EXPERIMENTAL RESULTS
No learning (alpha = 1/3 in common)
Assuming all pixels are background events, they are all accepted to the pixel's codeword. The codeword mean and variance are updated accordingly as we discussed.
Fixed illumination
Variable illumination
Codeword learning
Set a critical value t* as 4, 3, 2, ... to see how this codeword learning affects the variance adaptation. Multiple codewords are allowed.
NOTES:
The images are resized (80x60 by sub-sampling) to accelerate the learning process.
In the codeword, variance, F, and t images, white pixels mean no codeword matched. However, in the variance image, some pixels having high variance show white pixels.
The variance plot is showing the change of variances of selected 5 sample pixels. Since some pixels have multiple codewords, there may be several plot for those codewords.
The 'cumulative count of new codewords' is what we discussed last time.
Fixed illumination
s2_init = 9.0 (alpha = 0.3333, beta=0.1)
s2_init = 2.5
New one-minute sequences
There are three sequences 'lowfixed', 'highfixed', and 'variable' which were recorded for one minutes. TD = threshold for detection, TC = threshold for coding.
Old algorithm (TD=TC)
Low fixed
High fixed
Variable
New algorithm (separate TD and TC)
Low fixed
High fixed
Variable
Non-parametric Variance Estimation with Single Exponential Smoothing
Variable
Apr 8, 2004
Non-para / No MeanAdj / No MeanDBL / No VarDBL
Non-para / No MeanAdj / No MeanDBL / Yes VarDBL
Non-para / Yes MeanAdj / No MeanDBL / No VarDBL
Non-para / Yes MeanAdj / No MeanDBL / Yes VarDBL