Perceptual Grouping and Geometric Models for Object Recognition
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Solution Overview
Problem
Current object detection and recognition techniques in computer systems are inadequate for accurately identifying objects in images, as they fail to effectively partition images into meaningful regions and utilize fixed-size models that do not align with the image content.
Innovation Solution
A computing device employs a combination of perceptual grouping and geometric-configuration models to partition images into disjoint pixel groups and bins, respectively, allowing for a strong membership measurement of pixels within these groups, which enhances object detection and recognition algorithms by providing higher-order information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current object detection and recognition techniques are used, then the processing can be performed with simple methods, but the accuracy of object detection and recognition is insufficient
Solution Approach 1:
The image is partitioned into multiple disjoint groups of pixels based on perceptual grouping principles, where pixels within each group share similar visual characteristics. This segmentation allows the system to process meaningful regions rather than individual pixels, improving detection accuracy while managing complexity through hierarchical organization
Solution Approach 2:
The patent introduces a new dimensional framework by combining perceptual grouping (content-based) with geometric-configuration models (spatial arrangement). This multi-dimensional approach captures both what pixels represent and where they are located, enabling more accurate object recognition without proportionally increasing processing complexity
2Adaptability or versatility
If fixed-size geometric models are used, then the processing framework is simple, but the models do not align with the actual image content
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on perceptual grouping. Each disjoint pixel group is processed according to its specific characteristics and geometric configuration, allowing the system to adapt to local content variations rather than applying uniform fixed-size models throughout
Solution Approach 2:
The system dynamically adjusts the grouping and geometric configuration models based on the actual image content. Rather than using static fixed-size models, the perceptual grouping adapts to the distribution and characteristics of pixels in different regions, enabling content-aligned processing that responds to the specific structure of each image
Data Source
AI summary
Methods and systems for processing an image to facilitate automated object recognition are disclosed. More particularly, an image is processed based on a perceptual grouping for the image (e.g., derived via segmentation, derived via contour detection, etc.) and a geometric-configuration model for the image (e.g., a bounding box model, a constellation, a k-fan, etc.).