Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Artificial Intelligence Review - Special issue on lazy learning
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Locally Weighted Polynomial Regression Predictions
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A real-time tracker for markerless augmented reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploratory learning structures in artificial cognitive systems
Image and Vision Computing
Efficient computation of channel-coded feature maps through piecewise polynomials
Image and Vision Computing
Simultaneously learning to recognize and control a low-cost robotic arm
Image and Vision Computing
Invariant object recognition and pose estimation with slow feature analysis
Neural Computation
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One problem in appearance-based pose estimation is the need for many training examples, i.e. images of the object in a large number of known poses. Some invariance can be obtained by considering translations, rotations and scale changes in the image plane, but the remaining degrees of freedom are often handled simply by sampling the pose space densely enough. This work presents a method for accurate interpolation between training views using local linear models. As a view representation local soft orientation histograms are used. The derivative of this representation with respect to the image plane transformations is computed, and a Gauss-Newton optimization is used to optimize all pose parameters simultaneously, resulting in an accurate estimate.