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