Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Computing the Aspect Graph for Line Drawings of Polyhedral Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing exact aspect graphs of curved objects: solid of revolution
International Journal of Computer Vision
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
Space and Time Bounds on Indexing 3D Models from 2D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
3d object recognition using invariant feature indexing of interpretation tables
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Perceptual grouping for generic recognition
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Polynomial-Time Geometric Matching for Object Recognition
International Journal of Computer Vision
Matching 3-D Models to 2-D Images
International Journal of Computer Vision
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pose Clustering Guided by Short Interpretation Trees
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Efficient pose estimation using view-based object representations
Machine Vision and Applications
Journal of Cognitive Neuroscience
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In model-based object recognition and pose estimation, it is common for the set of extracted image features to be much larger than the set of object model features owing to clutter in the image. However, another class of recognition problems has a large model, but only a portion of the object is visible in the image, in which a small set of features can be extracted, most of which are salient. In this case, reducing the effective complexity of the object model is more important than the image clutter. We describe techniques to accomplish this by sampling the space of object positions. A subset of the object model is considered for each sampled pose. This reduces the complexity of the method from cubic to linear in the number of extracted features. We have integrated this technique into a system for recognizing craters on planetary bodies that operates in real-time.