Computer Vision

1
Computer Vision

• CSPP 56553
• Artificial Intelligence
• March 3, 2004

2
Roadmap

• Motivation
• Computer vision applications
• Is a Picture worth a thousand words?
• Low level features
• Feature extraction intensity, color
• High level features
• Top-down constraint shape from stereo, motion,..
• Case Study Vision as Modern AI
• Fast, robust face detection (Viola Jones 2002)

3
Perception

• From observation to facts about world
• Analogous to speech recognition
• Stimulus (Percept) S, World W
• S g(W)
• Recognition Derive world from percept
• Wg(S)
• Is this possible?

4
Key Perception Problem

• Massive ambiguity
• Optical illusions
• Occlusion
• Depth perception
• Objects are closer than they appear
• Is it full-sized or a miniature model?

5
Handling Uncertainty

• Identify single perfect correct solution
• Impossible!
• Noise, ambiguity, complexity
• Solution
• Probabilistic model
• P(WS) aP(SW) P(W)
• Maximize image probability and model probability

6
Handling Complexity

• Dont solve the whole problem
• Dont recover every object/position/color
• Solve restricted problem
• Find all the faces
• Recognize a person
• Align two images

7
Modern Computer Vision Applications

• Face / Object detection
• Medical image registration
• Face recognition
• Object tracking

8
Computer Vision Case Study

• Rapid Object Detection using a Boosted Cascade
  of Simple Features, Viola/Jones 01
• Challenge
• Object detection
• Find all faces in an arbitrary images
• Real-time execution
• 15 frames per second
• Need simple features, classifiers

9
Rapid Object Detection Overview

• Fast detection with simple local features
• Simple fast feature extraction
• Small number of computations per pixel
• Rectangular features
• Feature selection with Adaboost
• Sequential feature refinement
• Cascade of classifiers
• Increasingly complex classifiers
• Repeatedly rule out non-object areas

10
Picking Features

• What cues do we use for object detection?
• Not direct pixel intensities
• Features
• Can encode task specific domain knowledge (bias)
• Difficult to learn directly from data
• Reduce training set size
• Feature system can speed processing

11
Rectangle Features

• Treat rectangles as units
• Derive statistics
• Two-rectangle features
• Two similar rectangular regions
• Vertically or horizontally adjacent
• Sum pixels in each region
• Compute difference between regions

12
Rectangle Features II

• Three-rectangle features
• 3 similar rectangles horizontally/vertically
• Sum outside rectangles
• Subtract from center region
• Four-rectangle features
• Compute difference between diagonal pairs
• HUGE feature set 180,000

13
Rectangle Features
14
Computing Features Efficiently

• Fast detection requires fast feature calculation
• Rapidly compute intermediate representation
• Integral image
• Value for point (x,y) is sum of pixels above,
  left
• ii(x,y) Sxltx,ylty i(x,y)
• Computed by recurrence
• s(x,y) s(x,y-1) i(x,y) , where s(x,y)
  cumulative row
• ii(x,y) ii(x-1,y) s(x,y)
• Compute rectangle sum with 4 array references

15
Rectangle Feature Summary

• Rectangle features
• Relatively simple
• Sensitive to bars, edges, simple structure
• Coarse
• Rich enough for effective learning
• Efficiently computable

16
Learning an Image Classifier

• Supervised training /- examples
• Many learning approaches possible
• Adaboost
• Selects features AND trains classifier
• Improves performance of simple classifiers
• Guaranteed to converge exponentially rapidly
• Basic idea Simple classifier
• Boosts performance by focusing on previous errors

17
Feature Selection and Training

• Goal Pick only useful features from 180000
• Idea Small number of features effective
• Learner selects single feature that best
  separates /- ve examples
• Learner selects optimal threshold for each
  feature
• Classifier h(x) 1 if pf(x)ltp?, 0 otherwise

18
Basic Learning Results

• Initial classification Frontal faces
• 200 features
• Finds 95, 1/14000 false positive
• Very fast
• Adding features adds to computation time
• Features interpretable
• Darker region around eyes that nose/cheeks
• Eyes are darker than bridge of nose

19
Attentional Cascade

• Goal Improved classification, reduced time
• Insight Small fast classifiers can reject
• But have very few false negatives
• Reject majority of uninteresting regions quickly
• Focus computation on interesting regions
• Approach Degenerate decision tree
• Aka cascade
• Positive results passed to high detection
  classifiers
• Negative results rejected immediately

20
Cascade Schematic
All Sub-window Features
T
T
T
CL 1
CL 2
CL 3
More Classifiers
F
F
F
Reject Sub-Window
21
Cascade Construction

• Each stage is a trained classifier
• Tune threshold to minimize false negatives
• Good first stage classifier
• Two feature strong classifier eye/check
  eye/nose
• Tuned Detect 100 40 false positives
• Very computationally efficient
• 60 microprocessor instructions

22
Cascading

• Goal Reject bad features quickly
• Most features are bad
• Reject early in processing, little effort
• Good regions will trigger full cascade
• Relatively rare
• Classification is progressively more difficult
• Rejected the most obvious cases already
• Deeper classifiers more complex, more error-prone

23
Cascade Training

• Tradeoffs Accuracy vs Cost
• More accurate classifiers more features, complex
• More features, more complex Slower
• Difficult optimization
• Practical approach
• Each stage reduces false positive rate
• Bound reduction in false pos, increase in miss
• Add features to each stage until meet target
• Add stages until overall effectiveness targets met

24
Results

• Task Detect frontal upright faces
• Face/non-face training images
• Face 5000 hand-labeled instances
• Non-face 9500 random web-crawl, hand-checked
• Classifier characteristics
• 38 layer cascade
• Increasing number of features 1,10,25, 6061
• Classification Average 10 features per window
• Most rejected in first 2 layers
• Process 384x288 image in 0.067 secs

25
Detection Tuning

• Multiple detections
• Many subwindows around face will alert
• Create disjoint subsets
• For overlapping boundaries, only report one
• Return average of corners
• Voting
• 3 similarly trained detectors
• Majority rules
• Improves overall

26
Conclusions

• Fast, robust facial detection
• Simple, easily computable features
• Simple trained classifiers
• Classification cascade allows early rejection
• Early classifiers also simple, fast
• Good overall classification in real-time