Segmentation Output Quality Prediction From Detection Score Maps
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Solution Overview
Problem
Existing detection and segmentation algorithms often produce poor quality outputs, especially when analyzing data far from their training dataset distribution, leading to reduced user confidence in their reliability.
Innovation Solution
A system and method that analyzes the intermediate output of a detection and segmentation algorithm at multiple operating points to compute features, using a classifier to predict whether the final output will meet a detection precision threshold, preventing poor quality outputs from being shown to the end user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If detection and segmentation algorithms are applied to data far from training dataset distribution, then the algorithms can handle diverse input scenarios, but the output quality deteriorates and reliability decreases
Solution Approach 1:
The patent applies preliminary action by computing features from the detection score map at multiple operating points before final segmentation. This intermediate analysis allows the system to predict output quality in advance and identify cases where the algorithm may fail, enabling proactive quality control before poor results are generated
Solution Approach 2:
The patent introduces an intermediary quality assessment mechanism that analyzes the detection score map at multiple operating points. This intermediary analysis acts as a mediator between the detection stage and final segmentation, providing quality predictions that prevent poor outputs from being generated in the first place
2Measurement precision
If the system analyzes detection score map at multiple operating points to compute features, then the quality prediction accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by selecting specific operating points for analysis rather than exhaustively analyzing all possible thresholds. By computing features at strategically chosen operating points, the system achieves sufficient quality prediction accuracy without the excessive computational burden of complete analysis
Solution Approach 2:
The patent utilizes parameter changes by varying the operating points (thresholds) at which the detection score map is analyzed. By computing features across multiple threshold values, the system captures different aspects of detection quality, improving prediction accuracy while managing computational complexity through selective parameter variation
3Reliability
If the system prevents poor quality outputs from being displayed, then user confidence in reliability improves, but the productivity decreases due to additional quality checking steps
Solution Approach 1:
The patent applies preliminary action by performing quality assessment during the detection stage before final segmentation is generated. By computing features and making quality predictions at this intermediate stage, the system identifies poor-quality cases early, preventing unnecessary processing and avoiding the generation of low-confidence outputs
Data Source
AI summary
In an approach for automatically detecting whether an output of a detection and segmentation algorithm is of an acceptable quality, a processor receives an image. A processor applies a detection stage of a detection and segmentation algorithm to the image. A processor computes a set of features from a detection score map output by the detection stage of the detection and segmentation algorithm by analyzing the detection score map at more than one different operating points. A processor inputs the set of features into a classifier that predicts whether a final output of the detection and segmentation algorithm will be of an acceptable quality, wherein the acceptable quality is defined based on whether a detection precision threshold has been reached. A processor receives an output of the classifier.