Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Object location by parallel pose clustering
Computer Vision and Image Understanding
Feature-based object recognition and localization in 3D-space, using a single video image
Computer Vision and Image Understanding
Solution of the simultaneous pose and correspondence problem using Gaussian error model
Computer Vision and Image Understanding
Probabilistic 3D Object Recognition
International Journal of Computer Vision
Computer Vision and Image Understanding
View Variation of Point-Set and Line-Segment Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Pose from 3 Points Using Weak-Perspective
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Indexing for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical learning, localization, and identification of objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
SoftPOSIT: Simultaneous Pose and Correspondence Determination
International Journal of Computer Vision
Point matching as a classification problem for fast and robust object pose estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, a recognition algorithm based on point features is presented. In this algorithm sets of hypothesized matches between model and image points are generated. From them the pose of the object is estimated and stored in a lookup table. When two similar poses are found the pose is assumed to be correct and the hypothesis is verified. The main contribution of this paper is that poses and their uncertainties are represented by the uncertainty regions of the projections of several 3D points, which are circles in the image. These uncertainty regions are due to the measurement uncertainty of the image features, which result in uncertainty in the recovered pose. When two poses are consistent, the pairs of uncertainty regions of the same 3D point will have a non-empty intersection. The algorithm exploits the fact that these uncertainty regions can be computed easily and accurately. The algorithm has been implemented and tested on real images.