Advanced Computer Vision Devi Parikh Electrical and Computer

Advanced Computer Vision Devi Parikh Electrical and Computer Engineering

Plan for today • Topic overview • Introductions • Course overview: – Logistics – Requirements • Please interrupt at any time with questions or comments

Computer Vision • Automatic understanding of images and video – Computing properties of the 3 D world from visual data (measurement) – Algorithms and representations to allow a machine to recognize objects, people, scenes, and activities. (perception and interpretation) – Algorithms to mine, search, and interact with visual data (search and organization) Kristen Grauman

What does recognition involve? Fei-Fei Li

Detection: are there people?

Activity: What are they doing?

Object categorization mountain tree building banner street lamp vendor people

Instance recognition Potala Palace A particular sign

Scene and context categorization • outdoor • city • …

Attribute recognition gray made of fabric crowded flat

Why recognition? • Recognition a fundamental part of perception – e. g. , robots, autonomous agents • Organize and give access to visual content – Connect to information – Detect trends and themes • Where are we now? Kristen Grauman

We’ve come a long way…

We’ve come a long way…

We’ve come a long way…

Posing visual queries Yeh et al. , MIT Belhumeur et al. Kristen Grauman Kooaba, Bay & Quack et al.

Exploring community photo collections Snavely et al. Kristen Grauman Simon & Seitz

Autonomous agents able to detect objects Kristen Grauman http: //www. darpa. mil/grandchallenge/gallery. asp

We’ve come a long way… Fischler and Elschlager, 1973

We’ve come a long way…

We’ve come a long way… Dollar et al. , BMVC 2009

Still a long way to go… Dollar et al. , BMVC 2009

Dollar et al. , BMVC 2009

Dollar et al. , BMVC 2009

Challenges

Challenges: robustness Illumination Occlusions Kristen Grauman Object pose Intra-class appearance Clutter Viewpoint

Challenges: context and human experience Context cues Kristen Grauman

Challenges: context and human experience Context cues Kristen Grauman Function Dynamics Video credit: J. Davis

Challenges: scale, efficiency • Half of the cerebral cortex in primates is devoted to processing visual information • ~20 hours of video added to You. Tube per minute • ~5, 000 new tagged photos added to Flickr per minute • Thousands to millions of pixels in an image • 30+ degrees of freedom in the pose of articulated objects (humans) • 3, 000 -30, 000 human recognizable object categories Kristen Grauman

Challenges: learning with minimal supervision More Less Un mu lab lti ele pl d, e ob je c ts Kristen Grauman Cl so asse me s clu labe tte led , r Cr pa opp lab rts a ed t ele nd o o d cla bje sse ct, s

Slide from Pietro Perona, 2004 Object Recognition workshop

Slide from Pietro Perona, 2004 Object Recognition workshop

What kinds of things work best today? Reading license plates, zip codes, checks Frontal face detection Recognizing flat, textured objects (like books, CD covers, posters) Fingerprint recognition Kristen Grauman

Inputs in 1963… L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph. D. thesis, MIT Department of Electrical Engineering, 1963. Kristen Grauman

… and inputs today Personal photo albums Surveillance and security Movies, news, sports Medical and scientific images Slide credit; L. Lazebnik

… and inputs today 350 mil. photos, 1 mil. added daily 1. 6 bil. images indexed as of summer 2005 916, 271 titles 10 mil. videos, 65, 000 added daily Understand organize and index all this data!! Images on the Web Satellite imagery Movies, news, sports City streets Slide credit; L. Lazebnik

Introductions • What is your name? • Which program are you in? How far along? • What is your research area and current project about? – Take a minute to explain it to us – In a way that we can all follow • Have you taken a computer vision course before? Machine learning or pattern recognition? • What are you hoping to get out of this class?

This course • • ECE 5984 TR 3: 30 pm to 4: 45 pm Hutcheson (HUTCH) 207 Office hours: by appointment (email) • Course webpage: http: //filebox. ece. vt. edu/~S 14 ECE 5984/ (Google me My homepage Teaching)

This course • Focus on current research in computer vision • High-level recognition problems, innovative applications.

Goals • • Understand state-of-the-art approaches Analyze and critique current approaches Identify interesting research questions Present clearly and methodically

Expectations • Discussions will center on recent papers in the field [15%] • Paper reviews each class [25%] – Can have 3 late days over the course of the semester • Presentations (2 -3 times) [25%] – Papers and background reading – Experiments • Project [35%] No “Assignments”, Exams, etc.

Prerequisites • Course in computer vision • Courses in machine learning is a plus

Paper reviews • For each class – Review one paper in detail – Review one paper at a high-level – (Reduced from last time I offered this course) • Email me reviews by noon (12: 00 pm) the day of the class • Skip reviews the classes you are presenting.

Paper review guidelines • One page • Detailed review: – – – Brief (2 -3 sentences) summary Main contribution Strengths? Weaknesses? How convincing are the experiments? Suggestions to improve them? Extensions? Applications? Additional comments, unclear points • High-level review: – Problem being addressed – High-level intuition/idea of approach • • Relationships observed between the papers we are reading Will pick on students in class during discussions Write in your own words Write well, proof read

Paper presentation guidelines • Papers • Experiments

Papers • Read selected papers in topic area and look at background papers as necessary • Well-organized talk, 45 minutes • What to cover? – Topic overview, motivation – For selected papers: • • Problem overview, motivation Algorithm explanation, technical details Experimental set up, results Strengths, weaknesses, extensions – Any commonalities, important differences between techniques covered in the papers. • See class webpage for more details.

Experiments • Implement/download code for a main idea in the paper and evaluate it: – Experiment with different types of training/testing data sets – Evaluate sensitivity to important parameter settings – Show an example to analyze a strength/weakness of the approach – Show qualitative and quantitative results

Tips • Look up papers and authors. Their webpage may have data, code, slides, videos, etc. – Make sure talk flows well and makes sense as a whole. – Cite ALL sources. • Don’t forget the high-level picture. • Give a very clear and well-organized and thought out talk. • Will interrupt if something is not clear

Tips • Make sure you are saying everything we need to know to understand what you are saying. • Make sure you know what you are talking about. • Think about your audience. • Make your talks visual (images, video, not lots of text).

Projects Possibilities: – Extension of a technique studied in class – Analysis and empirical evaluation of an existing technique – Comparison between two approaches – Design and evaluate a novel approach – Be creative! Can work with a partner Talk to me if you need help with ideas

Project timeline • Project proposals (1 page) [10%] – March 6 th • Mid-semester presentations (10 minutes) [20%] – March 27 th and April 1 st • Final presentations (20 minutes) [35%] – April 24 th to May 6 th • Project reports (4 pages) [35%] – May 12 th – Could serve as a first draft of a conference submission!

Implementation • Use any language / platform you like • No support for code / implementation issues will be provided

Miscellaneous • Best presentation, best project and best discussion prizes! – We will vote – Dinner • Feedback welcome and useful

Coming up • Read the class webpage – Schedule is up – Tour of schedule • Select 6 dates (topics) you would like to present – Email me by Wednesday (tomorrow) – Webpage shows how many people have already signed up for a topic – Select those that have fewer selections • Overview of my research on Thursday – How many of you were at the ECE grad seminar in November?

Questions? See you Thursday!