Vehware --- Ali Arda Eker

09/14/2016

- CPT form is prepared and submitted.

- Spoke with Mr. Bill (CEO of Vehware) about possible projects of his company which are about visual information processing.

- Spoke with Prof. Yin about his project with Vehware.

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09/21/2016

- CPT form is accepted and initialized.

- Prof. Yin approved that I can work in his project with Vehware.

- Bill arranged a meeting for me to speak with a executive from Coca-Cola who is interested in image processing.

- Watched the demo made by Bill to the executive from Coca-Cola about visual information processing.

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09/28/2016

- Talked to the Peng Liu who is the phd student of Prof. Yin. We will be working in the eye tracking project together.

- Installed related software and frame work to my pc. OpenCV and visual studio 2015 will be used.

- Sent an e mail to Peng. Waiting to hear from him or Prof. Yin about what is next.

10/05/2016

Goal of the project:

  Creating a smart wheel chair that will capture the patient's eye motions to move wheel chair where he or she looks without the need of using hand gestures.

Requirements:

  There will be cameras attached to the wheel chair to sense the patient's eye movements. Patient will not need to wear a helmet.

I am assigned to implement adaBoost algorithm for openCV. It is a supervised classification method that combines the performance of many week classifiers instead of a monolithic strong classifier such as SVM or Neural Network. Decision Trees are most popular week classifiers for Adaboost (Adaptive Boosting). Decision Tree means a binary tree (each non-leaf node has 2 child) and for classification, each leaf is marked with a class label. Multiple leaves may have the same label.

  Adaboost Model: 
 
  Y = F(X)
          
  X(i) ∈ R(k), Y(i) ∈ -1, +1

           k = component vector. Each component encodes a vector.

  Boosting Types:

  1) Two-class Discrete Adaboost (what I will implement)

  2) Real Adaboost

  3) LogitBoost

  4) Gentle Adaboost