@inproceedings{34c083327f8243b985bae5f4b2a5638d,
title = "Instance-based object recognition with simultaneous pose estimation using keypoint maps and neural dynamics",
abstract = "We present a method for biologically-inspired object recognition with one-shot learning of object appearance. We use a computationally efficient model of V1 keypoints to select object parts with the highest information content and model their surroundings using simple colour features. This map-like representation is fed into a dynamical neural network which performs pose, scale and translation estimation of the object given a set of previously observed object views. We demonstrate the feasibility of our algorithm for cognitive robotic scenarios and evaluate classification performance on a dataset of household items.",
keywords = "Biologically Inspired Keypoints, Neural Dynamics, Object Recognition, Pose Estimation, Vision",
author = "Oliver Lomp and Kasim Terzi{\'c} and Christian Faubel and {Du Buf}, {J. M. H.} and Gregor Sch{\"o}ner",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-11179-7_57",
language = "English",
isbn = "9783319111780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "451--458",
booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings",
address = "Germany",
note = "24th International Conference on Artificial Neural Networks, ICANN 2014 ; Conference date: 15-09-2014 Through 19-09-2014",
}