Image Segmentation Correction for Recognition Accuracy
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Solution Overview
Problem
Existing image processing methods struggle to optimize training sets for recognition models, which affects the accuracy of object recognition in images.
Innovation Solution
An image processing method that involves obtaining a detection image and a marked image, applying an image segmentation model to segment the image, correcting the segmented image based on the marked image, and adjusting the image size to create a standard segmented image for training recognition models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used for training set optimization, then the process is simple, but the recognition accuracy is insufficient
Solution Approach 1:
The patent applies image segmentation to divide the detection image into multiple regions corresponding to different objects or parts. This segmentation enables the recognition model to focus on specific regions of interest, thereby improving recognition accuracy while providing a structured approach to handle complex images systematically
Solution Approach 2:
The patent performs preliminary image processing including segmentation and correction before the actual recognition task. By pre-processing the images to create standardized segmented images with corrected positions and sizes, the system prepares optimal training data in advance, which improves recognition accuracy without adding complexity during the recognition phase
2Measurement precision
If image segmentation and correction are applied to optimize training sets, then recognition accuracy improves, but processing time and complexity increase
Solution Approach 1:
The patent employs an image segmentation model that automatically segments and corrects images without requiring manual intervention. The model autonomously identifies regions, determines their positions and sizes, and generates standardized segmented images, thereby reducing processing time while maintaining high recognition accuracy through automated optimization of training sets
Data Source
AI summary
In an image processing method, a detection image and a marked image are obtained. An image segmentation model is applied to segment a first segmented image from the detection image. The first segmented image is corrected according to the marked image to obtain a second segmented image. A size of the second segmented image is adjusted to obtain an adjusted segmented image. The adjusted segmented image is used as a standard segmented image of the detection image. The method improves accuracy of image segmentation and recognition.