Template-Guided Edge Curve Fitting for Industrial Image Positioning
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Solution Overview
Problem
Existing edge detection methods in image processing, particularly for industrial products, suffer from inaccuracies due to highlight or dirty regions, leading to incorrect edge curve fitting and reduced accuracy and stability.
Innovation Solution
An image processing method and apparatus that utilizes a template image to determine an object detection region, performs pixel position transformation to identify a local mapped region, and then performs edge curve fitting on a foreground region to improve accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge detection is performed using discontinuity of grayscale values, then the edge curve can be determined through segmentation, but edge misdetection occurs easily due to highlight regions, dirty regions, or color similarity with background
Solution Approach 1:
The patent uses a template image containing a template object with known geometric information to copy and transfer accurate edge curve information to the service image. The template object's precise edge characteristics are replicated onto the target object in the service image, overcoming the limitations of direct grayscale-based edge detection on the service image alone.
Solution Approach 2:
The template image serves as an intermediary that bridges the gap between the service image and the desired accurate edge detection. By combining template information with service image data through pixel position transformation and curve fitting, the system achieves reliable edge detection without being directly affected by highlights, dirt, or color similarities in the service image.
2Measurement precision
If the entire service image is processed for edge detection, then comprehensive coverage is achieved, but processing time increases and accuracy decreases due to irrelevant regions
Solution Approach 1:
The patent segments the service image into a relevant region of interest (ROl) based on the template object's position and size. By determining the ROl coordinates through pixel position transformation from the template image, the system processes only the necessary portion of the service image, significantly reducing processing time while maintaining edge detection accuracy.
Solution Approach 2:
Instead of processing the entire service image, the patent applies partial action by focusing computation only on the ROl that corresponds to the template object. This selective processing approach reduces computational burden while providing sufficient accuracy for the edge curve fitting task.
Data Source
AI summary
In an image processing method, a service image including a target object and a template image including a template object are obtained. An object detection region corresponding to the template object is determined. A matched region in the service image is identified as an initial positioned region of the target object based on the object detection region. A pixel position transformation relationship is determined based on pixel position of pixels in the object detection region and the initial positioned region. A local mapped region in the service image is determined based on the pixel position transformation relationship. In a target positioned image corresponding to a target positioned region in the service image based on the local mapped region, a foreground region corresponding to the target object is identified. An edge curve of the target object is obtained based on performing edge curve fitting processing on pixels in the foreground region.