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Image Statistics and Operations in Visualization Laboratories

Learn about the foundations of image visualization, including statistics like mean, variance, and standard deviation, as well as point operations, color modifications, and compositing techniques. This course provides essential knowledge for making automated decisions about images.

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Image Statistics and Operations in Visualization Laboratories

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  1. ENDS 375 Foundations of Visualization 9/20/05 Notes Visualization Laboratory, Texas A&M University

  2. Image Statistics • Useful input into computational algorithms • measures of image quality • basis for automated decisions about images Visualization Laboratory, Texas A&M University

  3. Image Statistics • Arithmetic Mean mean = sum(Pxy)/(x*y) • Variance variance = (sum(Pxy*Pxy)/(x*y)-mean*mean) Visualization Laboratory, Texas A&M University

  4. Image Statistics • Standard Deviation stdev = square root (variance) • Histogram • two axis plot of pixel values vs number of pixels • basis for deciding - contrast range, overall brightness, thresholding, ... Visualization Laboratory, Texas A&M University

  5. Visualization Laboratory, Texas A&M University

  6. Point Operations on Images • Numeric Transformation • (often not reversible) • Transfer Functions • Often implemented using look-up tables Visualization Laboratory, Texas A&M University

  7. Specific Operations Unity Invert Visualization Laboratory, Texas A&M University

  8. Specific Operations Contrast Adjustment Higher Lower Visualization Laboratory, Texas A&M University

  9. Specific Operations Threshold Gamma Visualization Laboratory, Texas A&M University

  10. Color Modification Less Red More Yellow Visualization Laboratory, Texas A&M University

  11. Arithmetic Operations Two or more images Cxy = Axy< operation > Bxy • Addition • Subtraction • Averaging, etc ... Visualization Laboratory, Texas A&M University

  12. Logical Operations and, or nand, nor xor, xnor Visualization Laboratory, Texas A&M University

  13. Image Averaging Add corresponding pixels from multiple images then divide by the number of images Visualization Laboratory, Texas A&M University

  14. Alpha Blending Cxy = Axy*Mxy + Bxy*(max -Mxy ) “Blends” two images Need a “matte” image Basis for image compositing Visualization Laboratory, Texas A&M University

  15. Compositing Visualization Laboratory, Texas A&M University

  16. Readings • Course notes section 1-7 • Course notes section 1-8 Visualization Laboratory, Texas A&M University

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