Image analysis – Image transformation or preprocessing – Changing the image coordinates
Reexamination Certificate
2007-09-25
2007-09-25
Bella, Matthew C. (Department: 2624)
Image analysis
Image transformation or preprocessing
Changing the image coordinates
C382S289000
Reexamination Certificate
active
10712077
ABSTRACT:
In methods, systems, and computer program products for locating a regularly configured object within a digital image, a plurality of primary rotated integral images of the digital image are computed. Each primary rotated integral image has a different in-plane rotation. A set of secondary rotated integral images are derived from each of the primary rotated integral images. The secondary rotated integral images have further in-plane rotations relative to the respective primary rotated integral image. A window is defined within the digital image and corresponding windows of the rotated integral images. The values of convolution sums of a predetermined set of feature boxes within the window, in each of the rotated integral images are extracted. The dimensionality of the convolution sums is reduced to provide a set of reduced sums. A probability model is applied to the reduced sums to provide a best estimated derotated image of the window.
REFERENCES:
“Face Detection Using the 1st -Order RCE Classifier”, Proc. IEEE Int'l Conf. Image Processing, Jeon, B., Lee, S., and Lee, K., 2002.
“Face Detection Using the 1st -Order RCE Classifier”,Proc. IEEE Int'l Conf. Image Processing, Jeon, B., Lee, S., and Lee, K., 2002.
“Rotation Invariant Neural Network-Based Face Detection”,Proc. IEEE Conf. Computer Vision and Pattern Recognition, Rowley, H., Baluja, S., and Kanade, T., 1998, pp. 38-44.
“Robust Real-Time Object Recognition”,Proc. Second International Workshop on Statistical and Computational Theories of Vision—Modeling, Learning, Computing, and Sampling, Viola, P. and Jones, M., 2001.
Feraud, Raphael, et al., “A Fast and Accurate Face Detector Based On Neural Networks”,IEEE Trans. Pattern Analysis and Machine Intelligence, 23(1), pp. 42-53.
Schneiderman, H., and Kanade, T., “Probabilities Modeling of Local Appearance and Spatial Relationships for Object Recognition”,Proc. CVPR 1998, pp. 45-51.
Pudil, P., Novovicova, J, and Kitller, J., “Floating search methods in feature selection”,Pattern Recognition Letters 15, pp. 1119-1125.
Bella Matthew C.
Cunningham G. F.
Walker Robert Luke
LandOfFree
In-plane rotation invariant object detection in digitized... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with In-plane rotation invariant object detection in digitized..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and In-plane rotation invariant object detection in digitized... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3760548