Read DigitalImageProcessingSyllabus.pdf text version

ECE 6532 Digital Image Processing

Fall Semester 2010 Instructor: Tolga Tasdizen

Course Description: Digital image processing is a subfield of digital signal processing that is concerned with a particular class of signals: two- and three- dimensional digital images. The field of digital image processing was born in the 1960s and proliferated in the 1970s as cheaper computers became available. Today, digital images can come from a wide variety of sources including digital cameras, scanners, satellites, biometric scanners, microscopes, and clinical imaging modalities such as Computer Tomography (CT). With these developments image processing has become widespread in a diverse range of fields including security, remote sensing, healthcare and biomedical research. Digital image processing is preferred over analog processing because if allows the use of more complex methods for a wide range of tasks such as noise reduction, restoration, compression, feature extraction and pattern recognition. Course Objectives: By the end of this semester you should · understand the basic principles and methods of digital image processing, · be able to formulate solutions to general image processing problems, · have a comprehensive background in image filtering, · be prepared for research in image processing if you choose to go that way. Topics Overview: · Introduction: Image processing applications, light and the human visual system, image sensors, displays, sampling and quantization. · Point operations: Intensity transforms, intensity histograms, histogram equalization and matching. · Basic spatial-domain filtering: Linear filtering and convolution in two dimensions. · Advanced spatial-domain filtering: Nonlinear filtering, bilateral filter, partial differential equation based filtering, neighborhood based filtering. · Frequency-domain filtering: Continuous and discrete Fourier transform in two dimensions, sampling/aliasing, image smoothing and sharpening. · Image restoration: Image noise and degradation models, Wiener filtering. · Wavelet transforms: Image pyramids, multiresolution expansion, wavelet transforms in one and two dimensions. · Morphological processing: Erosion, dilation, opening, closing, boundary extraction, thinning and skeletons. · Edge detection: Edge models, simple edge detection, Canny edge detection algorithm. · Segmentation: Thresholding, region-based segmentation, watershed segmentation algorithm. Depending on student interests and time we might also cover one of the following topics: · Image reconstruction: Computed Tomography (CT), reconstruction from projections, Radon Transform, Fourier slice theorem, Filtered backprojection. · Image compression: Coding methods including Huffman, arithmetic, LZW, run-length, Block transforms and Wavelets.

Teaching and grading: · Lectures: Wednesday and Friday 11:50 am - 1:10 pm, WEB 1450 · Instructor: Tolga Tasdizen ­ Email: [email protected] ­ Office: Warnock Engineering Building (WEB) 3887 ­ Phone: 581-3539 · Office Hours: To be arranged. Also by appointment. · Textbook: Digital Image Processing by Rafael Gonzalez and Richard Woods, 3rd edition, Publisher: Pearson Prentice Hall · Course web page: www.sci.utah.edu/tolga/ece6532/ · Prerequisites: ECE 5530 Digital Signal Processing or equivalent and basic knowledge of MATLAB. A background in probability will also be very helpful. · Grading: Assignments (50%), midterms (2×10%), project (25%) and classroom participation (5%) · Classroom participation: Students should come to the lectures prepared and participate in discussions. Assignments: · All assignments are due at the start of class on the due date. This also applies to assignments being submitted by email. Students are required to follow e-mail correspondence instructions below. A reply by the instructor to the student will acknowledge receipt of the assignment. If you don't get a reply email within 24 hours, check with me. Late assignments will not receive full points in fairness to other students unless approved by the instructor prior to the deadline. Email correspondence: · I will send important notices to the entire class such as corrections to assignment problems via email using the CIS class mailer. This email goes to your utah.edu address by default. Make sure you receive and regularly (daily) read email from this address or have it forwarded to an address that you read. · If you email me, I will strive to answer it in a timely manner (within 24 hours not including holidays and weekends). However, please do not expect a reply within the next hour. While I might sometimes reply that fast, there is no guarantee. Academic Honesty: · Copying someone else's work on an exam is considered cheating. · Copying someone else's work on a homework is considered cheating. Copying from a webpage or other source without giving a reference is considered plagiarism which is a form of cheating. You are encouraged to discuss homework problems with each other, but the written work you turn in must be original and your own. Equal Access: The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. If you will need accommodations in this class, reasonable prior notice needs to be given to the instructor and to the Center for Disability Services, 162 Olpin Union Building, 581-5020 (V/TDD) to make arrangements for accommodations.

Information

2 pages

Report File (DMCA)

Our content is added by our users. We aim to remove reported files within 1 working day. Please use this link to notify us:

Report this file as copyright or inappropriate

255246