Image Object Detection via Gradient Run Hierarchies
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Solution Overview
Problem
Current methods for analyzing and interpreting electronically acquired video imagery are inefficient in generating compact descriptions that accurately represent large numbers of images, leading to challenges in identifying objects within these images.
Innovation Solution
The method involves creating a compact description of pixel arrangements through gradient runs and Grenze Sets, using anchor primitives and decision trees to identify objects, and employing sensor fusion to synthesize data from multiple sources for accurate object detection and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current methods are used to analyze and interpret electronically acquired video imagery, then object identification can be performed, but the description of pixel arrangements is inefficient and not compact
Solution Approach 1:
The patent segments the image analysis process into distinct hierarchical levels: pixels are grouped into gradient runs, gradient runs are organized into Grenze Sets, and Grenze Sets are structured into primitives. This segmentation creates a compact descriptive hierarchy that reduces information loss while maintaining manageable complexity at each level.
Solution Approach 2:
The patent introduces a new dimensional organization by arranging gradient runs in specific spatial relationships (e.g., parallel arrangements, T-junctions, cross junctions) to form Grenze Sets. This dimensional structuring transforms raw pixel data into organized geometric primitives, achieving compact description without excessive complexity.
2Productivity
If a compact description of pixel arrangements is created, then descriptive efficiency is improved, but the ability to accurately identify objects may be compromised
Solution Approach 1:
The patent performs preliminary organization of pixels into gradient runs and Grenze Sets before final object identification. This preliminary structuring preserves essential geometric and spatial relationships while creating a compact description, enabling accurate object identification without sacrificing descriptive efficiency.
Solution Approach 2:
The patent introduces intermediate structures (gradient runs and Grenze Sets) that mediate between raw pixel data and final object identification. These intermediaries maintain the necessary information for accurate object recognition while providing the compact hierarchical organization needed for descriptive efficiency.
3Speed
If pixels are grouped into gradient runs and Grenze Sets for efficient description, then processing speed is improved, but the complexity of validating primitives increases
Solution Approach 1:
The patent employs dynamic validation where the complexity of primitive validation adapts to the specific image content and detected features. Decision trees and predefined rules are applied selectively based on the hierarchical structure discovered in the image, enabling fast processing while managing validation complexity dynamically rather than through static rigid processes.
Data Source
AI summary
Vision pattern recognition is used to discover the presence, position, orientation, and movement of physical objects within electronic representations of data such as images based on the objects' appearance, according to certain embodiments.