|
Copyright notice
|
tarix | 17.11.2018 | ölçüsü | 7,46 Mb. | | #80060 |
|
Copyright notice The slides can be used freely for non-profit education provided that the source is appropriately cited. Please report any usage on a regular basis (namely in university courses) to the authors. For commercial usage ask the authors for permission. © Jan Flusser, Tomas Suk, and Barbara Zitová, 2008
Traffic surveillance - can we recognize the license plates?
Recognition (classification) = assigning a pattern/object to one of pre-defined classes Recognition (classification) = assigning a pattern/object to one of pre-defined classes Features – measurable quantities, usually form an n-D vector in a metric space
Non-ideal imaging conditions degradation of the image g = D(f)
D - unknown degradation operator
Základní přístupy Brute force Normalized position inverse problem
What are invariants? Invariants are functionals defined on the image space such that I(f) = I(D(f)) for all admissible D
What are invariants? Invariants are functionals defined on the image space such that I(f) = I(D(f)) for all admissible D I(f1), I(f2) “different enough“ for different f1, f2
What are moment invariants? Functions of image moments, invariant to certain class of image degradations Rotation, translation, scaling Affine transform Elastic deformations Convolution/blurring Combined invariants
What are moments? Moments are “projections” of the image function into a polynomial basis
The most common moments
Geometric moments – the meaning
Uniqueness theorem
Invariants to translation
Invariants to translation and scaling
Invariants to translation and scaling
Invariants to translation and scaling
Dostları ilə paylaş: |
|
|