1. WEEK4 RESEARCH Amari Lewis Aidean Sharghi

2. PREPARING THE DATASET  Cars – 83 samples  3 images for each sample when x=0  7 images for each sample when y=0  Buildings- 80 samples  3 images for each sample when x=0  7 images for each sample when y=0  1630

3. RUNNING EPI PROGRAM  Extracting the EPI for all categories  When y=0;  And when x=0;  The primary category is Cars and Buildings, the other EPIs from the other categories: Trees, Buses, and Bikes will be used as negative data (classification).

4. RUNNING HOG  Dense- Histogram of Oriented Gradients – type of feature descriptor  Extracted features from the EPI images to train a classifier  *Due to the images sizes, takes very long time up to 5hours.

6.  In order to compare our results, we ran HOG program on a.jpg image when x=0 and y=0 for each individual sample.  Extracted features

7. NEXT STEP…  Principal Component Analysis- run PCA  Apply Fischer kernel  Train classifier  Test data