CS 6723 Image Processing Fall 2001

This course covers the fundamentals of digital image processing from both algorithmic and implementation perspectives. Matlab and the Matlab Imaging Processing Toolbox will be integrated throughout the course to enable students to do fast prototyping and display. In addition to the standard images from the image processing literature, biological images will be used to test some the approaches and to illustrate the difficulties that are typically encountered in real problems.

• Introduction: What is image processing? What are the fundamental issues? What is the role of perception? Matlab orientation.

• Basic image models and definitions: pixels, sampling, quantization, resolution, representation as a matrix, operations, camera angles and perspective transformations

• Image transformations: Fourier transform and spectral analysis, separable transformations, principal component analysis, wavelets.

• Image enhancement: histograms, subtraction, averaging, spatial filtering and sharpening, lowpass and highpass filtering.

• Image restoration: brief overview and models

• Image compression: fundamental principles, compression models, variable-length coding, predictive coding, JPEG and GIF standards.

• Image segmentation: line and edge detection, boundary detection, thresholding, region-oriented approaches.

• Topological approaches: representations of boundaries and regions, morphology.

• Image recognition: statistical classifiers, neural network approaches.

• Advanced topics and student presentations