Lecture 15 - Stanford Vision Lab
Lecture 15 - Stanford Vision Lab
Lecture 15 - Stanford Vision Lab
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Foreground model<br />
Generative probabilistic model<br />
Clutter model<br />
Gaussian shape pdf Prob. of detection Uniform shape pdf # detections<br />
0.8 0.75<br />
Assumptions: (a) Clutter independent of foreground detections<br />
(b) Clutter detections independent of each other<br />
Example<br />
1. Object Part Positions<br />
2. Part Absence 3a. N false detect<br />
0.9<br />
p Poisson (N 1 | 1 )<br />
p Poisson (N 2 | 2 )<br />
p Poisson (N 3 | 3 )<br />
3b. Position f. detect<br />
N 1<br />
N 2<br />
N 3<br />
Fei-Fei Li<br />
<strong>Lecture</strong> <strong>15</strong> -<br />
64<br />
14‐Nov‐11