Method and apparatus for acquisition, compression, and characterization of spatiotemporal signals
First Claim
1. A method of evaluating a dynamic system, comprising:
- a. acquiring a plurality of images representative of the dynamic system in two or more dimensions using at least one sensor;
b. determining self-similarity among a representative set of images using an unsupervised algorithm defined as being absent a previously known model, wherein the unsupervised algorithm is implemented on at least one data processing device; and
c. characterizing the set of images as a statistical function of self-similarity;
thereby evaluating the dynamic system.
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Abstract
The present invention provides methods and apparatus for acquisition, compression, and characterization of spatiotemporal signals. In one aspect, the invention assesses self-similarity over the entire length of a spatiotemporal signal, as well as on a moving attention window, to provide cost effective measurement and quantification of dynamic processes. The invention also provides methods and apparatus for measuring self-similarity in spatiotemporal signals to characterize, adaptively control acquisition and/or storage, and assign meta-data for further detail processing. In some embodiments, the invention provides for an apparatus adapted for the characterization of biological units, and methods by which attributes of the biological units can be monitored in response to the addition or removal of manipulations, e.g., treatments. The attributes of biological units can be used to characterize the effects of the abovementioned manipulations or treatments as well as to identify genes or proteins responsible for, or contributing to, these effects.
60 Citations
13 Claims
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1. A method of evaluating a dynamic system, comprising:
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a. acquiring a plurality of images representative of the dynamic system in two or more dimensions using at least one sensor; b. determining self-similarity among a representative set of images using an unsupervised algorithm defined as being absent a previously known model, wherein the unsupervised algorithm is implemented on at least one data processing device; and c. characterizing the set of images as a statistical function of self-similarity; thereby evaluating the dynamic system. - View Dependent Claims (2, 3, 4)
the acquisition parameterization is adjusted as a function of the self-similarity of the images.
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5. A method of evaluating a dynamic system, comprising:
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a. acquiring a plurality of images representative of the dynamic system in two or more dimensions using at least one sensor; b. determining self-similarity among a representative set of images using an unsupervised algorithm defined as being absent a previously known model, wherein the unsupervised algorithm is implemented on at least one data processing device, wherein determining self-similarity includes estimating a short-term temporal similarity and a long-term temporal similarity; and c. characterizing the set of images as a statistical function of self-similarity, thereby evaluating the dynamic system. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13)
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Specification