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Building Recognition
Landry HuetSung Hee ParkDW Wheeler
Problem Statement
• Identify Stanford buildings from photos– 16 buildings– Database of 300 pictures
• Fast enough to implement real time system
Project Outline
colorhistogram
List ofSIFT
descriptors
Bldg name
Image descriptor
colorhistogram
Feature descriptor
Img #SIFT
descriptorBldg
Featuredatabase
Imagedatabase
Ransac
Skilling
1. Color histogram matching2. SIFT feature matching3. Image-by-image comparison
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
• 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
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