Feature Density Classification for Region-Specific Object Recognition
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Solution Overview
Problem
Existing object recognition technologies are inefficient across diverse classes of objects due to high variability in feature density, often relying on a single algorithm approach that fails to account for the specific processing requirements of different types of objects.
Innovation Solution
A system comprising a plurality of recognition modules and a data preprocessing module that identifies regions of interest in a digital representation by applying invariant feature identification algorithms, clustering features, and assigning appropriate recognition algorithms based on feature density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single recognition algorithm is applied to all regions, then the device complexity is reduced, but the recognition accuracy deteriorates due to high variability in feature density across different object types
Solution Approach 1:
The image is divided into multiple regions of interest based on feature density clustering. Each region is independently analyzed and assigned appropriate recognition algorithms based on its specific characteristics. This segmentation allows different processing strategies to be applied to different parts of the image, improving overall recognition accuracy while managing complexity through localized processing.
Solution Approach 2:
Different recognition algorithms are assigned to different regions based on their feature density characteristics. High feature density regions receive different processing than low feature density regions. This local quality approach ensures that each region receives the most appropriate processing method for its specific properties, maximizing recognition accuracy without requiring all possible algorithms to be applied everywhere.
2Measurement precision
If multiple recognition algorithms are applied to each region, then the recognition accuracy improves, but the processing time increases
Solution Approach 1:
Feature density clustering is performed as a preliminary step before applying recognition algorithms. This preliminary action identifies and groups regions with similar characteristics, allowing the system to pre-determine which algorithms will be most effective for each region. This avoids the need to trial multiple algorithms and reduces overall processing time while maintaining high recognition accuracy.
Solution Approach 2:
Instead of applying all possible recognition algorithms to all regions, the system applies only the necessary subset of algorithms to each specific region based on its feature density characteristics. This partial action approach avoids unnecessary processing in regions where certain algorithms would be ineffective, significantly reducing total processing time while maintaining recognition accuracy through targeted algorithm selection.
3Adaptability or versatility
If feature density clustering is performed, then the adaptability to different object types improves, but the computational resources required increase
Solution Approach 1:
The system changes the parameter of feature density as the basis for clustering and algorithm selection. By using feature density as the key parameter, the system can adapt to different object types without requiring complex models for each object class. This parameter-based approach provides high adaptability while keeping computational resources manageable through efficient clustering algorithms.
Data Source
AI summary
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.