Robust Recognition of Scaled Eigenimages through a Hierarchical Approach

Authors:
H. Bischof;A. Leonardis
Affiliations:
-;-
Venue:
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Year:
1998

Citing 9
Cited 5

Illumination Planning for Object Recognition Using Parametric Eigenspaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance

International Journal of Computer Vision
SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition

Neural Computation
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Dynamic Appearance-Based Recognition

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Dealing with occlusions in the eigenspace approach

CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Face recognition from one example view

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Probabilistic visual learning for object detection

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Indexing for local appearance-based recognition of planar objects

Pattern Recognition Letters
Recognizing Objects by Their Appearance Using Eigenimages

SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
Illumination insensitive recognition using eigenspaces

Computer Vision and Image Understanding
Contour-based partial object recognition using symmetry in image databases

Proceedings of the 2005 ACM symposium on Applied computing
Multiresolution approach to biomedical image segmentation with statistical models of appearance

Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, we have proposed a new approach to estimation of the coefficients of eigenimages, which is robust against occlusion, varying background, and other types of non-Gaussian noise [4, 5]. In this paper we show that our method for estimating the coefficients can be applied to convolved and subsampled images yielding the same value of the coefficients. This enables an efficient multiresolution approach, where the values of the coefficients can directly be propagated through the scales. This property is used to extend our robust method to the problem of scaled images. We performed extensive experimental evaluations to confirm our theoretical results.