Title :
Segmenting simply connected moving objects in a static scene
Author_Institution :
Dept. of Comput. Sci., Zurich Univ., Switzerland
fDate :
11/1/1994 12:00:00 AM
Abstract :
A new segmentation algorithm is derived, based on an object-background probability estimate exploiting the experimental fact that the statistics of local image derivatives show a Laplacian distribution. The objects´ simple connectedness is included directly into the probability estimate and leads to an iterative optimization approach that can be implemented efficiently. This new approach avoids early thresholding, explicit edge detection, motion analysis, and grouping
Keywords :
image segmentation; iterative methods; optimisation; probability; statistical analysis; Laplacian distribution; iterative optimization approach; local image derivatives; object-background probability estimate; segmentation algorithm; simply connected moving objects; static scene; statistic; Application software; Computer vision; Image edge detection; Image motion analysis; Image segmentation; Iterative methods; Layout; Optical computing; Poisson equations; Probability;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
