$LAYYYTER
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@trackingsparse-blog

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LaRank vs OLaRank
Synthetic data with dimensions $[250,2]$.
Results (train/test):
LaRank .992/.964
OLaRank online manner: 0.552, After learning 0.964/0.956.
Winner-takes-it-all multiclass svm using structured output SVM. - Gist is a simple way to share snippets of text and code with others.
Loss functions
Hinge loss $$\max \{0,1-y_n w^T x_n \}$$
Log loss $$\log [1+ \exp{(-y_n w^T x_n) }]$$
Exponential loss $$\exp{(-y_n w^T x_n) }$$
Object localization $$ \Delta(y,y')=1- \frac{|y \cap y'|}{|y \cup y'|}$$
Rotational invariance

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Rotational non-invariance
Exploratory analysis for COSFIRE operators vs sparse portraits.
Exploratory analysis of COSFIRE filters on sparse portraits
Problem: find a way how COSFIRE filters could detect the object in a robust way?
what does define a good filter?
How much tuples are necessary for operator to be useful?
Steps used to perform analysis:
Calculate COSFIRE operator at each location in the middle of each ground truth data point.
Visualize it and compare to the image itself.
Save resulting images to create a video
sparse velocity (speed decomposition)
Video c09

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Video c01
c15 prior normalization
c09 prior normalization
c01 prior normalization
Sparse portraits in different color spaces part 2.

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Sparse portraits in different color spaces part 1.
Difference between video decomposition if sharpening is used. This is video 1, frames 1,20,30,35.