Introduction.
Fundamentals of digital image: image formation, acquisition and digitalisation. Binary representation, storage and visualisation of digital images.
Image characterisation.
Spatial domain processing: histograms, equalisation, image improvement. Spatial filtering.
Spectral domain processing: Fourier transforms. Filters. FFT. Convolution and correlation theorem.
Image recovery: degradation/recovery process model. Noise models. Deconvolution.
Colour processing: colour models.
Shape processing and segmentation: dilation, erosion.
Detection/extraction of characteristics. Hough transform. Domain growth.
Image reconstruction: data organization. Radon transform. Analytical and iterative methods. Reconstruction.
Other techniques: alignment and fusion. PCA. "Machine Learning".
Practical classes syllabus: use of programming languages for image processing and visualisation.

Livro de referência / main book:

R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2nd ed., 2001

Outros livros / Other books:

Rangaraj M R, Biomedical Image Analysis, CRC Press, 2005

R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing using Matlab, Prentice Hall, 2004

Anil J. Kain, Fundamentals of Digital Image Processing, Prentice Hall, 1989