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Research Activities at Computer Vision and Image Understanding Group Florida State University

Research Activities at Computer Vision and Image Understanding Group Florida State University. Xiuwen Liu Florida State Vision Group Department of Computer Science Florida State University http://fsvision.cs.fsu.edu. Outline. Motivations Some applications of computer vision techniques

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Research Activities at Computer Vision and Image Understanding Group Florida State University

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  1. Research Activities at Computer Vision and Image Understanding GroupFlorida State University Xiuwen Liu Florida State Vision Group Department of Computer Science Florida State University http://fsvision.cs.fsu.edu

  2. Outline • Motivations • Some applications of computer vision techniques • Computer Vision and Image Understanding Group • Some of the research projects • Contact information

  3. Introduction • An image patch represented by hexadecimals

  4. Introduction - continued • Fundamental problem in computer vision • Given a matrix of numbers representing an image, or a sequence of images, how to generate a perceptually meaningful description of the matrix? • An image can be a color image, gray level image, or other format such as remote sensing images • A two-dimensional matrix represents a signal image • A three-dimensional matrix represents a sequence of images • A video sequence is a 3-D matrix • A movie is also a 3-D matrix

  5. Introduction - continued

  6. Introduction - continued • Why do we want to work on this problem? • It is very interesting theoretically • It involves many disciplines to develop a computational model for the problem • It is the key component to understand and model intelligence • Note that 50% of the brain is devoted to vision • It has many practical applications • Internet applications • Movie-making applications • Military applications

  7. 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%

  8. Computer Vision Applications– continued

  9. Computer Vision Applications– continued DARPA Grant Challenge: http://www.darpa.mil/grandchallenge/index.htm

  10. Computer Vision Applications– continued • Military applications • Automated target recognition

  11. Computer Vision Applications– continued

  12. Computer Vision Applications– continued • Extracted hydrographic regions

  13. Computer Vision Applications– continued • Medical image analysis • Characterize different types of tissues in medical images for automated medical image analysis

  14. Computer Vision Applications– continued

  15. Computer Vision Applications– continued • Biometrics • From faces, fingerprints, iris patterns ..... • It has many applications such as security, ATM withdrawal, credit card managements .....

  16. Computer Vision Applications – cont.

  17. Computer Vision Applications– continued • Content-based image retrieval has become an active research area to meet the needs of searching images on the web in a meaningful way • Color histogram has been widely used

  18. Content-Based Image Retrieval – cont.

  19. Vision-Based Image Morphing

  20. Vision-Based Image Morphing - continued

  21. Computer Vision and Image Understanding Group • Faculty: Xiuwen Liu, Anuj Srivastava, Washington Mio, Eric Klassen • Goals: Develop and implement effective image understanding algorithms and systems for images and videos from multi modalities including visible, infrared, and range sensors • Approaches: Learning-based vision algorithms, statistical modeling of objects, computational modeling and analysis of textures, statistical modeling of shapes, stochastic optimization, inference algorithms on manifolds, and Bayesian inference

  22. Research Projects • The group offers a wide range of research possibilities • Implementation projects • Development of new applications • Development of new algorithms • Theoretical and mathematical analysis of algorithms

  23. Implementation Projects • These projects involve implementing proven ideas and algorithms on specific datasets with specific interface and programming language constraints • For example, Haitao Wu implemented a graphical user interface for a face recognition algorithm we have as his Masters project • Yu Wang implemented a web-based interface for a content-based image retrieval algorithm

  24. A Real-time Recognition/Tracking System

  25. Content-based Image Retrieval Image Query System by Yu Wang

  26. Future Implementation Possibilities • Implement a Java-based system for face detection • Implement a Java-based system for learning • Implement and improve web-based systems for content-based image and video retrieval

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

  28. 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

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

  30. Spectral Histogram Representation - continued

  31. Average spectral histogram error A Texture Synthesis Example • A white noise image was transformed to a perceptually similar texture by matching the spectral histogram

  32. Observed image Synthesized image Texture Synthesis Examples - continued • A random texture image

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

  34. Texture Synthesis Examples - continued • A mud image with some animal foot prints Mud image Synthesized image

  35. Texture Synthesis Examples - continued • A random texture image with elements Observed image Synthesized image

  36. Object Synthesis Examples • As in texture synthesis, we start from a random image • In addition, similar object images are used as boundary conditions in that the corresponding pixel values are not updated during sampling process

  37. Object Synthesis Examples - continued

  38. Object Synthesis Examples - continued

  39. Principal Component Analysis

  40. Eigen Values of 400 Eigen Vectors

  41. Principal Component Analysis - continued Reconstructed using 50 PCs Reconstructed using 200 PCs Original Image

  42. Principal Component Analysis - continued

  43. Principal Component Analysis - continued

  44. Difference Between Reconstruction and Sampling Reconstruction is not sufficient to show the adequacy of a representation and sampling from the set of images with same representation is more informational

  45. 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

  46. Face detection - continued

  47. Face detection - continued

  48. Face detection - continued

  49. Rotation invariant face detection

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