1. Building Recognition Landry Huet Sung Hee Park DW Wheeler

2. Problem Statement Identify Stanford buildings from photos –16 buildings –Database of 300 pictures Fast enough to implement real time system

3. Project Outline color histogram List of SIFT descriptors Bldg name Image descriptor color histogram Feature descriptor Img # SIFT descriptor Bldg Feature database Image database Ransac Skilling 1. Color histogram matching 2. SIFT feature matching 3. Image-by-image comparison

4. Approach and Results Timing speed-up –Find buildings in database that have similar color properties –Use kd-tree to find images with the most SIFT feature matches –Time reduced from 34 seconds to 22 seconds

5. Accuracy improvement –Distinguish buildings by both color information and SIFT features –Use HSV color representation and color normalization to be invariant to light conditions –Measure average error between inlier features using ransac algorithm Approach and Results

6. Work Distribution Landry Huet –Feature space search, kd- tree structure, photography Sung Hee Park –Database interface, SIFT matching, Ransac, vanishing points, photography DW Wheeler –Color histograms, photography