Segmentation of objects by minimizing global-local variational energy
First Claim
1. A system for automatically identifying a boundary curve for delimiting an object of interest within an image frame, comprising using a computing device to perform the steps for:
- receiving an image frame containing an object of interest;
sampling separate areas of the image frame to initialize separate probabilistic color distribution models of a foreground region and a background region of the image frame, said probabilistic models of the foreground and background regions jointly comprising a local image data likelihood;
initializing a global image data likelihood as a function of a combination of the probabilistic models of the foreground and background regions of the image frame;
initializing a boundary curve as a boundary surrounding the area of the image frames sampled to initialize the probabilistic model of the foreground region; and
jointly performing an iterative minimization of energy functionals representing the boundary curve, the local image data likelihood and the global image data likelihood to generate a final boundary curve for delimiting the object of interest.
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Abstract
An “Image Segmenter” provides a variational energy formulation for segmentation of natural objects from images. In general, the Image Segmenter operates by adopting Gaussian mixture models (GMM) to capture the appearance variation of objects in one or more images. A global image data likelihood potential is then computed and combined with local region potentials to obtain a robust and accurate estimation of pixel foreground and background distributions. Iterative minimization of a “global-local energy function” is then accomplished by evolution of a foreground/background boundary curve by level set, and estimation of a foreground/background model by fixed-point iteration, termed “quasi-semi-supervised EM.” In various embodiments, this process is further improved by providing general object shape information for use in rectifying objects segmented from the image.
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Citations
20 Claims
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1. A system for automatically identifying a boundary curve for delimiting an object of interest within an image frame, comprising using a computing device to perform the steps for:
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receiving an image frame containing an object of interest;
sampling separate areas of the image frame to initialize separate probabilistic color distribution models of a foreground region and a background region of the image frame, said probabilistic models of the foreground and background regions jointly comprising a local image data likelihood;
initializing a global image data likelihood as a function of a combination of the probabilistic models of the foreground and background regions of the image frame;
initializing a boundary curve as a boundary surrounding the area of the image frames sampled to initialize the probabilistic model of the foreground region; and
jointly performing an iterative minimization of energy functionals representing the boundary curve, the local image data likelihood and the global image data likelihood to generate a final boundary curve for delimiting the object of interest. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer readable medium having computer executable instructions for automatically segmenting an object of interest from an image frame, said computer executable instructions comprising:
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sampling an input image in a plurality of regions along an outer edge of the input image to construct an initial Gaussian mixture model representing a color distribution of the input image background;
sampling the input image in a region assumed to include a least a portion of the object of interest to construct an initial Gaussian mixture model representing a color distribution of the input image foreground;
initializing a boundary curve as a curve encompassing the region sampled for construction of the foreground Gaussian mixture model;
initializing a global Gaussian mixture model as a weighted function of the foreground and background Gaussian mixture models;
constructing a local energy functional from a weighted combination of the foreground and background Gaussian mixture models;
constructing a global energy functional from the global Gaussian mixture model;
jointly performing an iterative minimization of an energy functional representing the boundary curve, the local energy functional, and the global energy functional for evolving a final boundary curve for delimiting the object of interest; and
segmenting the object of interest from the input image as a function of the final boundary curve. - View Dependent Claims (10, 11, 12, 13)
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14. A computer implemented process for identifying an object of interest within an image, comprising using a computer to:
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sample an image to construct an initial Gaussian mixture model representing a color distribution of the image background;
sample a region of the image to construct an initial Gaussian mixture model representing a color distribution of the image foreground;
initialize a boundary curve to encompass the region sampled for construction of the foreground Gaussian mixture model;
initialize a global Gaussian mixture model as a weighted function of the foreground and background Gaussian mixture models;
jointly performing an iterative minimization of the energy of;
the boundary curve, a combination of the foreground and background Gaussian mixture models, said combination representing a local Gaussian mixture model, and the global Gaussian mixture model; and
wherein the joint iterative minimization results in the evolution of a final boundary curve for delimiting the object of interest. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification