Pace DPS Semantic Geometric Features: A Preliminary Investigation of Automobile Identification Carl E. Abrams Sung-Hyuk Cha, Michael Gargano, and Charles Tappert

Pace DPS Agenda Overview of the Problem The Experiments Results Going Forward

Pace DPS 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?

Pace DPS 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

Pace DPS The Vehicles

Pace DPS Experiments used Euclidean Distance as the Measure the xi and ti are measurements from two different vehicles

Pace DPS Experiments used Euclidean Distance as the Measure (x1,y1) (x2,y2) c a b c = (a 2 +b 2 ) 1/2 c = ((x1-x2) 2 +(y1-y2) 2 ) 1/2

Pace DPS Manufacturers Specifications First Experiment

Pace DPS Boundary Description using Rays Second Experiment

Pace DPS Semantic Features Third Experiment

Pace DPS 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

Pace DPS The Results

Pace DPS Distance Matrix – Semantic Features / , Honda Civic Honda Accord Mazda 3Mazda 6Porsche 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

Pace DPS Going Forward Extend techniques to encompass semantic shapes within an object (shape contexts) Compare the extended semantic methods to existing methods in multiple domains

Pace DPS Going Forward Shape Contexts

Pace DPS References [1]R. D. Acqua and R. Job, "Is global shape sufficient for automatic object identification?" Congitive Science, vol. 8, pp , [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., [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