Milestones

Milestones

  1. Stage 0 - Data preparation
    1. Convert video streams into image sequences
    2. Label sequences according to sequence runs
      1. Each distinct viewpoint gets a unique camera name. all adjacent frames from the viewpoint follow in sequence
      2. For example: a0000.jpg a0001.jpg a0002.jpg b0000.jpg
    3. Prep the images by pushing them through an outlining / edge-detection filter
  2. Stage 1 - Data Segmentation and Flattening
    1. Segment the images into self-similar patches
      1. Edge the images to approximate edges of patches
        1. Crawler?
        2. Use outlined and original image as a guide
      2. Describe patch by a generalized histogram - ex: find mean and std.dev for each channel
      3. Match patches within image sequence - assume that the patches do not move "greatly" from frame to frame
  3. Stage 2 - Stitching patchwork and testing coherency
    1. Stitch the patches into adjacent patchworks
      1. Use "feedback loops" to test temporal / spatial coherence
      2. If texture A and B are adjacent in frame f but not in frame g, either they're not adjacent on the object's surface or there is something occluding the joint between A and B.
    2. "Flatten" surface texture - Find highest quality, average level set of pixels that best describe the texture
  4. Stage 3 - 3D Reconstruction
    1. SIFT and SFM ? to create sparse 3D reconstruction
    2. Describe how all permutations change the texture's representation (shade, highlight, etc.)
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