Artificial Intelligence and Pattern Recognition, Vision, Learning

This chapter describes a few problems and methods combining artificial intelligence, pattern recognition, computer vision and learning. The intersection between these domains is growing and gaining importance, as illustrated in this chapter with a few examples. The first one deals with knowledge guided image understanding and structural recognition of shapes and objects in images. The second example deals with code supervision, which allows designing specific applications by exploiting existing algorithms in image processing, focusing on the formulation of processing objectives. Finally, the third example shows how different theoretical frameworks and methods for learning can be associated with the constraints inherent to the domain of robotics.

[1]  Peter Ford Dominey,et al.  The basis of shared intentions in human and robot cognition , 2011 .

[2]  Filipo Studzinski Perotto,et al.  Anticipative coordinated cognitive processes for interactivist and Piagetian theories , 2008, AGI.

[3]  Pierre-Yves Oudeyer,et al.  Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.

[4]  Ludovic Saint-Bauzel,et al.  A Reactive Robotized Interface for Lower Limb Rehabilitation: Clinical Results , 2009, IEEE Transactions on Robotics.

[5]  Aline Deruyver,et al.  Constraint Satisfaction Problem with Bilevel Constraint: Application to Interpretation of Over-Segmented Images , 1997, Artif. Intell..

[6]  L. Vieu.  Spatial Representation and Reasoning in Artificial Intelligence , 1997 .

[7]  Daniel Charlebois.  A planning system based on plan re-use and its application to geographical information systems and remote sensing , 1998 .

[8]  Isabelle Bloch,et al.  Information Fusion in Signal and Image Processing , 2008 .

[9]  Olivier Sigaud,et al.  Towards Deep Developmental Learning , 2016, IEEE Transactions on Cognitive and Developmental Systems.

[10]  V. Pasqui,et al.  Characterization of a Least Effort User-Centered Trajectory for Sit-to-Stand Assistance , 2011 .

[11]  Vincent Martin,et al.  A Learning Approach for Adaptive Image Segmentation , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[12]  David Silver,et al.  Learning to search: Functional gradient techniques for imitation learning , 2009, Auton. Robots.

[13]  Isabelle Bloch,et al.  A constraint propagation approach to structural model based image segmentation and recognition , 2013, Inf. Sci..

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  Isabelle Bloch,et al.  Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations , 2012, Comput. Vis. Image Underst..

[16]  I. B. Gurevich,et al.  Fundamental concepts and elements of image analysis ontology , 2009, Pattern Recognition and Image Analysis.

[17]  Gérard Ligozat,et al.  Reasoning about Cardinal Directions , 1998, J. Vis. Lang. Comput..

[18]  Mohamed Chetouani,et al.  Social-Task Learning for HRI , 2015, ICSR.

[19]  Mario Vento,et al.  Thirty Years Of Graph Matching In Pattern Recognition , 2004, Int. J. Pattern Recognit. Artif. Intell..

[20]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[21]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[22]  Monique Thonnat,et al.  Ontology based complex object recognition , 2008, Image Vis. Comput..

[23]  Marinette Revenu,et al.  An Ontology-Based Model for Representing Image Processing Objectives , 2010 .

[24]  Stefan Schaal,et al.  Learning inverse kinematics , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[25]  Sylvain Calino,et al.  Robot programming by demonstration : a probabilistic approach , 2009 .

[26]  Mehdi Khamassi,et al.  Modelling Individual Differences in the Form of Pavlovian Conditioned Approach Responses: A Dual Learning Systems Approach with Factored Representations , 2014, PLoS Comput. Biol..

[27]  P. Dejean,et al.  Image analysis operators as concept constructors , 1996, Proceeding of Southwest Symposium on Image Analysis and Interpretation.

[28]  Olivier Sigaud,et al.  Path Integral Policy Improvement with Covariance Matrix Adaptation , 2012, ICML.

[29]  Marinette Revenu,et al.  Borg: A Knowledge-Based System for Automatic Generation of Image Processing Programs , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Alessandro Saffiotti,et al.  Anchoring Symbols to Vision Data by Fuzzy Logic , 1999, ESCQARU.

[31]  N. Goodwin,et al.  Learning to Detect Objects in Images via a Sparse, Part-Based Representation , 2004 .

[32]  Michael G. Strintzis,et al.  Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback , 2004, EURASIP J. Adv. Signal Process..

[33]  Takashi Matsuyama.  Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[34]  M R Quillian,et al.  Word concepts: a theory and simulation of some basic semantic capabilities. , 1967, Behavioral science.

[35]  Isabelle Bloch,et al.  Fuzzy spatial relation ontology for image interpretation , 2008, Fuzzy Sets Syst..

[36]  Rachid Alami,et al.  Exploiting human cooperation in human-centered robot navigation , 2010, 19th International Symposium in Robot and Human Interactive Communication.

[37]  Isabelle Bloch,et al.  Fuzzy spatial relationships for image processing and interpretation: a review , 2005, Image Vis. Comput..

[38]  Guillermo Sapiro,et al.  Interactive Image Segmentation via Adaptive Weighted Distances , 2007, IEEE Transactions on Image Processing.

[39]  Cornelius Rosse,et al.  A Reference Ontology for Bioinformatics: The Foundational Model of Anatomy , 2003 .

[40]  Michael M. Richter,et al.  Image processing in case-based reasoning , 2005, Knowl. Eng. Rev..

[41]  Isabelle Bloch,et al.  Spatial reasoning under imprecision using fuzzy set theory, formal logics and mathematical morphology , 2006, Int. J. Approx. Reason..

[42]  Marinette Revenu,et al.  An Ontology-Based Model for Representing Image Processing Application Objectives , 2010, Int. J. Pattern Recognit. Artif. Intell..

[43]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  R. Saravanan,et al.  Ontology based process plan generation for image processing , 2007, Int. J. Metadata Semant. Ontologies.

[45]  Monique Thonnat,et al.  A knowledge-based approach to integration of image processing procedures , 1993 .

[46]  Olivier Sigaud,et al.  Learning Forward Models for the Operational Space Control of Redundant Robots , 2010, From Motor Learning to Interaction Learning in Robots.

[47]  Bruce A. Draper,et al.  ADORE: Adaptive Object Recognition , 1999, ICVS.

[48]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[49]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[50]  U. Rost,et al.  Knowledge-Based Configuration Of Image Processing Algorithms , 1998 .

[51]  R GruberThomas.  Toward principles for the design of ontologies used for knowledge sharing , 1995 .

[52]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Isabelle Bloch,et al.  Sequential spatial reasoning in images based on pre-attention mechanisms and fuzzy attribute graphs , 2008, ECAI.

[55]  Christopher Town,et al.  Ontological inference for image and video analysis , 2006, Machine Vision and Applications.

[56]  Olivier Sigaud,et al.  Many regression algorithms, one unified model: A review , 2015, Neural Networks.

[57]  Isabelle Bloch,et al.  Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition--Application to 3D brain imaging , 2003, Artif. Intell..

[58]  Isabelle Bloch,et al.  Integrating Bipolar Fuzzy Mathematical Morphology in Description Logics for Spatial Reasoning , 2010, ECAI.

[59]  Bernd Neumann,et al.  On scene interpretation with description logics , 2006, Image Vis. Comput..

[60]  Roberto Marcondes Cesar Junior,et al.  Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms , 2005, Pattern Recognit..

[61]  Johan H. C. Reiber,et al.  Multi-agent segmentation of IVUS images , 2004, Pattern Recognit..

[62]  Giulio Sandini,et al.  Developmental robotics: a survey , 2003, Connect. Sci..

[63]  Isabelle Bloch,et al.  Fuzzy morphisms between graphs , 2002, Fuzzy Sets Syst..

[64]  Richard Lepage,et al.  Knowledge-Based Image Understanding Systems: A Survey , 1997, Comput. Vis. Image Underst..

[65]  Pieter Abbeel,et al.  Learning for control from multiple demonstrations , 2008, ICML '08.

[66]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[67]  Kaufman,et al.  A New Color-Namiing System for Graphics Languages , 1982, IEEE Computer Graphics and Applications.

[68]  Takashi Matsuyama.  Expert systems for image processing: Knowledge-based composition of image analysis processes , 1989, Comput. Vis. Graph. Image Process..

[69]  Pieter Abbeel,et al.  Apprenticeship learning and reinforcement learning with application to robotic control , 2008 .

[70]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[71]  Arnold Blömer,et al.  Architecture of the knowledge based configuration system for image analysis 'CONNY' , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[72]  Florence Le Ber,et al.  The Design of an Object-based System for Representing and Classifying Spatial Structures and Relations , 2002, J. Univers. Comput. Sci..

[73]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[74]  K. Doya.  Complementary roles of basal ganglia and cerebellum in learning and motor control , 2000, Current Opinion in Neurobiology.

[75]  Sylvain Calinon,et al.  Robot Programming by Demonstration - a Probabilistic Approach , 2009 .

[76]  Marinette Revenu,et al.  Ho w to formulate image processing applications , 2007 .

[77]  Isabelle Bloch,et al.  Explanatory Reasoning for Image Understanding Using Formal Concept Analysis and Description Logics , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[78]  Steffen Staab,et al.  Exploiting Spatial Context in Image Region Labelling Using Fuzzy Constraint Reasoning , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[79]  Naphtali Rishe,et al.  Content-based image retrieval , 1995, Multimedia Tools and Applications.

[80]  Isabelle Bloch,et al.  Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation , 2006, Pattern Recognit..

[81]  J. Chusid,et al.  Correlative Neuroanatomy , 1951, The Indian Medical Gazette.

[82]  Aline Deruyver,et al.  Qualitative spatial relationships for image interpretation by using a conceptual graph , 2009, Image Vis. Comput..

[83]  Desachy Jacky.  ICARE an Expert System for Automatic Mapping from Satellite Imagery , 1990 .

[84]  Steffen Staab,et al.  Semantic Annotation of Images and Videos for Multimedia Analysis , 2005, ESWC.

[85]  Jane Hunter,et al.  Adding Multimedia to the Semantic Web: Building an MPEG-7 ontology , 2001, SWWS.

[86]  Benjamin Kuipers,et al.  Navigation and Mapping in Large Scale Space , 1988, AI Mag..

[87]  Jacob Stroem,et al.  Traitement d'images , 2004 .

[88]  DenœuxThierry.  Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence , 2008 .

[89]  Isabelle Bloch,et al.  Fuzzy constraint satisfaction problem for model-based image interpretation , 2016, Fuzzy Sets Syst..

[90]  Martin D. Levine,et al.  Low Level Image Segmentation: An Expert System , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[91]  Isabelle Bloch,et al.  Inexact graph matching by means of estimation of distribution algorithms , 2002, Pattern Recognit..

[92]  Marinette Revenu,et al.  CBR for the management and reuse of image-processing expertise: a conversational system , 1999 .

[93]  Sabine Moisan,et al.  What can program supervision do for program reuse? , 2000, IEE Proc. Softw..

[94]  Bernt Schiele,et al.  Learning semantic object parts for object categorization , 2008, Image Vis. Comput..

[95]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[96]  Gabriella Sanniti di Baja,et al.  Case-Based-Reasoning for Image Segmentation , 2008, Int. J. Pattern Recognit. Artif. Intell..

[97]  Marie-Laure Mugnier,et al.  Graph-based Knowledge Representation - Computational Foundations of Conceptual Graphs , 2008, Advanced Information and Knowledge Processing.

[98]  Takashi Matsuyama,et al.  SIGMA: A Knowledge-Based Aerial Image Understanding System , 1990 .

[99]  F. Lyddy.  Cerebellar symmetry in dyslexics , 2002, Trends in Cognitive Sciences.

[100]  Catherine Garbay,et al.  A Society of Goal-Oriented Agents for the Analysis of Living Cells , 1997, AIME.

[101]  Silvia Biasotti,et al.  What’s in an image? , 2005, The Visual Computer.

[102]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[103]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[104]  Gerald L. Lohse,et al.  Towards a texture naming system: Identifying relevant dimensions of texture , 1993, Vision Research.

[105]  John P. McDermott,et al.  Rule-Based Interpretation of Aerial Imagery , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[106]  José L. V. Mejino,et al.  A reference ontology for biomedical informatics: the Foundational Model of Anatomy , 2003, J. Biomed. Informatics.

[107]  M. Revenu,et al.  How to formulate image processing applications ? , 2007 .

[108]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[109]  Steve A. Chien,et al.  Automating Image Processing for Scientific Data Analysis of a Large Image Database , 1996, IEEE Trans. Pattern Anal. Mach. Intell..