Two-Stage Image Region Recognition for Accurate Segmentation
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Solution Overview
Problem
Current image segmentation technologies often produce inaccurate results that require extensive user correction, leading to inefficiencies in obtaining precise image segmentation and recognition.
Innovation Solution
A two-stage image segmentation process using a first image segmentation model followed by a second model with user-provided annotations to refine the initial segmentation, improving accuracy without extensive manual correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-stage image segmentation model is used, then the process is simple and fast, but the segmentation accuracy is insufficient and requires extensive user correction
Solution Approach 1:
The patent divides the image segmentation process into two distinct stages: a first-stage segmentation model that performs initial segmentation, and a second-stage auxiliary segmentation model that refines the results. This multi-stage approach resolves the contradiction by improving accuracy through sequential processing while keeping each individual stage relatively simple.
Solution Approach 2:
The first-stage segmentation model performs preliminary segmentation before the second-stage model refines the results. This preliminary action allows the system to establish a baseline segmentation that can then be improved, reducing the need for extensive user correction while maintaining a manageable process complexity.
2Measurement precision
If extensive user correction is applied to improve segmentation accuracy, then the segmentation precision improves, but the time and effort required increases significantly
Solution Approach 1:
The system performs self-correction through the second-stage auxiliary segmentation model, which automatically refines the segmentation results from the first stage. This self-service mechanism improves accuracy without requiring extensive manual user correction, thereby reducing the time and effort loss.
Solution Approach 2:
The second-stage model uses the segmentation results from the first stage as input and provides refined output, creating a feedback loop that automatically improves accuracy. This automated feedback mechanism eliminates the need for time-consuming manual correction while maintaining high segmentation precision.
3Measurement precision
If a two-stage segmentation process is implemented, then the segmentation accuracy improves, but the system complexity and computational resources increase
Solution Approach 1:
The patent implements a two-stage segmentation system where each stage has a specific function: the first stage performs initial segmentation and the second stage performs auxiliary refinement. This segmentation of functions improves accuracy while managing system complexity by dividing the overall task into specialized sub-tasks.
Solution Approach 2:
The second-stage auxiliary segmentation model serves multiple purposes: it refines segmentation boundaries, corrects errors from the first stage, and can adapt to different types of images. This multi-functionality justifies the increased system complexity by providing comprehensive improvement in segmentation accuracy across various scenarios.
Data Source
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AI summary
An image region recognition method, comprising: obtaining an image to be segmented, wherein said image comprises a plurality of extreme points (101); generating first image feature information according to said image, wherein the first image feature information comprises N image matrices and a first heat map, the first heat map being generated according to the plurality of extreme points (102); obtaining, by means of a first image segmentation model, a first image segmentation region corresponding to the first image feature information (103); obtaining a second heat map according to annotation points corresponding to the first image segmentation region, and the first image segmentation region (104); obtaining, by means of a second image segmentation model, a second image segmentation region corresponding to said image (105); and generating an image recognition result of said image according to the second image segmentation region (106). According to the method, by means of auxiliary segmentation in the second stage, the region having a poor effect in the image segmentation in the first stage is further segmented, so as to obtain a more accurate image segmentation result, thereby improving the performance of image segmentation.