SPIE Conference on Real-Time Image and Video Processing April 16, 2010 - Brussels M. George a , N. Kehtarnavaz a , M. Rahman a , M. Carlsohn b a Signal and Image Processing Lab, University of Texas at Dallas b Engineering and Consultancy for Computer Vision and Image Communication, Bremen, Germany This work has been partially supported by the Wireless Terminal Business Unit of Texas Instruments.
Color is a very effective feature but has the problem of being dependent on the light source (color temperature) under which the image is taken. By using online color calibration, the dependency on the light source is adjusted on-the-fly; we previously introduced this online color calibration for face detection
M. Rahman, N. Kehtarnavaz, and Jianfeng Ren, “A Hybrid Face Detection Approach For Real-Time Depolyment On Mobile Devices,” Proceedings of IEEE International Conference on Image Processing (ICIP 2009), Cairo, Egypt, Nov. 2009.
K-means clustering is used to find the most prominent color cluster (black/white can be a dominant color too) in the SIFT detected logo area
Chrominance values modeled by a Gaussian Mixture Model (GMM)
Large color areas with high color probability are considered
Hu moment invariants (7 invariants) are then used to find the logo area by eliminating similar large color areas
Dominant color cluster in the Cb-Cr color space found on-the-fly and modeled by GMM
SIFT matching Dominant color image Detection after moment invariants
Moment invariants used to detect the logo among similar large color areas
Table 1. SIFT detection rates for the Samsung logo using different image sizes Table 2. Detection rates and times for different sample logos Table 3. Tracking results per frame with and without using median filtering. SIFT image size Total SIFT Points Number of Matches Detection Time (ms) Detection Rate (%) 160 x 120 115 17 727 98.9 320 x 480 163 34 1531 96.4 640 x 480 238 28 4138 100 Logo Total SIFT Points Number of Matches Detection Rate (%) Detection Time (ms) DHL 101 24 87.6 747 UTD 55 8 94.0 439 IEEE 45 9 98.4 430 Samsung 115 17 98.9 727 National Instruments 71 8 92.2 499 Logo Tracking Rate (%) With Filtering (%) Time (ms) DHL 84.2 98.1 56 UTD 87.6 98.6 47 IEEE 88.2 98.4 53 Samsung 95.9 99.8 55 National Instruments 94.7 99.6 46
A computationally efficient logo detection algorithm is developed by combining SIFT for initial detection (~700 ms) and online color based detection for subsequent frames (~50 ms) providing an average processing rate of 20 fps on PC platform
Ongoing work involves porting this algorithm to the OMAP mobile platform and its real-time implementation on this mobile platform