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Presentation 4

Presentation 4. Zach Robertson. Our method. Pedro’s method. Low threshold, false positive. Low threshold, no false positive. High threshold, false negative. Head Detector. Our method. Pedro’s Method. Train Human Model in 3D. We need a good descriptor for depth as good as HOG for RGB

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Presentation 4

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  1. Presentation 4 Zach Robertson

  2. Our method Pedro’s method Low threshold, false positive Low threshold, no false positive High threshold, false negative

  3. Head Detector Our method Pedro’s Method

  4. Train Human Model in 3D • We need a good descriptor for depth as good as HOG for RGB • What should we use for Depth?

  5. Papers • Invariant Surface Characteristics for 3d Object Recognition by Besl and Jain • Mean curvature and Gaussian Curvature as visible invariant

  6. Gaussian Curvature • Gives where surface is convex, saddle, or flat • Indicates surface shape at a pixel Mean Curvature • The average of the principal curvatures • If zero, minimal surface

  7. Coded • Produce normal vectors • Produce mean curvature • Produce gaussian curvature

  8. Train SVM • There are 9 different possibilities • Only 8 will actually happen • Created a histogram of curvature

  9. Noise

  10. Normal and Curvatures Norm in Z direction Norm in X direction Norm in Y direction Mean Curvature Gaussian Curvature

  11. Fixing Noise • Use the median to smooth • Save images in lossless format (such as .png) • Changing the range of values from 0 to 255 to 0 to 4000 • Allows more detail to be maintain

  12. Median Smoothed Gaussian Smoothed

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