Captured Image Detection Using Region-Based Noise Filtering
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Solution Overview
Problem
Existing image processing algorithms struggle to accurately distinguish between minute detection targets and noise in captured images, particularly in the presence of high-density regions, leading to reduced accuracy and unstable detection performance.
Innovation Solution
A detection system that includes a candidate generator, a pre-processor, and a detector, which uses machine learning models to identify and exclude noise regions based on image features and inhibition factors, allowing for precise detection of minute targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing algorithms such as particle analysis are used to detect minute detection targets in captured images, then detection capability is provided, but the algorithms cannot distinguish between minute particulate detection targets and noise, leading to reduced accuracy
Solution Approach 1:
The detection system segments the image processing task into multiple specialized components: a candidate generator that identifies potential detection targets, a pre-processor that divides the image into small regions and evaluates noise characteristics, and a detector that performs final classification. This segmentation allows each component to specialize in specific aspects of the detection problem, improving overall accuracy by systematically addressing the difficulty of distinguishing targets from noise.
2Adaptability or versatility
If classification is performed using observation instruments at magnification that allows observers to classify cell types, then cell type classification is enabled, but minute objects in the captured image containing large amounts of noise cannot be properly detected
Solution Approach 1:
The system transitions from relying solely on visual observation at a single magnification level to multi-dimensional analysis by: (1) processing images at the original capture magnification, (2) generating synthetic high-magnification views through super-resolution processing, and (3) evaluating noise characteristics across multiple spatial scales. This dimensional expansion enables accurate detection of minute objects by analyzing them from multiple perspectives simultaneously.
3Productivity
If detection is performed in high-density regions where cells and tissues are densely distributed, then comprehensive coverage is achieved, but the visibility of the minute shape of the detection target is reduced by subjects other than the detection target, inhibiting detection and making performance unstable
Solution Approach 1:
The system applies local quality analysis by dividing the captured image into multiple small regions and independently evaluating the noise characteristics and detection suitability of each region. The pre-processor assesses local density and noise levels in each small region, allowing the detector to adapt its detection strategy to local conditions. This enables reliable detection in high-density regions by treating each local area with appropriate detection parameters rather than applying uniform processing across the entire image.
Data Source
AI summary
A detection system includes: a candidate generator; a pre-processor; and a detector. The candidate generator generates candidates for a detection target image, in which a detection target is captured, from a captured image for detection that includes an image of an object having the detection target and a noise image. The pre-processor that divides the captured image for detection into small regions and determines whether or not each small region image has features of subjects other than the object having the detection target based on an image feature amount of the small region image to obtain an exclusion region in the captured image based on a determination result. The detector detects the detection target image from among the candidates for the detection target image that are not included in the exclusion region by predetermined detection processing.