1	Announcements Project 2 due tomorrow night Project 3 out Wednesday Jiun-Hung will take photos beginning of class
2	Recognition Readings Szeliski Chapter 14
3	Recognition Readings Szeliski Chapter 14
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10	Applications: Computational photography
11	Applications: Assisted driving
12	Applications: image search
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20	Let’s start simple Today skin detection eigenfaces face detection with adaboost
21	Face detection How to tell if a face is present?
22	One simple method: skin detection Skin pixels have a distinctive range of colors Corresponds to region(s) in RGB color space for visualization, only R and G components are shown above
23	Skin detection Learn the skin region from examples Manually label pixels in one or more “training images” as skin or not skin Plot the training data in RGB space skin pixels shown in orange, non-skin pixels shown in blue some skin pixels may be outside the region, non-skin pixels inside. Why?
24	Skin classification techniques
25	Probability Basic probability X is a random variable P(X) is the probability that X achieves a certain value or Conditional probability: P(X \| Y) probability of X given that we already know Y
26	Probabilistic skin classification Now we can model uncertainty Each pixel has a probability of being skin or not skin
27	Learning conditional PDF’s We can calculate P(R \| skin) from a set of training images It is simply a histogram over the pixels in the training images each bin R_i contains the proportion of skin pixels with color R_i
28	Learning conditional PDF’s We can calculate P(R \| skin) from a set of training images It is simply a histogram over the pixels in the training images each bin R_i contains the proportion of skin pixels with color R_i
29	Bayes rule In terms of our problem:
30	Bayesian estimation Bayesian estimation Goal is to choose the label (skin or ~skin) that maximizes the posterior this is called Maximum A Posteriori (MAP) estimation
31	Skin detection results
32	General classification This same procedure applies in more general circumstances More than two classes More than one dimension
33	Linear subspaces Classification can be expensive Must either search (e.g., nearest neighbors) or store large PDF’s
34	Dimensionality reduction
35	Linear subspaces
36	Principal component analysis Suppose each data point is N-dimensional Same procedure applies: The eigenvectors of A define a new coordinate system eigenvector with largest eigenvalue captures the most variation among training vectors x eigenvector with smallest eigenvalue has least variation We can compress the data by only using the top few eigenvectors corresponds to choosing a “linear subspace” represent points on a line, plane, or “hyper-plane” these eigenvectors are known as the principal components
37	The space of faces An image is a point in a high dimensional space An N x M image is a point in R^NM We can define vectors in this space as we did in the 2D case
38	Dimensionality reduction The set of faces is a “subspace” of the set of images Suppose it is K dimensional We can find the best subspace using PCA This is like fitting a “hyper-plane” to the set of faces spanned by vectors v₁, v₂, ..., v_K any face
39	Eigenfaces PCA extracts the eigenvectors of A Gives a set of vectors v₁, v₂, v₃, ... Each one of these vectors is a direction in face space what do these look like?
40	Projecting onto the eigenfaces The eigenfaces v₁, ..., v_K span the space of faces A face is converted to eigenface coordinates by
41	Recognition with eigenfaces Algorithm Process the image database (set of images with labels) Run PCA—compute eigenfaces Calculate the K coefficients for each image Given a new image (to be recognized) x, calculate K coefficients Detect if x is a face If it is a face, who is it?
42	Choosing the dimension K How many eigenfaces to use? Look at the decay of the eigenvalues the eigenvalue tells you the amount of variance “in the direction” of that eigenface ignore eigenfaces with low variance
43	Issues: metrics What’s the best way to compare images? need to define appropriate features depends on goal of recognition task
44	Metrics Lots more feature types that we haven’t mentioned moments, statistics metrics: Earth mover’s distance, ... edges, curves metrics: Hausdorff, shape context, ... 3D: surfaces, spin images metrics: chamfer (ICP) ...
45	Issues: feature selection
46	Issues: data modeling Generative methods model the “shape” of each class histograms, PCA, mixtures of Gaussians graphical models (HMM’s, belief networks, etc.) ... Discriminative methods model boundaries between classes perceptrons, neural networks support vector machines (SVM’s)
47	Generative vs. Discriminative
48	Issues: dimensionality What if your space isn’t flat? PCA may not help
49	Issues: speed Case study: Viola Jones face detector Next few slides adapted Grauman & Liebe’s tutorial http://www.vision.ee.ethz.ch/~bleibe/teaching/tutorial-aaai08/ Also see Paul Viola’s talk (video) http://www.cs.washington.edu/education/courses/577/04sp/contents.html#DM
50	Feature extraction
51	Large library of filters
52	AdaBoost for feature+classifier selection Want to select the single rectangle feature and threshold that best separates positive (faces) and negative (non-faces) training examples, in terms of weighted error.
53	AdaBoost: Intuition
54	AdaBoost: Intuition
55	AdaBoost: Intuition
56	AdaBoost Algorithm
57	Cascading classifiers for detection
58	Viola-Jones Face Detector: Summary
59	Viola-Jones Face Detector: Results
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63	Detecting profile faces?
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