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Computer Vision

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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.

    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

    7. Image Formats TIF PGM PBM GIF JPEG

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

    9. Video Clip

    10. Sequence of Images

    11. 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) ---

    12. Face Recognition

    13. 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.

    14. 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

    15. Eigen Vectors and Eigen Values

    16. Example

    17. Eigen Values

    18. Eigen Vectors

    19. Face Recognition

    20. Face Recognition

    21. Face Recognition

    22. Face Recognition

    23. 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.

    24. 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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