MATLAB File Help: cv.SuperpixelSEEDS Index
cv.SuperpixelSEEDS

Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm

As described in [VBRV14].

The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy function that is based on color histograms and a boundary term, which is optional. The energy function encourages superpixels to be of the same color, and if the boundary term is activated, the superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the solution. The algorithm runs in real-time using a single CPU.

References

[VBRV14]:

Michael Van den Bergh, Xavier Boix, Gemma Roig, Benjamin de Capitani, and Luc Van Gool. "SEEDS: Superpixels Extracted via Energy-Driven Sampling". In Computer Vision-ECCV 2012, pages 13-26. Springer, 2012.

See also
Class Details
Superclasses handle
Sealed false
Construct on load false
Constructor Summary
SuperpixelSEEDS Initializes a SuperpixelSEEDS object 
Property Summary
id Object ID 
Method Summary
  addlistener Add listener for event. 
  clear Clears the algorithm state 
  delete Destructor 
  empty Checks if detector object is empty. 
  eq == (EQ) Test handle equality. 
  findobj Find objects matching specified conditions. 
  findprop Find property of MATLAB handle object. 
  ge >= (GE) Greater than or equal relation for handles. 
  getDefaultName Returns the algorithm string identifier 
  getLabelContourMask Returns the mask of the superpixel segmentation stored in object 
  getLabels Returns the segmentation labeling of the image 
  getNumberOfSuperpixels Calculates the superpixel segmentation on a given image stored in object 
  gt > (GT) Greater than relation for handles. 
Sealed   isvalid Test handle validity. 
  iterate Calculates the superpixel segmentation on a given image with the initialized parameters in the object 
  le <= (LE) Less than or equal relation for handles. 
  load Loads algorithm from a file or a string 
  lt < (LT) Less than relation for handles. 
  ne ~= (NE) Not equal relation for handles. 
  notify Notify listeners of event. 
  save Saves the algorithm parameters to a file