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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 3D 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
building
tree
banner
vendorpeople
street lamp
Instance recognition
Potala Palace
A particular sign
Scene and context categorization
• outdoor
• city
• …
Attribute recognition
flat
graymade of fabric
crowded
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
Kooaba, Bay & Quack et al.
Yeh et al., MIT
Belhumeur et al.
Kristen Grauman
Exploring community photo collections
Snavely et al.
Simon & SeitzKristen Grauman
http://www.darpa.mil/grandchallenge/gallery.asp
Autonomous agents able to detect objects
Kristen Grauman
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
Object pose
ViewpointIntra-class appearance
Occlusions
Clutter
Kristen Grauman
Challenges: context and human experience
Context cues
Kristen Grauman
Challenges:context and human experience
Context cues Function Dynamics
Video credit: J. DavisKristen Grauman
Challenges: scale, efficiency
• Half of the cerebral cortex in primates is devoted to processing visual information
• ~20 hours of video added to YouTube 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
MoreLess
Cropped to
object, parts and
classes labeled
Classes labeled,
some clutter
Unlabeled,
multiple
objects
Kristen Grauman
Slide from Pietro Perona, 2004 Object Recognition workshop
Slide from Pietro Perona, 2004 Object Recognition workshop
Recognizing flat, textured objects (like books, CD
covers, posters)
Reading license plates, zip codes, checks
Fingerprint recognition
Frontal face detection
What kinds of things work best today?
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
Personal photo albums
Surveillance and security
Movies, news, sports
Medical and scientific images
Slide credit; L. Lazebnik
… and inputs today
… and inputs today
Images on the Web Movies, news, sports
916,271 titles
10 mil. videos, 65,000 added daily
350 mil. photos, 1 mil. added daily
1.6 bil. images indexed as of summer 2005
Satellite imagery City streets
Slide credit; L. Lazebnik
Understand and organize and index all this data!!
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/~S14ECE5984/(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).
ProjectsPossibilities:
– 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 6th
• Mid-semester presentations (10 minutes) [20%]– March 27th and April 1st
• Final presentations (20 minutes) [35%]– April 24th to May 6th
• Project reports (4 pages) [35%]– May 12th
– 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!
