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  • 1. Semantic Geometric Features: A Preliminary Investigation of Automobile Identification Carl E. Abrams Sung-Hyuk Cha, Michael Gargano, and Charles Tappert
  • 2. Agenda
    • Overview of the Problem
    • The Experiments
    • Results
    • Going Forward
  • 3. Overview
    • Object recognition remains a hard problem
    • The human mind uses shapes to recognize objects
    • Can semantic features defined by their shapes be more effective in the recognition and identification of objects?
  • 4. The Experiments
    • 10 test images of cars
    • Directly form the manufactures websites
    • Images were restricted to side views of the cars taken from 90 degrees
    • All 2005 models
    • Feature vectors calculated/measured from the images
  • 5. The Vehicles
  • 6. Experiments used Euclidean Distance as the Measure the xi and ti are measurements from two different vehicles
  • 7. Experiments used Euclidean Distance as the Measure (x1,y1) (x2,y2) c = (a 2 +b 2 ) 1/2 c = ((x1-x2) 2 +(y1-y2) 2 ) 1/2 c a b
  • 8. Manufacturers Specifications First Experiment
  • 9. Boundary Description using Rays Second Experiment
  • 10. Semantic Features Third Experiment
  • 11. Challenge: Determine the qualitative ability of the feature vectors to separate the vehicles
    • Within each experiment compute the distance of each vehicle from all the others
    • Evenly divide the measures into 5 bins
    • Observe the distribution of the measures
  • 12. The Results
  • 13. Distance Matrix – Semantic Features Honda Civic Honda Accord Mazda 3 Mazda 6 Porsche Carerra Toyota Camry Toyota Celica Toyota Corolla Toyota Echo VW Passat Honda Civic Honda Accord Mazda 3 Mazda 6 Porsche Carerra Toyota Camry Toyota Celica Toyota Corolla Toyota Echo VW Passat 0 154.01 10.48 156.53 13.34 162.50 5.00 7.55 4.12 156.23 154.01 0 148.33 28.00 151.63 51.07 151.06 150.09 154.01 26.07 10.48 148.33 0 152.24 5.83 159.84 6.55 9.43 13,45 151.90 156.53 28.00 152.24 0 156.36 27.58 154.36 153.60 156.36 2.82 13.34 151.63 5.83 156.36 0 164.35 9.43 11.44 16.09 155.96 162.50 51.08 159.84 27.58 164/35 0 160.89 159.77 161.73 27.65 5.00 151.06 6.55 154.37 9.43 160.89 0 4.24 7.07 154.00 7.55 150.09 9.43 153.60 11.44 159.78 4.24 0 7.21 153.17 4.12 154.01 13.45 156.36 16.09 161.72 7.07 7.21 0 156.02 156.23 26.07 151.90 2.82 155.96 27.66 154.00 153.17 156.02 0
  • 14. Going Forward
    • Extend techniques to encompass semantic shapes within an object (shape contexts)
    • Compare the extended semantic methods to existing methods in multiple domains
  • 15. Going Forward Shape Contexts
  • 16. References
    • [1] R. D. Acqua and R. Job, "Is global shape sufficient for automatic object identification?" Congitive Science , vol. 8, pp. 801-821, 2001.
    • [2] A. K. Jain, A. Ross, and S. Pankanti, "A Prototype Hand Geomtery-based Verification System," presented at Proceedings of 2nd International conference on Audio and Video-based Biometric Person Authentication, Wahington D.C., 1999.
    • [3] H. Schneiderman and T. Kanade, "A Statistical Model for 3D Object Detection Applied to Faces and Cars," presented at IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2000
    • [4] S. Belongie,J Malik, J Puzicha, “Matching Shapes” ,presented at the International Conference on Computer Vision (ICCV 01) Vol 1, Jan 2001

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