Background-foreground segmentation using probability models that can provide pixel dependency and incremental training

A method, comprising:

• determining a global state that maximizes a likelihood probability of an image comprising a plurality of pixels;
• determining, for each of at least one pixels of an image, an individual likelihood probability;
• and
• assigning, for each of at least one pixels of an image, a pixel to a foreground when the pixel has a predetermined individual likelihood probability.

A system comprising:

• a memory that stores computer-readable code;
• and
• a processor operatively coupled to said memory, said processor configured to implement said computer-readable code, said computer-readable code configured to:
• determine a global state that maximizes a likelihood of probability of an image comprising a plurality of pixels;
• determine, for each of at least one pixels of an image, an individual likelihood probability;
• and
• assign, for each of at least one pixels of an image, a pixel to a foreground when the pixel has a predetermined individual likelihood probability.

An article of manufacture comprising:

• a computer-readable medium having computer-readable code means embodied thereon, said computer-readable program code means comprising:
• a step to determine a global state that maximizes a likelihood of probability of an image comprising a plurality of pixels;
• a step to determine, for each of at least one pixels of an image, an individual likelihood probability;
• and
• a step to assign, for each of at least one pixels of an image, a pixel to a foreground when the pixel has a predetermined individual likelihood probability.