Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a 
Real-World Test of a Neural Model

Franz, MO; Chahl, JS

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

会議論文

Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

MPS-Authors

/persons/resource/persons83919

Franz, MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

http://papers.nips.cc/paper/2247-linear-combinations-of-optic-flow-vectors-for-estimating-self-motion-a-real-world-test-of-a-neural-model.pdf
(出版社版)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Franz, M., & Chahl, J. (2003). Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model. In S., Becker, S., Thrun, & K., Obermayer (Eds.), Advances in Neural Information Processing Systems 15 (pp. 1319-1326). Cambridge, MA, USA: MIT Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-DB45-0

要旨

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for
the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show
that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.