TRAFFIC: recognizing objects using hierarchical reference frame transformations
Advances in neural information processing systems 2
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Shape representation in parallel systems
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Augmented attribute representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Deep learning of representations: looking forward
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural networks can be used to learn features that output a whole vector of instantiation parameters and we argue that this is a much more promising way of dealing with variations in position, orientation, scale and lighting than the methods currently employed in the neural networks community. It is also more promising than the hand-engineered features currently used in computer vision because it provides an efficient way of adapting the features to the domain.