Grenze Set Image Analysis Using Decision Trees
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Solution Overview
Problem
Existing methods fail to effectively identify and organize objects and activities within electronically acquired images by analyzing pixels, especially in complex scenarios involving shape, position, and movement, particularly in video imagery.
Innovation Solution
The use of decision trees to characterize and search time series images by grouping pixels into gradient representations and primitives, applying transition rules and error correction mechanisms to identify objects and activities based on shape, position, and movement, while employing a ladder of abstraction for robust analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to identify objects and activities in images, then the process is simple, but the identification accuracy and reliability are insufficient
Solution Approach 1:
The patent segments the image analysis process into multiple hierarchical levels: pixel-level gradient calculation, gradient run grouping into Grenze sets, primitive identification from Grenze sets, and object/activity recognition from primitives. This multi-level segmentation allows complex analysis to be broken down into manageable stages, improving identification accuracy while organizing complexity systematically
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional image analysis by creating multiple levels of abstraction from pixels to gradient runs to Grenze sets to primitives to objects. This dimensional hierarchy transforms the analysis from a single-level process to a multi-layered framework, enabling more reliable identification through progressive refinement
2Reliability
If pixel analysis is performed to identify shapes in images, then object identification is possible, but the analysis reliability is insufficient for complex scenarios
Solution Approach 1:
The patent performs preliminary actions by calculating gradient information for each pixel before proceeding to higher-level analysis. Gradient runs are pre-computed and grouped into Grenze sets before primitive identification occurs. This preliminary processing of gradient data establishes a solid foundation that improves detection reliability while organizing the complexity of subsequent shape analysis
Solution Approach 2:
The patent introduces intermediate structures (gradient runs and Grenze sets) that mediate between raw pixel data and final object identification. These intermediary representations serve as bridge concepts that transform complex pixel patterns into more manageable forms, making detection more reliable without overwhelming the analysis system
Data Source
AI summary
An apparatus and method is disclosed for acquiring an electronic image and forming at least one Grenze Set including pixels of the electronic image. A decision tree is used to apply vocabulary and rules associated with a primitive to evaluate pixels of the Grenze Set. The pixels of the Grenze Set are explained by re-building the Grenze Set using a set of sub-primitives. Higher order analysis are applied to the Grenze Set according to a ladder of abstraction to assemble pixels into at least one of objects or activities that are meaningful to a user.