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| author | Jon Whiteaker <jbw@berkeley.edu> | 2012-02-27 22:28:57 -0800 |
|---|---|---|
| committer | Jon Whiteaker <jbw@berkeley.edu> | 2012-02-27 22:28:57 -0800 |
| commit | 65fa4b2d3c6ce5703fc52d9cf097669890282344 (patch) | |
| tree | 4627f95189b27cfc7d9a5dbc6698f2830df363d4 | |
| parent | 3b1bf0cec030136c2e3e97a468ba9df176098941 (diff) | |
| download | kinect-65fa4b2d3c6ce5703fc52d9cf097669890282344.tar.gz | |
adding outline of paper
| -rw-r--r-- | controlled.tex | 5 | ||||
| -rwxr-xr-x | data/face-run-recognition-accuracy.py | 2 | ||||
| -rwxr-xr-x | data/nn-goldman.py | 26 | ||||
| -rw-r--r-- | kinect.tex | 4 | ||||
| -rw-r--r-- | uncontrolled.tex | 6 |
5 files changed, 34 insertions, 9 deletions
diff --git a/controlled.tex b/controlled.tex new file mode 100644 index 0000000..002b490 --- /dev/null +++ b/controlled.tex @@ -0,0 +1,5 @@ +\section{Controlled Skeleton Recognition} +\label{sec:controlled} + +\subsection{Experiment design} +\subsection{Results} diff --git a/data/face-run-recognition-accuracy.py b/data/face-run-recognition-accuracy.py index 4d0fc03..7d39b41 100755 --- a/data/face-run-recognition-accuracy.py +++ b/data/face-run-recognition-accuracy.py @@ -18,7 +18,7 @@ for line in open(sys.argv[1]): user = line[1] if run not in runs: runs[run] = 0 - labels[run] = users.index(user) + 1 + labels[run] = int(user) #users.index(user) + 1 for line in open(sys.argv[2]): line = line.split(',') diff --git a/data/nn-goldman.py b/data/nn-goldman.py index 297544b..34072de 100755 --- a/data/nn-goldman.py +++ b/data/nn-goldman.py @@ -3,13 +3,19 @@ import sys import numpy as np #in place modification ! -def normalize(a): - print a - for i in range(a.shape[1]): +def normalize(a,weights=None): + if weights == None: + weights= {} + cols = a.shape[1] + for i in range(cols): + weights[i] = None + + for i in weights.keys(): column = a[:,i] - weights = np.mean(column), np.std(column) - a[:,i] = (column-weights[0])/weights[1] - return a + if weights[i] == None: + weights[i] = np.mean(column), np.std(column) + a[:,i] = (column-weights[i][0])/weights[i][1] + return a,weights def knn_search(names,d1,d2,k): for i,row2 in enumerate(d2): @@ -29,7 +35,11 @@ if __name__ == "__main__": sk_data = sk_data[:,1:] noise1 = np.random.normal(0,var,sk_data.shape) noise2 = np.random.normal(0,var,sk_data.shape) - sk1 = normalize(sk_data+noise1) - sk2 = normalize(sk_data+noise2) + #sk1,weights = normalize(sk_data+noise1) + #sk2,weights = normalize(sk_data+noise2,weights) + sk1 = sk_data + noise1 + sk2 = sk_data + noise2 + print sk1 + print sk2 knn_search(names,sk1,sk2,1) @@ -61,6 +61,10 @@ \input {uniqueness} +\input{controlled} + +\input{uncontrolled} + \input{conclusion} { diff --git a/uncontrolled.tex b/uncontrolled.tex new file mode 100644 index 0000000..316b3c9 --- /dev/null +++ b/uncontrolled.tex @@ -0,0 +1,6 @@ +\section{Uncontrolled Skeleton Recognition} +\label{sec:uncontrolled} + +\subsection{Algorithm design} +\subsection{Experiment design} +\subsection{Results} |
