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Xiuwen Liu Department of Computer Science Florida State University PowerPoint Presentation
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Xiuwen Liu Department of Computer Science Florida State University

Xiuwen Liu Department of Computer Science Florida State University

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Xiuwen Liu Department of Computer Science Florida State University

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  1. Research Activities at Center for Applied Vision and Imaging Sciences andFlorida State Vision GroupFlorida State University Xiuwen Liu Department of Computer Science Florida State University http://cavis.fsu.edu & http://fsvision.fsu.edu

  2. Research Statement • My research goal is to create machines that can “see” with similar human performance • This seems a trivial problem as each of us can do this without any effort • Computer + Camera = “A See Machine” ?

  3. Visual Pathway

  4. Visual Illusion

  5. Outline • Motivations • Some applications of computer vision and pattern recognition techniques • Some of the research projects • Related Courses • Contact information

  6. Computer Vision Applications • No hands across America • Sponsored by Delco Electronics, AssistWare Technology, and Carnegie Mellon University • Navlab 5 drove from Pittsburgh, PA to San Diego, CA, using the RALPHcomputer program. • The trip was 2849 miles of which 2797 miles were driven automatically with no hands • Which is 98.2%

  7. Computer Vision Applications– continued

  8. Computer Vision Applications– continued

  9. Human-Computer Interactions

  10. Sign Language Recognition

  11. CyberKnife

  12. CyberKnife – Cont.

  13. Image-Guided Neurosurgery

  14. Intelligent Transportation Systems http://dfwtraffic.dot.state.tx.us/dal-cam-nf.asp

  15. Computer Vision Applications – cont. • Military applications • Automated target recognition

  16. Computer Vision Applications– continued

  17. Biometrics – cont. Iris code can achieve zero false acceptance

  18. Computer Vision in Sports • How was the yellow created?

  19. How can we characterize all these images perceptually? Generic Image Modeling

  20. Spectral Histogram Representation • Spectral histogram • Given a bank of filters F(a), a = 1, …, K, a spectral histogram is defined as the marginal distribution of filter responses

  21. LoG filter Gabor filter Spectral Histogram Representation - continued • Choice of filters • Laplacian of Gaussian filters • Gabor filters • Gradient filters • Intensity filter

  22. Spectral Histogram Representation - continued

  23. Texture Synthesis Examples - continued • An image with periodic structures Observed image Synthesized image

  24. Object Synthesis Examples - continued

  25. Performance Comparison

  26. Face Detection Based On Spectral Representations • Face detection is to detect all instances of faces in a given image • Each image window is represented by its spectral histogram • A support vector machine is trained on training faces • Then the trained support vector machine is used to classify each image window in an input image • More results athttp://fsvision.fsu.edu/face-detection

  27. Face detection - continued

  28. Face detection - continued

  29. Face detection - continued

  30. Rotation Invariant Face Detection

  31. Rotation Invariant Face Detection - continued

  32. Linear Representations • Linear representations are widely used in appearance-based object recognition and other applications • Simple to implement and analyze • Efficient to compute • Effective for many applications

  33. Standard Linear Representations • Principal Component Analysis • Designed to minimize the reconstruction error on the training set • Obtained by calculating eigenvectors of the co-variance matrix • Fisher Discriminant Analysis • Designed to maximize the separation between means of each class • Obtained by solving a generalized eigen problem • Independent Component Analysis • Designed to maximize the statistical independence among coefficients along different directions • Obtained by solving an optimization problem with some object function such as mutual information, negentropy, ....

  34. Standard Linear Representations - continued • Standard linear representations are sub optimal for recognition applications • Evidence in the literature • A toy example • Standard representations give the worst recognition performance • Optimal component analysis

  35. Performance Measure - continued • Suppose there are C classes to be recognized • Each class has ktrain training images • It has kcross cross validation images • We used h(x) = 1/(1+exp(-2bx)

  36. Performance Measure - continued • F(U) depends on the span of U but is invariant to change of basis • In other words, F(U)=F(UO) for any orthonormal matrix O • The search space of F(U) is the set of all the subspaces, which is known as the Grassmann manifold • It is not a flat vector space and gradient flow must take the underlying geometry of the manifold into account

  37. Deterministic Gradient Flow - continued • Gradient at [J] (first d columns of n x n identity matrix)

  38. Deterministic Gradient Flow - continued • Gradient at U: Compute Q such that QU=J • Deterministic gradient flow on Grassmann manifold

  39. Stochastic Gradient and Updating Rules • Stochastic gradient is obtained by adding a stochastic component • Discrete updating rules

  40. MCMC Simulated Annealing Optimization Algorithm • Let X(0) be any initial condition and t=0 • Calculate the gradient matrix A(Xt) • Generate d(n-d) independent realizations of wij’s • Compute Y (Xt+1) according to the updating rules • Compute F(Y) and F(Xt) and set dF=F(Y)- F(Xt) • Set Xt+1 = Y with probability min{exp(dF/Dt),1} • Set Dt+1 = Dt / g and set t=t+1 • Go to step 1

  41. ORL Face Dataset

  42. Performance Comparison

  43. Performance Comparison – cont.

  44. Brain Curve Classification

  45. Brain Curve Classification – cont.

  46. Real-time Scene Interpretation • Object detection and recognition problem • Given a set of images, find regions in these images which contain instances of relevant objects • Here the number of relevant objects is assumed to be large • For example, the system should be able to handle 30,000 different kinds of objects, an estimate of the human brain’s capacity for basic level visual categorization [I. Biederman, Psychological Review, vol. 94, pp. 115-147, 1987]

  47. Global Monitoring Through High-resolution Satellite Images

  48. Problem Statement for Scene Interpretation • Object detection and recognition problem • Given a set of images, find regions in these images which contain instances of relevant objects • Here the number of relevant objects is assumed to be large • For example, the system should be able to handle 30,000 different kinds of objects, an estimate of the human’s capacity for basic level visual categorization [I. Biederman, Psychological Review, vol. 94, pp. 115-147, 1987] • Goal • Develop a system that can achieve real-time detection and recognition for images of size 640 x 480 with high accuracy • Say, at a frame rate of 15 frames per second