System and method for automatically learning flexible sprites in video layers
First Claim
1. A system for automatically decomposing an image sequence, comprising:
- providing a first sequence of at least one image frame of a scene;
providing a preferred number of objects to be identified within the image sequence;
providing a preferred number of layers into which each frame of the image sequence is to be decomposed; and
automatically decomposing the first image sequence into the preferred number of objects and the preferred number of layers.
2 Assignments
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Accused Products
Abstract
A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.
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Citations
58 Claims
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1. A system for automatically decomposing an image sequence, comprising:
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providing a first sequence of at least one image frame of a scene; providing a preferred number of objects to be identified within the image sequence; providing a preferred number of layers into which each frame of the image sequence is to be decomposed; and automatically decomposing the first image sequence into the preferred number of objects and the preferred number of layers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A computer-implemented process for automatically generating a layered representation of at least one image sequence, comprising using a computer to:
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acquire at least one image sequence, each image sequence having at least one image frame; automatically decompose each image sequence into a generative model, with each generative model including a set of model parameters that represent at least one image sprite and at least one image layer learned for each image sequence. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47)
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48. A computer-readable medium having computer executable instructions for automatically learning layered flexible image sprites from an image sequence, comprising:
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providing an input sequence of at least one image frame of a scene; automatically learning a probabilistic number of image sprite classes to be identified within the image sequence; automatically learning a probabilistic dimensionality of each image sprite class; automatically learning a probabilistic number of layers for the image sprite classes; and automatically learning at least one layered image sprite from the input sequence given the automatically learned number of image sprite classes, image sprite dimensionality, and image layers, wherein each image sprite represents an object in the input sequence. - View Dependent Claims (49, 50, 51, 52, 53, 54, 55, 56, 57, 58)
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Specification