Office Hours:

M W 10:00 AM -11:30 AM and by appointment

Course Description:

This is a graduate-level/advanced undergraduate seminar exploring the algorithms and state- of-the-art techniques in the field of computer vision and image processing. Topics covered in the first half of the seminar include image processing, pattern recognition, feature detection, image and object segmentation, computational photography, object detection, and face recognition. In the second half of the semester, students will read and lead discussions on both classic and recent literature in computer vision, machine learning, and related fields. A familiarity with linear algebra and statistical methods is recommended but not required.

Course Schedule

*lecture notes come from a variety of sources including Mubarak Shah UCF, Robert Collins PSU, Duda et al Pattern Recognition, Victor Lavrenko, Richard Szeliski Computer Vision, Fei Fei Li Stanford, etc.

DateClass SummaryExamples and CodeHomework
8/24/2016 Introduction to what is computer vision. Read Chapters 1 in Computer Vision.
8/31/2016 Linear Algebra refresher. Image formation and color spaces. [slides] . Matlab handout . Matlab book chapter . Linear algebra in 4 pages Read Chapters 2.1.1 - 2D points, 2D lines, 3D pints, 3D planes.
2.1.2 - 2D transformations, 3D transformations.
2.3.2 Color.
3.1, 3.2 Point operations and Linear filtering in Computer vision.
. Read the Color transfer paper. Color Transfer paper
9/7/2016 Matlab Tutorial. Image formation, Image filtering [slides] Read Chapters 3.3 - More neighborhood operators Homework 1 . Image 1. Image 2. Project 1 . Color image 1 . Color image 2
9/14/2016 Edges, Derivative of Gaussian, Laplacian, Sobel, Prewitt, Canny, Seam Carving [slides] Read 4.1 and 4.2 in the book. Read the seam carving paper Seam Carving . Homework 2 . Image 1. Image 2.
9/21/2016 Interest points, eigenvalue/eigenvector, Harris corners [slides] Read 4.1 in the book.
9/28/2016 SIFT Interest points, Hessian, image descriptors, image pyramids, difference of gaussians, Scale Space [slides] Review 4.1 in the book. Read the paper on SIFT. Homework 3 . Puzzle . Piece 1 . Piece 2
10/5/2016 Projections, covariance, dimensionality reduction, PCA, LDA, eigenfaces [slides] Read 14.2.1 in the book. Read the paper on Eigenfaces. Project 2
10/12/2016 Fall Break
10/19/2016 Midterm Review Sheet
10/26/2016 Classification, linear regression, Boosting [slides] Lab 4. Lab5 . Presentation instructions
11/2/2016 Classification, Bag of Visual Words, Random Forest , Neural Networks Batches Meta . Data30k.mat . Labels30k.mat. Mdl.mat. [slides] Lab 6 . Final Project Description
11/9/2016 Introduction to Deep Learning, Backpropogation, Sigmoid, ReLU [slides] . Deep Learning Matlab Demo Extra Credit . Captchas . Numbers
11/16/2016 Hidden Layers, Softmax, Convolutional Neural Networks, Pooling, Autoencoders, Fine-tuning, transfer learning, multimodal learning, RNN, LSTMs [slides] . Using CNNs as a feature extractor Lab 8
11/23/2016 No Class - Thanksgiving
11/30/2016 Research paper presentations
12/7/2016 No in-class, final project workday
12/14/2016 Final Project Presentations Final Project presentations will be in the ICE center starting after the CSC Senior Project presentations.