Feature Extraction Validation Using Screen Page Segmentation
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Solution Overview
Problem
Manual analysis of high-resolution imagery data for feature extraction is time-consuming, costly, and prone to human error, as operators need to manually pan and zoom through large images to verify feature identification.
Innovation Solution
Implementing systems and methods for efficient feature data analysis by determining the minimal number of screen pages needed to verify feature identification, selecting appropriate validation processes based on the number of features, and performing these processes to ensure accurate classification, using data-driven pan and zoom operations and parallel processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is performed by operators panning and zooming through high-resolution imagery, then feature identification accuracy can be verified, but the process becomes time-consuming and costly
Solution Approach 1:
The patent divides the large high-resolution image into multiple smaller screen pages or tiles that can be processed and reviewed independently. This segmentation allows automated extraction to work on specific regions without requiring manual review of the entire large image, reducing time while maintaining accuracy through targeted verification of extracted features in each segment
Solution Approach 2:
The system performs automated feature extraction and identification before manual verification. By pre-processing the imagery to identify and extract potential features, the system reduces the workload for operators who only need to verify pre-identified candidates rather than manually searching through entire images, thus reducing time while maintaining accuracy
2Measurement precision
If operators manually pan and zoom through large images to verify each feature, then accurate classification can be achieved, but the complexity and cost of the process increases
Solution Approach 1:
The patent introduces an automated feature extraction system as an intermediary between the raw imagery and manual verification. This intermediary performs initial feature identification, clustering, and candidate selection, providing a structured set of pre-processed results that simplify the subsequent manual verification process while maintaining classification accuracy
Solution Approach 2:
The system applies different processing and verification strategies to different regions or types of features based on their characteristics. High-confidence features may require minimal verification while low-confidence or complex features receive more thorough review, optimizing the balance between accuracy and process complexity by tailoring verification effort to local feature qualities
3Measurement precision
If comprehensive manual verification of all detected features is performed, then high accuracy can be ensured, but productivity decreases
Solution Approach 1:
The patent implements partial verification by selecting only a subset of detected features for manual review based on confidence scores, feature importance, or other criteria. Instead of verifying all detected features, the system focuses manual effort on the most critical or uncertain cases, maintaining high overall accuracy while significantly increasing productivity through selective verification
Solution Approach 2:
The system replaces manual mechanical review of all features with automated computational methods for initial extraction, filtering, and prioritization. This substitution of automated algorithms for manual mechanical processes enables high-volume processing while reserving human judgment only for cases where it adds the most value, thereby increasing productivity without sacrificing accuracy
Data Source
AI summary
Systems (100) and methods (300) for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.