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Building Recognition

Building Recognition. Landry Huet Sung Hee Park DW Wheeler. Problem Statement. Identify Stanford buildings from photos 16 buildings Database of 300 pictures Fast enough to implement real time system. SIFT descriptor. Img #. Bldg. color histogram. Image database. Feature database.

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Building Recognition

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  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. SIFT descriptor Img # Bldg color histogram Image database Feature database Ransac Skilling Bldg name List of SIFT descriptors color histogram Image descriptor Feature descriptor Project Outline 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. Approach and Results • 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

  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

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