Research output: Contribution to journal › Article › peer-review
We present a biologically-inspired method for object detection which is capable of online and 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 by a simple binary descriptor based on responses of cortical cells. We feed these features into a dynamical neural network which binds compatible features together by employing a Bayesian criterion and a set of previously observed object views. We demonstrate the feasibility of our algorithm for cognitive robotic scenarios by evaluating detection performance on a dataset of common household items.
Original language | English |
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Pages (from-to) | 511-518 |
Number of pages | 8 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 8834 |
Publication status | Published - 1 Jan 2014 |
Additional links |
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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Research output: Contribution to journal › Editorial › peer-review
Research output: Contribution to journal › Editorial › peer-review
Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
ID: 255500659