Lecture: TuTh 12:30pm - 1:45pm @ CSIC 1122

Office Hours: By appointment or before class @ AVW 4475

TA: Peratham Wiriyathammabhum (peratham [at] cs |dot| umd |dot| edu)

Office Hours: Tu 4:00pm-6:00pm @ AVW 4103 (or AVW 4470)

Piazza Link: http://piazza.com/umd/spring2016/cmsc426/home

Acknowledgement: Special thanks to Dr. Cornelia Fermüller

for providing most of the lecture materials for this class!! (link)

- (May 10
^{th}) - Check out the final exam. It is a take-home exam. - (Apr 23
^{rd}) - Check out the 5^{th}and the 6^{th}homework at the homework section. - (Apr 14
^{th}) - Check out the 4^{th}homework at the homework section. - (Apr 13
^{th}) - Check out the 3^{rd}homework at the homework section. - (Mar 27
^{th}) - The midterm is posted!! The password will be distributed in class. - (Mar 4
^{th}) - Check out the 2^{nd}homework at the homework section. - (Feb 10
^{th}) - Check out the 1^{st}homework at the homework section. Also, the grading policy has been updated. Check the outline section. - (Feb 5
^{th}) - Join this class piazza at http://piazza.com/umd/spring2016/cmsc426 - (Feb 4
^{th}) - Check out the MATLAB image filtering tutorial. - (Jan 25
^{th}) - Welcome to CMSC 426!! Checkout the MATLAB inclass tutorials in the resources section!!! The university provides MATLAB via https://terpware.umd.edu/.

- Introduction:

What is Computer Vision? Ongoing Research and Application Areas. - Image Formation:

Geometric aspects, Radiometric Aspects, Digital Images, The Human Eye, Camera parameters. - Filters:

Linar Filters and Convolution, Spatial Frequency and Fourier Transform, Sampling and Aliasing, Noise Reduction small. - Edge Detection:

Gradient based edge Detectors, Laplacian, Parametric Models. - Other Image Features:

Hough Transform, Ellipse fitting, Deformable contours. - Lightness and Color:

Surface Reflectance, Recovering Lightness, The Physics of Color, Human Color Perception, Color Representations. - Camera Calibration :

Intrinsic Parameters, Extrinsic Parameters. - Multiple View Geometry:

Stereo, The Correspondence Problem, Epipolar Geometry, 3D Reconstruction. - Motion:

The Image Motion Field, Estimation of 3D Motion and Structure, Segmentation on the basis of different Motion, Image Compression. - Shape from Single Image Cues:

Surface Descriptions, Shape from Contours, Shape from Shading, Shape from Texture.

There is no required text. We will distribute material from a variety of sources.

**Grading Policy: There will be six assignments/projects in the class accounting for 60% of the grade. There will be a midterm and a final exam each accounting for 20% of the grade. Assignments handed in by the deadline will receive a 5% bonus.**

- Lecture 1 - Jan 28
^{th}: Correlation and Convolution [pdf] - Lecture 2 - Feb 2
^{nd}: Histogram Equalization [pdf] - Lecture 3 - Feb 4
^{th}: Image Formation 1 [pdf] [pdf] [ppt] - Lecture 4 - Feb 9
^{th}: Projective Geometry [ppt] (10 MB) - Lecture 5 - Feb 11
^{th}: Linear Algebra Review [ppt] - Lecture 6 - Feb 16
^{th}: Camera Calibration [pdf] - Lecture 7 - Feb 18
^{th}: Filtering [ppt] - Lecture 8 - Feb 23
^{th}: Edge detection [ppt] - Lecture 9 - Feb 25
^{th}: Resampling [ppt] (Slides from Univ. of Washington) - Lecture 10 - Mar 1
^{st}: Image motion [ppt] - Lecture 11 - Mar 3
^{rd}: Statistics on image features: Review of statistical concepts [ppt] [Website on illusions] - Lecture 12 - Mar 8
^{th}: Stereopsis [ppt] - Lecture 13 - Mar 10
^{th}: Epipolar Geometry [ppt] (8 MB) - Lecture 14 - Mar 15
^{th}: Spring Break - Lecture 15 - Mar 17
^{th}: Spring Break - Lecture 16 - Mar 22
^{nd}: Interpretation of image motion fields [ppt] - Lecture 17 - Mar 24
^{th}: 3D motion estimation from image derivatives [ppt] - Lecture 18 - Mar 29
^{th}: Midterm - Lecture 19 - Mar 31
^{st}: Classification with SVMs and Deep Neural Networks [piazza] (Many slides are from Univ. of Toronto) - Lecture 20 - Apr 5
^{th}: Classification with SVMs and Deep Neural Networks (con't) [MatConvNet Quick Demo] - Lecture 21 - Apr 7
^{th}: Shape from Shading [pdf] (from Daniel DeMenthon) - Lecture 22 - Apr 12
^{th}: Texture [ppt] (5.5 MB) - Lecture 23 - Apr 14
^{th}: Tracking with Kalman Filters [pdf] (from Daniel DeMenthon) - Lecture 24 - Apr 19
^{th}: Homework Recitation - Lecture 25 - Apr 21
^{th}: Perception for Robots 1 [pdf] - Lecture 26 - Apr 26
^{th}: Vision & Language Integration with Cognitive Dialogue [pdf] - Lecture 27 - Apr 28
^{th}: Active Perception [pdf] - Lecture 28 - May 3
^{rd}: Finalize - Lecture 29 - May 5
^{th}: Finalize - Lecture 30 - May 10
^{th}: Final & Closing

- Homework 1 (Due Feb 25
^{th}): Projective Geometry and Histogram Equalization [pdf] - Homework 2 (Due Mar 22
^{nd}): Stereopsis and Epipolar Geometry [pdf] - Homework 3 (Due Apr 21
^{nd}): Background Subtraction [pdf] [data] - Homework 4 (Due Apr 21
^{nd}): Texture Synthesis [pdf] - Homework 5 (Due May 10
^{th}): Bag of Visual Words [pdf] [skeleton code] - Homework 6 (Due May 10
^{th}): Review for Final Exam [pdf]

Suggested book for reference: "Multiple View geometry in Computer Vision" by Hartley and Zisserman

Online resource of computer vision topics (contains short descriptions and tutorials on basic and advanced topics)

Image Processing Learning resources

Some slides for using MATLAB in Image Processing from ETH Zurich

MATLAB tutorial from University of Toronto