Download Image Processing and Communications Challenges 7 by Ryszard S. Choraś PDF

By Ryszard S. Choraś

This publication comprises papers authorized for IP&C 2015, the foreign convention on snapshot Processing and Communications, held at UTP college of technology and know-how, Bydgoszcz, Poland, September Sep 11, 2015. This convention was once the 8th variation within the IP&C sequence of annual meetings. This booklet and the convention have the purpose to assemble researchers and scientists within the huge fields of photo processing and communications, addressing contemporary advances in concept, technique and functions. The e-book could be of curiosity to a wide staff of researchers, engineers and practitioners in photo processing and communications.

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Image Vis. Comput. 24(6), 564–572 (2006) 4. : An extended set of haar-like features for rapid object detection. IEEE ICIP 2002, 900–903 (2002) 5. : On training cascade face detectors. In: Proceedings of Image and Vision Computing New Zealand, pp. 239–244 (2003) 6. : Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1686 (1998) 7. : Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004) 8. : Fast asymmetric learning for cascade face detection.

G. face (positive examples) and without it. The asymmetric classification error rates are assumed: small for positive examples (false negative rate) and big for negative examples (false positive rate). It is desirable to reject non-objects (non-faces) regions as quick as possible but leave almost 100 % of true positive examples. The final false positive error rate can be estimated as a product of all layer’s false positive error rates because the succeeding layers are independent and are trained without examples rejected in previous layers.

1, the position of corner points before and after the geometric transform of rotation is shown. Cross mark indicates the corner found on original image (a) and the same corner transformed to coordinates of rotated image (b). Circle mark represents corners found on transformed image (b). Let P be a set of corner points on image I : P = { p1 , p2 , . . , pn }, where n is the number of points detected on image I . Let T A is the operator of geometric transformation with parameters described by the matrix A, and P is set of corner points transformed by T A : Fig.

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