Active object recognition using appearance-based representations derived from solid geometric models

We present new test results for our active object recognition algorithms. The algorithms are used to classify and estimate the pose of objects in different stable rest positions and automatically re-position the camera if the class or pose of an object is ambiguous in a given image. Multiple object views are now used in determining both the final object class and pose estimate; previously, multiple views were used for classification only. A feature space trajectory (FST) in eigenspace is used to represent 3D distorted views of an object. FSTs are constructed using images rendered from solid models. We discuss lighting and material settings for photorealism in the rendering process. The FSTs are analyzed to determine the camera positions that best resolve ambiguities. Real objects are recognized from intensity images using the FST representation derived from rendered imagery.

[1]  Steven A. Shafer,et al.  Precision imaging and control for machine vision research at Carnegie Mellon University , 1992, Electronic Imaging.

[2]  David P. Casasent,et al.  Classifier and shift-invariant automatic target recognition neural networks , 1995, Neural Networks.

[3]  B. S. Manjunath,et al.  An Eigenspace Update Algorithm for Image Analysis , 1997, CVGIP Graph. Model. Image Process..

[4]  C. W. Therrien,et al.  Decision, Estimation and Classification: An Introduction to Pattern Recognition and Related Topics , 1989 .

[5]  Thomas Ertl,et al.  Computer Graphics - Principles and Practice, 3rd Edition , 2014 .

[6]  J. Loewenthal.  DECISION , 1969, Definitions.

[7]  David Casasent,et al.  Feature space trajectory representation for active vision , 1997, Defense, Security, and Sensing.

[8]  Steven K. Rogers,et al.  Space object identification using spatiotemporal pattern recognition , 1996, Defense + Commercial Sensing.

[9]  David Casasent,et al.  Feature space trajectory neural net classifier: 8-class distortion-invariant tests , 1995, Other Conferences.

[10]  Daniel M. Gaines,et al.  A National Repository for Design and Process Planning , 1997 .

[11]  M. Carter.  Computer graphics: Principles and practice , 1997 .

[12]  Michael L. Philpott,et al.  Synthetic template methodology for CAD directed robot vision , 1997 .

[13]  John Krumm,et al.  Eigenfeatures for planar pose measurement of partially occluded objects , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Gérard G. Medioni,et al.  Structural Indexing: Efficient 3-D Object Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Frank P. Ferrie,et al.  Active recognition: using uncertainty to reduce ambiguity , 1996, Proceedings of 13th International Conference on Pattern Recognition.