 Linear Discriminant Analysis (LDA)...............119 3.7.3 Experimental Results..............................121 3.7.4 Summary...................................122 4 Planning and Control for Autonomous Systems 124 4.1 Path Planning with Dynamic Fields........................127 4.1.1 The Planning Dynamics...........................127 4.1.2 The Dynamic Field Approach........................129 4.1.3 Experimental Results ............................132 4.2 Integration with Dynamic Neural Fields ......................135 4.2.1 Neural Field Architecture: An Example ..................139 4.2.2 Results ....................................142 4.3 Behavior-Based Visual Navigation: Theory ....................145 4.3.1 Methods....................................148 4.3.2 Making and Stabilization of Decisions....................152 4.3.3 Estimation of Robot’s Position .......................157 5 Adaptation of the Information Processing Structure 163 5.1 Early Visual Processing: Discrete Parametric Representations..........163 5.2 Self-Organization of Binocular Cells.........................168 5.2.1 Biological Findings..............................168 5.2.2 Self-Organization...............................169 5.2.3 Producing Binocular Pattern.........................169 5.2.4 Developing Maps of Binocular Cells.....................172 5.2.5 Representation of Disparity and
Orientation................174 5.2.6 Functional Aspects..............................178 6 Aspects of a Neural Architecture 181 6.1 Principles.......................................181 6.2 Structure of the system................................183 6.3 Coupling of Behavioral Modules ..........................186
VI CONTENTS 7 Demonstration of Results 188 7.1 Behavior-Based Visual Navigation: Experiment..................188 7.2 Demonstration in a Maze Like Environment....................192 7.3 Concurrent Active Vision and Depth Reconstruction ...............195 7.3.1 Saccadic Scene Exploration and Recognition ...............196 7.3.2 Stereoscopic Tracking.............................198 7.3.3 Depth Segmentation and Reconstruction..................200 Acknowledgements 205 Bibliography 206
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| illustrated | Illustrated |
| indexdate | 2024-07-09T17:54:40Z |
| institution | BVB |
| isbn | 3183388103 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007023141 |
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| physical | VI, 221 S. Ill., graph. Darst. |
| publishDate | 1995 |
| publishDateSearch | 1995 |
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| series2 | Verein Deutscher Ingenieure: [Fortschrittberichte VDI / 10] |
| spelling | A neural architecture for autonomous visually guided robots results of the NAMOS project Werner von Seelen ... Als Ms. gedr. Düsseldorf VDI-Verl. 1995 VI, 221 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Verein Deutscher Ingenieure: [Fortschrittberichte VDI / 10] 388 Navigation (DE-588)4041457-7 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Autonomer Roboter (DE-588)4304075-5 gnd rswk-swf Autonomer Roboter (DE-588)4304075-5 s Navigation (DE-588)4041457-7 s Neuronales Netz (DE-588)4226127-2 s DE-604 Seelen, Werner von 1936- Sonstige (DE-588)172368421 oth 10] Verein Deutscher Ingenieure: [Fortschrittberichte VDI 388 (DE-604)BV000897204 388 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007023141&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | A neural architecture for autonomous visually guided robots results of the NAMOS project Navigation (DE-588)4041457-7 gnd Neuronales Netz (DE-588)4226127-2 gnd Autonomer Roboter (DE-588)4304075-5 gnd |
| subject_GND | (DE-588)4041457-7 (DE-588)4226127-2 (DE-588)4304075-5 |
| title | A neural architecture for autonomous visually guided robots results of the NAMOS project |
| title_auth | A neural architecture for autonomous visually guided robots results of the NAMOS project |
| title_exact_search | A neural architecture for autonomous visually guided robots results of the NAMOS project |
| title_full | A neural architecture for autonomous visually guided robots results of the NAMOS project Werner von Seelen ... |
| title_fullStr | A neural architecture for autonomous visually guided robots results of the NAMOS project Werner von Seelen ... |
| title_full_unstemmed | A neural architecture for autonomous visually guided robots results of the NAMOS project Werner von Seelen ... |
| title_short | A neural architecture for autonomous visually guided robots |
| title_sort | a neural architecture for autonomous visually guided robots results of the namos project |
| title_sub | results of the NAMOS project |
| topic | Navigation (DE-588)4041457-7 gnd Neuronales Netz (DE-588)4226127-2 gnd Autonomer Roboter (DE-588)4304075-5 gnd |
| topic_facet | Navigation Neuronales Netz Autonomer Roboter |
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