Feature density object classification, systems and methods
First Claim
1. An object data processing system comprising:
- at least one processor configured to execute;
a plurality of diverse recognition modules stored on at least one non-transitory computer-readable storage medium;
each recognition module comprising at least one recognition algorithm and having feature density selection criteria wherein the feature density selection criteria include rules that operate as a function of at least features per unit area; and
a data preprocessing module executed by at least one processor;
the data preprocessing module comprising an invariant feature identification algorithm and configured to;
obtain a digital representation of a scene;
generate a set of invariant features by applying the invariant feature identification algorithm to the digital representation;
cluster the set of invariant features into regions of interest in the digital representation of the scene, each region of interest having a region feature density;
assign each region of interest at least one recognition module from the plurality of diverse recognition modules as a function of the region feature density of each region of interest and the feature density selection criteria of the plurality of diverse recognition modules; and
configure the assigned recognition modules to process their respective regions of interest, wherein the data preprocessing module is further configured to assign each region of interest at least one recognition module as a function of a scene context derived from the digital representation; and
wherein the scene context includes a type of data relating to a medical event.
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Abstract
A system capable of determining which recognition algorithms should be applied to regions of interest within digital representations is presented. A preprocessing module utilizes one or more feature identification algorithms to determine regions of interest based on feature density. The preprocessing modules leverages the feature density signature for each region to determine which of a plurality of diverse recognition modules should operate on the region of interest. A specific embodiment that focuses on structured documents is also presented. Further, the disclosed approach can be enhanced by addition of an object classifier that classifies types of objects found in the regions of interest.
61 Citations
35 Claims
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1. An object data processing system comprising:
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at least one processor configured to execute; a plurality of diverse recognition modules stored on at least one non-transitory computer-readable storage medium;
each recognition module comprising at least one recognition algorithm and having feature density selection criteria wherein the feature density selection criteria include rules that operate as a function of at least features per unit area; anda data preprocessing module executed by at least one processor;
the data preprocessing module comprising an invariant feature identification algorithm and configured to;
obtain a digital representation of a scene;
generate a set of invariant features by applying the invariant feature identification algorithm to the digital representation;
cluster the set of invariant features into regions of interest in the digital representation of the scene, each region of interest having a region feature density;assign each region of interest at least one recognition module from the plurality of diverse recognition modules as a function of the region feature density of each region of interest and the feature density selection criteria of the plurality of diverse recognition modules; and
configure the assigned recognition modules to process their respective regions of interest, wherein the data preprocessing module is further configured to assign each region of interest at least one recognition module as a function of a scene context derived from the digital representation; and
wherein the scene context includes a type of data relating to a medical event. - 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, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35)
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Specification