Toward a Model-Based Bayesian Theory for Estimating and Recognizing Parameterized 3-D Objects Using Two or More Images Taken from Different Positions

Authors:
B. Cernuschi-Frias;D. B. Cooper;Y.-P. Hung;P. N. Belhumeur
Affiliations:
Univ. of Buenos Aires, Buenos Aires, Argentina;Brown Univ., Providence, RI;Brown Univ., Providence, RI;Brown Univ., Providence, RI
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1989

Citing 8
Cited 5

Three-dimensional object recognition

ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
On Optimally Combining Pieces of Information, with Application to Estimating 3-D Complex-Object Position from Range Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise

Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Robot Vision

Robot Vision
Computer Vision

Computer Vision
Depth Map Processing for Recognizing Objects Modeled by Planes and Quadrics of Revolution

Intelligent Autonomous Systems, An International Conference
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Statistical Analysis of Inherent Ambiguities in Recovering 3-D Motion from a Noisy Flow Field

IEEE Transactions on Pattern Analysis and Machine Intelligence
Part II: 3-D Object Recognition and Shape Estimation from Image Contours Using B-splines, Shape Invariant Matching, and Neural Network

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition, Resolution, and Complexity of Objects Subject to Affine Transformations

International Journal of Computer Vision
Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective

IEEE Transactions on Pattern Analysis and Machine Intelligence
A direct method for real-time tracking in 3-d under variable illumination

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition

Quantified Score

Hi-index	0.14

Visualization

Abstract

A parametric modeling and statistical estimation approach is proposed and simulation data are shown for estimating 3-D object surfaces from images taken by calibrated cameras in two positions. The parameter estimation suggested is gradient descent, though other search strategies are also possible. Processing image data in blocks (windows) is central to the approach. After objects are modeled as patches of spheres, cylinders, planes and general quadrics-primitive objects, the estimation proceeds by searching in parameter space to simultaneously determine and use the appropriate pair of image regions, one from each image, and to use these for estimating a 3-D surface patch. The expression for the joint likelihood of the two images is derived and it is shown that the algorithm is a maximum-likelihood parameter estimator. A concept arising in the maximum likelihood estimation of 3-D surfaces is modeled and estimated. Cramer-Rao lower bounds are derived for the covariance matrices for the errors in estimating the a priori unknown object surface shape parameters.