Multi-Stage Classifier for Image Segmentation
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Solution Overview
Problem
Existing image processing classifiers face challenges in accurately identifying distinctive regions while minimizing false positives and consuming significant processing resources, making them inefficient for real-time imaging applications.
Innovation Solution
A multi-stage classifier system comprising two component classifiers, where the first classifier has high sensitivity to identify target regions and the second classifier has high specificity to confirm the presence of the distinctive region, reducing complexity and processing burden by applying them sequentially or concurrently in an array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single complex classifier is used to achieve high accuracy in identifying distinctive regions, then measurement precision is improved, but device complexity increases and processing time is excessive
Solution Approach 1:
The patent divides a single complex classification task into two separate classification stages. The first classifier performs initial classification to identify candidate regions, and the second classifier performs refined classification to confirm the distinctive region. This segmentation reduces the complexity of each individual classifier while maintaining high overall classification accuracy.
2Measurement precision
If a single complex classifier is used to achieve high accuracy in identifying distinctive regions, then measurement precision is improved, but processing speed deteriorates
Solution Approach 1:
The patent divides a single complex classification task into two separate classification stages. The first classifier performs initial classification to identify candidate regions, and the second classifier performs refined classification to confirm the distinctive region. This segmentation reduces the complexity of each individual classifier while maintaining high overall classification accuracy.
3Measurement precision
If a single complex classifier is used to achieve high accuracy in identifying distinctive regions, then measurement precision is improved, but processing resources consumed increase
Solution Approach 1:
The patent divides a single complex classification task into two separate classification stages. The first classifier performs initial classification to identify candidate regions, and the second classifier performs refined classification to confirm the distinctive region. This segmentation reduces the complexity of each individual classifier while maintaining high overall classification accuracy.
4Measurement precision
If a single complex classifier is used to achieve high accuracy in identifying distinctive regions, then measurement precision is improved, but false positives increase
Solution Approach 1:
The patent divides a single complex classification task into two separate classification stages. The first classifier performs initial classification to identify candidate regions, and the second classifier performs refined classification to confirm the distinctive region. This segmentation reduces the complexity of each individual classifier while maintaining high overall classification accuracy.
Solution Approach 2:
The first classifier acts as an intermediary that filters the image data to identify candidate regions before they are passed to the second classifier. This intermediate filtering step reduces the number of false positives that reach the final classification stage, improving the overall reliability of the classification system.
Data Source
AI summary
The systems and methods described herein provide for fast and accurate image segmentation through the application of a multi-stage classifier to an image data set. An image processing system is provided having a processor configured to apply a multi-stage classifier to the image data set to identify a distinctive region. The multi-stage classifier can include two or more component classifiers. The first component classifier can have a sensitivity level configured to identify one or more target regions in the image data set and the second component classifier can have a specificity level configured to confirm the presence of the distinctive region in any identified target regions. Also provided is a classification array having multiple multi-stage classifiers for identification and confirmation of more than one distinctive region or for the application of different classification configurations to the image data set to identify a specific distinctive region.