Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
We present a fast Bayesian algorithm for category-level object detection in natural images. We modify the popular Naive Bayes Nearest Neighbour classification algorithm to make it suitable for evaluating multiple sub-regions in an image, and offer a fast, filtering-based alternative to the multi-scale sliding window approach. Our algorithm is example-based and requires no learning. Tests on standard datasets and robotic scenarios show competitive detection rates and real-time performance of our algorithm.
Original language | English |
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Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1658-1662 |
Number of pages | 5 |
ISBN (Electronic) | 9781479957514 |
DOIs | |
Publication status | Published - 28 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
ID: 255500434