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Xiuwen Liu Department of Computer Science Florida State University

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

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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

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