Multidimensional Digital Signal Processing

ECG782 Fall 2026

Description

Professor:
Dr. Brendan Morris,
SEB 3216
OH: TuTh 12:00-13:00

Lecture: TuTh 13:00-14:15, AEB 140
Final: Tu 12/08 13:00-15:00
Look up your final exam schedule now to determine conflicts.

Textbook

Digital Image Processing, 3rd Edition, Gonzalez and Woods, ISBN: 978031687288.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools and Techniques to Build Intelligent Systems, 2nd Edition, Geron, ISBN: 9781492032649.

Recommended Text

Image Processing, Analysis, and Machine Vision, 4th Edition, Sonka, Hlavac, and Boyle, ISBN: 978-1-133-59360-7 Available online [link]
Computer Vision: Algorithms and Applications, Richard Szeliski. Available online [link]

Catalog Description:

Theory and applications of multidimensional (M-D) digital signal processing. M-D signals and systems. M-D z-transform. M-D DFT and FFT. Design and implementation of M-D FIR and IIR filters. Applications to image processing such as image enhancement and restoration. Advanced topics chosen according to class interests.
Prerequisites: None. Expect DSP/Signals experience

Course Syllabus: [pdf]

Grading

ComponentPercentageDate
Midterm: 20% 10/14
Final: 20% 12/09
Project: 25% 12/13
Homework: 20% Weekly
Presentation: 5% -
Participation: 10% Weekly

Students may work together in study groups but all assignments must be completed individually. Homework will be due via Webcampus (Canvas) on the designated date. No late homework will be accepted unless prior notification and arrangements are made. Be sure you have a tool to scan or make pdf images of your work. The course will have a final computer vision term project. You will be required to submit a project report in the form of a conference styled manuscript and make a presentation.

Gradebook

The gradebook is available through UNLV Webcampus [link].

Schedule (Tentative)

WeekDateLecture TopicReadingAssignment
1 08/25 Tu class01: Course Info [pdf] Intro [pdf][Zoom] GW Ch 1 [pdf], 2 [pdf] HW00 [pdf]
Due 8/30
Latex template [tex][pdf]
08/27 Th class02: Image Fundamentals [pdf][Zoom]
2 09/01 Tu class03: Spatial Filtering [pdf] GW Ch 3 [pdf] HW01 [pdf]
Due Su 9/06
09/03 Th class04: Spatial Filtering
3 09/08 Tu class05: Color IP [pdf] Morphology [pdf] GW Ch 6 [pdf], 9 [pdf] HW02 [pdf]
Due Su 9/13
09/10 Th class06: Frequency Domain Filtering [pdf]
4 09/15 Tu class07: Frequency Domain Filtering [pdf] GW Ch 4 [pdf]
GW Ch 10 [pdf]		 HW03 [pdf]
Due Su 9/20
09/17 Th class08: Segmentation [pdf]
5 09/22 Tu class09: Segmentation Szeliski Ch 8
Sonka Ch 16 [pdf]		 HW04 [pdf]
Due Su 9/27
09/24 Th class10: Motion [pdf]
6 09/29 Tu class11: Keypoints [pdf] Szeliski Ch 4 [pdf]
[Stauffer and Grimson 1999] [pdf]		 HW05 [pdf]
Due Su 10/04
10/01 Th class12: Mixture of Gaussian Background Model [pdf]
7 10/06 Tu class13: Project Introduction [pdf] Project Description [pdf]
Project Examples [pdf]
10/08 Th class14: Midterm Review
8 10/13 Tu class15: Midterm Sonka Ch 9 [pdf]
Szeliski 2e Ch 6
10/15 Th class16: Object Recognition [pdf]
9 10/20 Tu class17: Viola-Jones Detector [pdf] [Viola Jones 2001] [pdf]
Geron Ch 1 [pdf]
10/22 Th class18: Machine Learning (ML) Overview [pdf]
10 10/27 Tu class19: Artificial Neural Network (ANN) Overview [pdf] Geron Ch 10 [pdf]
Geron Ch 14 [pdf]		 HW06
Due Su 11/01
10/29 Th class20: Deep Computer Vision Using CNNs [pdf]
11 11/03 Tu class21: [Election Day] CNNs
11/05 Th class22: No Class
12 11/10 Tu class23: Veterans Day Deep Recognition [pdf] HW07
Due Su 11/15
11/12 Th class24: Deep Recognition [pdf]
13 11/17 Tu class25: Segmentation Attention+Transformers [pdf]
11/19 Th class26: Attention and Transformers [pdf]
14 11/24 Tu class27: No Class
11/26 Th class28: Thanksgiving
15 12/01 Tu class29: Project Presentations
12/03 Th class30: Project Presentations
16 12/08 Tu Final
-
12/10 Th Freedom