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Computer Vision

Computer Vision. Mubarak Shah Computer Vision Lab University of Central Florida Orlando, FL 32816. Computer Vision. The ability of computers to see. Image Understanding Machine Vision Robot Vision Image Analysis Video Understanding. A picture is worth a thousand words.

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Computer Vision

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  1. Computer Vision Mubarak Shah Computer Vision Lab University of Central Florida Orlando, FL 32816

  2. Computer Vision • The ability of computers to see. • Image Understanding • Machine Vision • Robot Vision • Image Analysis • Video Understanding

  3. A picture is worth a thousand words.

  4. A word is worth a thousand pictures. A HUNT

  5. Image • 2-D array of numbers (intensity values, gray levels) • Gray levels 0 (black) to 255 (white) • Color image is 3 2-D arrays of numbers • Red • Green • Blue • Resolution (number of rows and columns) • 128X128 • 256X256 • 512X512 • 640X480

  6. Image Formats • TIF • PGM • PBM • GIF • JPEG

  7. Video • Sequence of frames • 30 frames per second • Formats • AVI • MPEG • Quick Time

  8. Video Clip

  9. Sequence of Images

  10. Digitization • TV camera is analog, need • A to D converter • Frame grabber • Digital Cameras do not need digitization • JVC (MPEG through fire wire, USB) • Sony (MPEG through fire wire, USB) • ---

  11. Face Recognition

  12. Simple Approach • Recognize faces (mug shots) using gray levels (appearance) • Each image is mapped to a long vector of gray levels • Several views of each person are collected in the model-base during training • During recognition a vector corresponding to an unknown face is compared with all vectors in the model-base • The face from model-base, which is closest to the unknown face is declared as a recognized face.

  13. Problems and Solution • Problems : • Dimensionality of each face vector will be very large (250,000 for a 512X512 image!) • Raw gray levels are sensitive to noise, and lighting conditions. • Solution: • Reduce dimensionality of face space by finding principal components (eigen vectors) to span the face space • Only a few most significant eigen vectors can be used to represent a face, thus reducing the dimensionality

  14. Eigen Vectors and Eigen Values The eigen vector, x, of a matrix A is a special vector, with the following property Where  is called eigen value To find eigen values of a matrix A first find the roots of: Then solve the following linear system for each eigen value to find corresponding eigen vector

  15. Example Eigen Values Eigen Vectors

  16. Eigen Values

  17. Eigen Vectors

  18. Face Recognition Collect all gray levels in a long vector u: Collect n samples (views) of each of p persons in matrix A (MN X pn): Form a correlation matrix L (MN X MN): Compute eigen vectors, , of L, which form a bases for whole face space

  19. Face Recognition Each face, u, can now be represented as a linear combination of eigen vectors Eigen vectors for a symmetric matrix are orthonormal:

  20. Face Recognition Therefore:

  21. Face Recognition L is a large matrix, computing eigen vectors of a large matrix is time consuming. Therefore compute eigen vectors of a smaller matrix, C: Let be eigen vectors of C, then are the eigen vectors of L:

  22. Training • Create A matrix from training images • Compute C matrix from A. • Compute eigenvectors of C. • Compute eigenvectors of L from eigenvectors of C. • Select few most significant eigenvectors of L for face recognition. • Compute coefficient vectors corresponding to each training image. • For each person, coefficients will form a cluster, compute the mean of cluster.

  23. Recognition • Create a vector u for the image to be recognized. • Compute coefficient vector for this u. • Decide which person this image belongs to, based on the distance from the cluster mean for each person.

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