Machine Learning Range Estimation for Printed Matter Edge Detection
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Solution Overview
Problem
Conventional edge detection methods for extracting images of printed matter from captured images are prone to erroneous detection and lack sufficient accuracy, particularly when dealing with faded or dark-colored regions.
Innovation Solution
An image processing apparatus employing a machine learning-based range estimation model to accurately identify the printed matter by setting a wider detection range and using edge detection algorithms to distinguish between the printed matter and adjacent dark-colored regions, thereby enhancing detection precision and reducing erroneous extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection methods are used to extract printed matter from captured images, then the extraction process is simple, but the detection accuracy is low and erroneous detection occurs frequently
Solution Approach 1:
The patent segments the edge detection process into multiple distinct stages: initial edge detection to identify candidate regions, classification to distinguish printed matter edges from dark-colored region edges, and final edge selection. This segmentation allows each stage to focus on specific tasks, improving overall detection accuracy while managing complexity through modular processing
Solution Approach 2:
The patent introduces an intermediary classification step between initial edge detection and final edge selection. This classification process acts as a mediator that filters candidate edges through multiple criteria (color information, positional relationships, continuity) before confirming them as printed matter edges, thereby reducing erroneous detection without requiring complete redesign of the detection system
2Area of stationary object
If the detection range is expanded to include more areas, then the printed matter can be fully captured, but the likelihood of detecting dark-colored regions as false edges increases
Solution Approach 1:
The patent applies local quality analysis by examining specific properties of edge candidates in different regions. The classification process evaluates color information, positional relationships, and continuity characteristics locally at each candidate edge position, allowing the system to distinguish between valid printed matter edges and false dark-colored region edges even within an expanded detection range
Solution Approach 2:
The patent changes multiple parameters simultaneously to improve reliability: color space parameters (using multiple color spaces for classification), spatial parameters (positional relationships between edges and candidate regions), and continuity parameters (whether edges form continuous boundaries). These parameter changes enable reliable distinction between printed matter and dark-colored regions across the expanded detection area
Data Source
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AI summary
Provided are an image processing apparatus, an image processing method, a program, and a recording medium for more appropriately extracting an image corresponding to a printed matter from a captured image of the printed matter by solving the above-described problem of the related art. An image processing apparatus that extracts an image corresponding to a printed matter from a captured image includes a processor, in which the processor executes an estimation process of estimating a first range including the printed matter in the captured image by applying a range estimation model constructed by machine learning regarding the printed matter to the captured image.