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Computer Vision Metrics: Chapter Two (Part E)

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For Part D of Chapter Two, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


Gradient-Ascent-Based Super-Pixel Methods

Gradient ascent methods iteratively refine the super-pixel clusters to optimize the segmentation until convergence criteria are reached. These methods use a tree graph structure to associate pixels together according to some criteria, which in this case may be the RGB values or Cartesian coordinates of the pixels, and then a distance function or other function is applied to create regions. Since these are iterative methods, the performance can be slow.

  • Mean-Shift [266] Works by registering off of the region centroid based on a kernel-based mean smoothing approach to create regions of like pixels.
  • Quick-Shift [267] Similar to the mean-shift method but does not use a mean blur kernel and instead uses a distance function calculated from the graph structure based on RGB values and XY pixel coordinates.
  • Watershed [268] Starts from local region pixel value minima points to find pixel value-based contour lines defining watersheds, or basin contours inside which similar pixel values can be substituted to create a homogeneous pixel value region.
  • Turbopixels [269] Uses small circular seed points placed in a uniform grid across the image around which super-pixels are collected into assigned regions, and then the super-pixel boundaries are gradually expanded into the unassigned region, using a geometric flow method to expand the boundaries using controlled boundary value expansion criteria, so as to gather more pixels together into regions with fairly smooth and uniform geometric shape and size.

Depth Segmentation

Depth information, such as a depth map as shown in Figure 2-20, is ideal for segmenting objects based on distance. Depth maps can be computed from a wide variety of depth sensors and methods, including a single camera, as discussed in Chapter 1. Depth cameras, such as the Microsoft Kinect camera, are becoming more common. A depth map is a 2D image or array, where each pixel value is the distance or Z value.


Figure 2-20. Depth images from Middlebury Data set: (Left) Original image. (Right) Corresponding depth image. Data courtesy of Daniel Scharstein and used by permission

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