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Image Based Recognition and Classification: Schedule & Associated Material

Lectures

The order of the lectures may change.

Date	Time / Venue	Topic	Lecture	Exercises	Extra Material
Mon, Mar 19	10:00-12:00 B2	Image Basics	Lecture1_part1.pdf Lecture1_part2.pdf	Exercises1.pdf montage.m StartImages.tar	Paper referenced in exercise Causes of colour
Tue, Mar 20	10:00-12:00 K2	Image Feature Extraction I	Lecture2.pdf	Exercises2.pdf EyeNoseImages.tar.gz	Chapter 13 of Simon Prince book
Thur, Mar 22	15:00-17:00 E2	Probability and Statistics	Lecture3.pdf	Exercises3.pdf	Chapter 1 of the Simon Prince book Chapter 5 of the Simon Prince book
Tue, Mar 27	10:00-12:00 E2	Decision Theory	Lecture4.pdf	Exercises4.pdf BackgroundImages.tar StudentImages.tar FindBiggestComp.m
Thur, Mar 29	15:00-17:00 E2	Non-parametric classifiers	Lecture5.pdf	Exercises5.pdf	Chapter 13 (section 13.3) of The Elements of Statistical Learning book covers the nearest neighbour classifier.
Thur, Apr 12	15:00-17:00, E2	Discriminative learning	Lecture6.pdf	Exercises6.pdf DigitImages.tar	Chapter 4 of ESL book covers many of the linear classification methods introduced in today's lecture. Section 8.1 of Simon Prince book also reviews logistc regression.
Fri, Apr 13	13:00-15:00 E3	Image Feature Extraction II/PCA	Lecture7.pdf	Exercises7.pdf frontface_dataset.tar.gz	Shlens' PCA tutorial
Mon, Apr 16	10:00-12:00 K2	Boosting	Lecture8.pdf	Exercises8.pdf	Sections 10.1 - 10.6 of ESL book cover the boosting algorithm in more detail and gives its explanation based on greedily minimizing the exponential loss. Robert Schapire's Video lecture on boosting , Robert Schapire's boosting tutorial , Viola and Jones face detection paper
Tor, Apr 19	15:00-17:00 E2	Exercise Class	ExerciseClass1.pdf	AnswersClass1.pdf
Wed, Apr 25	10:00-12:00 K2	VC dimension / SVMs I	Lecture9.pdf KKT.pdf Duality.pdf	Exercises9.pdf
Thur, Apr 26	15:00-17:00 E3	SVMs II / Kernel Trick	Lecture10.pdf	Exercises10.pdf
Wed, May 2	10:00-12:00 B2	Unsupervised Learning	Lecture11.pdf EM.pdf	Exercises11.pdf
Tue, May 8	15:00-17:00 E2	Exercise class	ExerciseClass2.pdf	AnswersClass2.pdf

Reference Books

• Much of the material covered in this course is described in more detail in the book Computer Vision: Models, Learning, and Inference by Simon Prince. It is not for sale yet. However, an electronic version is available for downloading from the linked site.
• Another interesing book available on the web is The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman. This book focuses on machine learning as opposed to machine learning for computer vision. It is quite a long book but many parts of it are quite readable.

My record of your homework completion

When the course starts I will keep my record of your homework completion here.

This record will be updated on a weekly basis, so please show some patience! But, do let me know if you think errors are present. Remember you need to complete 9 exercise homeworks to pass this part of the course.

This is my record of your Homeworks completed. This record will be updated on a weekly basis, so please show some patience! But, do let me know if you think errors are present. Remember you need to complete 9 exercise homeworks to pass this part of the course.

Labs

All labs will be held in 4V4Gul.

Date	Time	Download Material
Fri, Apr 20	09:00-12:00	Manual.pdf DebugInfo.tar.gz CheckPoints.pdf TrainingImages.tar.gz TestImages.tar.gz
Fri, Apr 27	09:00-12:00
Fri, May 4	09:00-12:00	Here are face images and bounding boxes from previous years Students12_faces-small.tar and Students12_faces_bbs.txt Students11_faces-small.tar and Students11_face_bbs.txt StudentImages2010.tar and Students2010_face_bbs.txt The ground truth is in the format: filename topleft_xpos topleft_ypos width height
Fri, May 11	09:00-12:00	Here is a potential template for your poster PosterTemplate.pdf
Thurs, May 24	10:00-12:00	POSTER SESSION on the 5th floor of Teknikringen 14

Setting data and time for the Poster session

When the course starts we will schedule a time for the lab poster session.

My record of your homework completion

When the course starts I will keep my record of your lab completion here. This is my record of your Lab Tasks completed. Let me know if you think I have an error in my records.