Toward RoboCup without color labeling

Object recognition and localization methods in RoboCup work on color-segmented camera images. Unfortunately, color labeling can be applied to object-recognition tasks only in very restricted environments, where different kinds of objects have different colors. To overcome these limitations, we propose an algorithm, called the CONTRACTING CURVE DENSITY (CCD) algorithm, for fitting parametric curves to image data. The method neither assumes object-specific color distributions or specific edge profiles, nor does it need threshold parameters. Hence, no training phase is needed. To separate adjacent regions, we use local criteria that are based on local image statistics. We apply the method to the problem of localizing the ball and show that the CCD algorithm reliably localizes the ball even in the presence of heavily changing illumination, strong clutter, specularity, partial occlusion, and texture.

[1]  Michael Beetz,et al.  Cooperative probabilistic state estimation for vision-based autonomous mobile robots , 2002, IEEE Trans. Robotics Autom..

[2]  Chris Bailey-Kellogg,et al.  Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially-Distributed Physical Systems , 2001, IJCAI.

[3]  Johan de Kleer,et al.  Readings in qualitative reasoning about physical systems , 1990 .

[4]  Feng Zhao,et al.  Imagistic reasoning , 1995, CSUR.

[5]  Feng Zhao,et al.  Spatial Aggregation: Theory and Applications , 1996, J. Artif. Intell. Res..

[6]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[7]  Gerald J. Sussman,et al.  Intelligence in scientific computing , 1989, CACM.

[8]  Jurjen Caarls,et al.  Fast and Accurate Robot Vision for Vision Based Motion , 2000, RoboCup.

[9]  James S. Duncan,et al.  Game-Theoretic Integration for Image Segmentation , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[11]  Theodosios Pavlidis,et al.  Segmentation of Plane Curves , 1974, IEEE Transactions on Computers.

[12]  Robert Hanek.  Model-Based Image Segmentation Using Local Self-Adapting Separation Criteria , 2001, DAGM-Symposium.

[13]  Roger Y. Tsai,et al.  Techniques for Calibration of the Scale Factor and Image Center for High Accuracy 3-D Machine Vision Metrology , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Kenneth D. Forbus,et al.  Qualitative Spatial Reasoning: The Clock Project , 1991, Artif. Intell..

[15]  Robert Hanek.  The contracting curve density algorithm and its application to model-based image segmentation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Charalabos C. Doumanidis,et al.  In-process control in thermal rapid prototyping , 1997 .

[17]  Hayit Greenspan,et al.  Color- and Texture-based Image Segmentation Using the Expectation-Maximization Algorithm and its Application to Content-Based Image Retrieval. , 1998, ICCV 1998.

[18]  Hiroshi Murase,et al.  Parametric Feature Detection , 1996, International Journal of Computer Vision.

[19]  Michael Beetz,et al.  Fast image-based object localization in natural scenes , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Anthony G. Cohn,et al.  Qualitative Spatial Representation and Reasoning: An Overview , 2001, Fundam. Informaticae.

[22]  Rachid Deriche,et al.  Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach , 2000, ECCV.

[23]  Lawrence J. Rosenblum,et al.  Scientific visualization : advances and challenges , 1994 .

[24]  Thomas O. Binford,et al.  On Detecting Edges , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Pietro Perona,et al.  Automating the hunt for volcanoes on Venus , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[27]  Michael Beetz,et al.  The AGILO autonomous robot soccer team: computational principles, experiences, and perspectives , 2002, AAMAS '02.

[28]  Chris Bailey-Kellogg,et al.  Sampling strategies for mining in data-scarce domains , 2002, Computing in Science & Engineering.

[29]  Beng Chin Ooi,et al.  Discovery of General Knowledge in Large Spatial Databases , 1993 .

[30]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[31]  Monika Lundell,et al.  A Qualitative Model of Physical Fields , 1996, AAAI/IAAI, Vol. 2.

[32]  Thorsten Schmitt,et al.  Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario , 1999, RoboCup.

[33]  K. Yip.  KAM: A System for Intelligently Guiding Numerical Experimentation by Computer , 1991 .

[34]  Nassir Navab,et al.  Fusion of color, shading and boundary information for factory pipe segmentation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[35]  Kenneth Yip.  Structural Inferences from Massive Datasets , 1997, IJCAI.

[36]  Sven Behnke,et al.  Robust Real Time Color Tracking , 2000, RoboCup.

[37]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Johan de Kleer,et al.  A Qualitative Physics Based on Confluences , 1984, Artif. Intell..

[39]  Pedro U. Lima,et al.  Vision-based self-localization for soccer robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[40]  Dimitris N. Metaxas,et al.  Image segmentation based on the integration of pixel affinity and deformable models , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[41]  Chris Bailey-Kellogg,et al.  Qualitative Analysis of Distributed Physical Systems with Applications to Control Synthesis , 1998, AAAI/IAAI.

[42]  Feng Zhao,et al.  Extracting and Representing Qualitative Behaviors of Complex Systems in Phase Spaces , 1991, IJCAI.

[43]  Roberto Manduchi,et al.  Bayesian fusion of color and texture segmentations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[44]  Chris Bailey-Kellogg,et al.  Spatial Aggregation: Language and Applications , 1996, AAAI/IAAI, Vol. 1.

[45]  Sven Behnke,et al.  An Omnidirectional Vision System That Finds and Tracks Color Edges and Blobs , 2001, RoboCup.

[46]  Feng Zhao,et al.  Relation-based aggregation: finding objects in large spatial datasets , 2000, Intell. Data Anal..

[47]  James R. Munkres,et al.  Elements of algebraic topology , 1984 .

[48]  Hisashi Nakamura,et al.  Fast Spatio-Temporal Data Mining of Large Geophysical Datasets , 1995, KDD.

[49]  Wolfram Burgard,et al.  Monte Carlo Localization with Mixture Proposal Distribution , 2000, AAAI/IAAI.

[50]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artif. Intell..

[51]  Ulrich Junker,et al.  History-based Interpretation of Finite Element Simulations of Seismic Wave Fields , 1995, IJCAI.

[52]  Vahab S. Mirrokni,et al.  A Fast Vision System for Middle Size Robots in RoboCup , 2001, RoboCup.

[53]  Thorsten Schmitt,et al.  Vision-based localization and data fusion in a system of cooperating mobile robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[54]  Feng Zhao,et al.  STA: Spatio-Temporal Aggregation with Applications to Analysis of Diffusion-Reaction Phenomena , 2000, AAAI/IAAI.

[55]  Benjamin J. Kaipers,et al.  Qualitative Simulation , 1989, Artif. Intell..

[56]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[57]  William L. Briggs,et al.  A multigrid tutorial , 1987 .

[58]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[59]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[60]  Deborah Silver,et al.  Visualizing features and tracking their evolution , 1994, Computer.

[61]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[62]  Naren Ramakrishnan,et al.  Mining scientific data , 2001, Adv. Comput..

[63]  Brian Falkenhainer,et al.  Compositional Modeling: Finding the Right Model for the Job , 1991, Artif. Intell..

[64]  Anthony G. Cohn,et al.  Qualitative Simulation Based on a Logical Formalism of Space and Time , 1992, AAAI.

[65]  Leo Joskowicz,et al.  Computational Kinematics , 1991, Artif. Intell..

[66]  Raj Acharya,et al.  Robust snake model , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[67]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[68]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[69]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[70]  Chris Bailey-Kellogg,et al.  Influence-Based Model Decomposition , 1999, AAAI/IAAI.

[71]  Benjamin Kuipers,et al.  The Spatial Semantic Hierarchy , 2000, Artif. Intell..

[72]  Monika Lundell.  A Qualitative Model of Gradient Flow in a Spatially Distributed Parameter , 1995 .

[73]  Manuela M. Veloso,et al.  Fast and inexpensive color image segmentation for interactive robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).