3D object recognition from static 2D views using multiple coarse data channels

Abstract A 3D object recognition system is described that employs novel multiresolution representation and coarse encoding of feature information. Modifications are bought to classic feature extraction methods by proposing the use of wavelet transform maxima for directing the actions of feature extraction modules. The reasons behind the use of a multi-channel architecture are described, together with the description of the feature extraction and coarse modules. The targeted field of application being automatic categorisation of natural objects, the proposed system is designed to run on ordinary hardware platforms and to process an input in a short timeframe. The system has been evaluated on a variety of 2D views of a set of 5 synthetic objects designed to present various degrees of similarity, as being rated by a panel of human subjects. Parallels between these ratings and the system’s behaviour are drawn. Additionally a small set of photomicrographs of fish larvae has been used to assess the system’s performance when presented with very similar, non-rigid shapes. For comparison, the parameters extracted from each image were fed into two categorisers, discriminant analysis and multilayer feedforward neural network with backpropagation of error. Experimental evidence is presented which demonstrates the efficacy of the methods. The satisfactory categorisation performances of the system are reported, and conclusions are drawn about the system’s behaviour.

[1]  N. Cressie,et al.  A statistical approach to identifying closed object boundaries in images , 1994, Advances in Applied Probability.

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

[3]  Bernt Schiele,et al.  Transinformation of object recognition and its application to viewpoint planning , 1997, Robotics Auton. Syst..

[4]  Albert Bijaoui,et al.  A Multiscale Vision Model to Analyse Field Astronomical Images , 1997 .

[5]  Israel Cohen,et al.  Orthonormal shift-invariant wavelet packet decomposition and representation , 1997, Signal Process..

[6]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[7]  Michael Unser,et al.  A review of wavelets in biomedical applications , 1996, Proc. IEEE.

[8]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[9]  J. Fleiss.  Statistical methods for rates and proportions , 1974 .

[10]  M. Tarr,et al.  Mental rotation and orientation-dependence in shape recognition , 1989, Cognitive Psychology.

[11]  Bartlett W. Mel.  SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[12]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Phil F. Culverhouse,et al.  Expert visual classification and neural networks: can general solutions be found? , 1994, Proceedings of OCEANS'94.

[14]  S. Edelman,et al.  Orientation dependence in the recognition of familiar and novel views of three-dimensional objects , 1992, Vision Research.

[15]  Henry L. Roediger,et al.  Research Methods in Psychology , 1985 .

[16]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[17]  S. Ullman.  Aligning pictorial descriptions: An approach to object recognition , 1989, Cognition.

[18]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Bernt Schiele,et al.  Object Recognition Using Multidimensional Receptive Field Histograms , 1996, ECCV.

[20]  K. Hoffmann,et al.  Visual Inspection of Three-Dimensional Objects by Human Observers , 1996, Perception.

[21]  Shimon Edelman.  Representation without reconstruction , 1994 .

[22]  P. Dutilleux.  An Implementation of the “algorithme à trous” to Compute the Wavelet Transform , 1989 .

[23]  Alireza Khotanzad,et al.  Recognition and pose estimation of unoccluded three-dimensional objects from a two-dimensional perspective view by banks of neural networks , 1996, IEEE Trans. Neural Networks.

[24]  Rajesh P. N. Rao,et al.  Modeling Saccadic Targeting in Visual Search , 1995, NIPS.

[25]  Shimon Ullman,et al.  The computational study of vision , 1989 .

[26]  Yee-Hong Yang,et al.  Multiresolution Color Image Segmentation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  R R Sokal,et al.  Classification: Purposes, Principles, Progress, Prospects , 1974, Science.

[28]  Michael Brady,et al.  Representing shape , 1984, ICRA.

[29]  Kuo-Chin Fan,et al.  Image Registration Using a New Edge-Based Approach , 1997, Comput. Vis. Image Underst..

[30]  Roland T. Chin,et al.  Scale-Based Detection of Corners of Planar Curves , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Stéphane Mallat,et al.  Wavelets for a vision , 1996, Proc. IEEE.

[32]  I. Biederman.  Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[33]  Stephen Grossberg,et al.  Fast-learning VIEWNET architectures for recognizing three-dimensional objects from multiple two-dimensional views , 1995, Neural Networks.

[34]  P. Culverhouse,et al.  Automatic classification of field-collected dinoflagellates by artificial neural network , 1996 .

[35]  A. Bijaoui,et al.  Wavelets and the study of the distant Universe , 1996, Proc. IEEE.

[36]  Jean-Luc Starck,et al.  1 - Restauration des images multi-échelles par l'algorithme à trous , 1994 .

[37]  S. Ullman.  Visual routines , 1984, Cognition.

[38]  Tom Tollenaere,et al.  A modular artificial neural network for texture processing , 1993, Neural Networks.

[39]  David Marr,et al.  VISION A Computational Investigation into the Human Representation and Processing of Visual Information , 2009 .

[40]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .