The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Integration of multiple feature groups and multiple views into a 3D object recognition system
Computer Vision and Image Understanding
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
The Effect of Gaussian Error in Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grouping-Based Nonadditive Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncertainty Propagation in Model-Based Recognition
International Journal of Computer Vision
Recognition of Articulated and Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bounds on Shape Recognition Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Appearance Models for Object Recognition
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Verifying model-based alignments in the presence of uncertainty
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Partially Occluded Object Recognition Using Statistical Models
International Journal of Computer Vision
Fingerprint Indexing Based on Novel Features of Minutiae Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Performance prediction for individual recognition by gait
Pattern Recognition Letters
Automatic view recognition in echocardiogram videos using parts-based representation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Has my algorithm succeeded? an evaluator for human pose estimators
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
A Computational Learning Theory of Active Object Recognition Under Uncertainty
International Journal of Computer Vision
| Hi-index | 0.14 |
We present a method for predicting fundamental performance of object recognition. We assume that both scene data and model objects are represented by 2D point features and a data/model match is evaluated using a vote-based criterion. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. In the second stage, the similarity information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using real synthetic aperture radar (SAR) data.