Spatial-Neighborhood Consistency in Image Feature Detection
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Solution Overview
Problem
Current computer vision systems for image feature detection face challenges in accurately identifying defects or anomalies, particularly in borderline cases, due to reliance on fixed thresholds and lack of spatial consistency, leading to false positives and incomplete detection coverage.
Innovation Solution
The system employs a method that calculates feature-detection scores for image areas, sorts them based on spectral and spatial thresholds, and uses neighborhood kernels to assign detection labels, incorporating a penalty-based approach to improve labeling accuracy and consistency across spatially adjacent areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed thresholds are used for feature detection, then the detection process is simple and fast, but the accuracy deteriorates due to false positives and incomplete detection coverage
Solution Approach 1:
The patent segments the image into multiple spatial neighborhoods and processes each neighborhood independently with its own threshold. This allows the system to maintain simplicity within each local region while achieving higher overall accuracy through localized processing, resolving the contradiction between detection accuracy and process complexity.
Solution Approach 2:
The patent applies different threshold values to different spatial neighborhoods based on their specific characteristics and requirements. Each neighborhood receives customized threshold parameters that optimize detection accuracy for that local region, enabling the system to achieve high precision without requiring a completely complex global detection process.
2Reliability
If spatial neighborhood information is incorporated, then the consistency and reliability of detection improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the image into discrete spatial neighborhoods and processes them in parallel, which reduces the computational complexity of incorporating spatial information. By segmenting the problem, the system can leverage spatial neighborhood relationships to improve reliability while avoiding the need for complex global optimization procedures.
3Productivity
If multiple thresholds and neighborhood kernels are used, then the detection coverage and true positives increase, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary processing by pre-calculating and storing neighborhood kernel values and threshold parameters before the main detection process. This preliminary action enables the system to achieve comprehensive detection coverage using multiple thresholds while reducing the processing time during actual image analysis, as the complex computations have already been performed in advance.
Data Source
AI summary
Devices, systems, and methods obtain respective corresponding feature-detection scores for a plurality of areas in an image; calculate respective corresponding sorting scores for at least some areas of the plurality of areas; for the at least some areas of the plurality of areas, arrange the corresponding feature-detection scores in order of the corresponding sorting scores, thereby generating an order of sorted feature-detection scores; and assign respective detection scores to the at least some areas based on the order of sorted feature-detection scores and on three or more of the following: the respective corresponding feature-detection scores of the areas, a spectral threshold, a spatial threshold, and a neighborhood kernel.