Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Feature extraction from faces using deformable templates
International Journal of Computer Vision
Computer Vision
Object Recognition Using Subspace Methods
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Statistical learning, localization, and identification of objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Fast object recognition in noisy images using simulated annealing
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Example Based Learning for View-Based Human Face Detection
Example Based Learning for View-Based Human Face Detection
Face Recognition Under Varying Pose
Face Recognition Under Varying Pose
Journal of Cognitive Neuroscience
Depth from edge and intensity based stereo
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A hierarchical non-parametric method for capturing non-rigid deformations
Image and Vision Computing
| Hi-index | 0.00 |
A simple method is presented for detecting, localizing and recognizing classes of objects, that accommodates wide variation in an object's pose. The method utilizes a small two-dimensional template that is warped into an image, and converts localization to a one-dimensional sub-problem, with the search for a match between image and template executed by dynamic programming. The method recovers three of the six degrees of freedom of motion (2 translation, 1 rotation), and accommodates two more DOF in the search process (1 rotation, 1 translation), and is extensible to the final DOF.Experiments demonstrate that the method provides an efficient search strategy that outperforms normalized correlation. This is demonstrated in the example domain of face detection and localization, and is extended to more general detection tasks. An additional technique recovers a rough object pose from the match results, and is used in a two stage recognition experiment using maximization of mutual information.