HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Localizing Overlapping Parts by Searching the Interpretation Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
Geometric aspects of visual object recognition
Geometric aspects of visual object recognition
Computing exact aspect graphs of curved objects: algebraic surfaces
International Journal of Computer Vision
Pose Estimation by Fusing Noisy Data of Different Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing without Invariants in 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Object Recognition Using Multidimensional Receptive Field Histograms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
A Cubist Approach to Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
Incremental learning with partial instance memory
Artificial Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
International Journal of Computer Vision
A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
Hierarchical building recognition
Image and Vision Computing
Flexible Spatial Configuration of Local Image Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
The quantitative characterization of the distinctiveness and robustness of local image descriptors
Image and Vision Computing
Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding
International Journal of Computer Vision
Flexible spatial models for grouping local image features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Research and applications on georeferenced multimedia: a survey
Multimedia Tools and Applications
Dependable 3D object recognition with two-layered particle filter
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Vision-based 3D object localization using probabilistic models of appearance
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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We describe how to model the appearance of a 3-D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object's appearance. These distributions are organized on two levels. Large variations are handled by partitioning training images into clusters corresponding to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions characterizing uncertainty in the presence, position, and measurements of various discrete features of appearance. Many types of features are used, ranging in abstraction from edge segments to perceptual groupings and regions. A matching procedure uses the feature uncertainty information to guide the search for a match between model and image. Hypothesized feature pairings are used to estimate a viewpoint transformation taking account of feature uncertainty. These methods have been implemented in an object recognition system, OLIVER. Experiments show that OLIVER is capable of learning to recognize complex objects in cluttered images, while acquiring models that represent those objects using relatively few views.