Image Tracking Effectiveness Evaluation via Texture Analysis
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Solution Overview
Problem
The existing methods for evaluating image tracking effectiveness are inefficient and lag behind product manufacturing, requiring actual product models and online testing to determine tracking effectiveness.
Innovation Solution
A method involving extracting feature points from a target image using predefined algorithms, determining texture information, processing the image to derive contrast information, and rating tracking effectiveness based on both, allowing for evaluation without actual product placement or finalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the target image is applied to an actual product before evaluating tracking effectiveness, then the evaluation is reliable, but the evaluation efficiency is low and lags behind product manufacturing
Solution Approach 1:
The patent performs tracking effectiveness evaluation on the target image before it is applied to the actual product. By extracting feature points and calculating texture information metrics (texture richness, texture distribution, texture repetition) in advance, the system enables preliminary assessment that does not depend on product finalization, thereby improving evaluation efficiency while maintaining reliability through comprehensive metric analysis
Solution Approach 2:
The patent creates a virtual evaluation environment by processing the target image independently without requiring the actual product or online model. It generates contrast images through image processing and evaluates tracking effectiveness based on extracted feature points and texture metrics, effectively copying the essential evaluation function to a standalone offline system
2Measurement precision
If the product model needs to be online for image testing, then the evaluation is accurate, but the evaluation process becomes complex and time-consuming
Solution Approach 1:
The patent extracts the essential evaluation function from the complex online product testing system. By isolating the target image and evaluating it independently through feature point extraction and texture metric calculation, the system removes the dependency on online product models and complex testing infrastructure, simplifying the evaluation process while maintaining measurement accuracy
Solution Approach 2:
The patent introduces texture information metrics as an intermediary between the target image and the final tracking effectiveness evaluation. These metrics (texture richness, distribution, repetition) serve as intermediate representations that capture the essential tracking characteristics without requiring direct interaction with complex product models or online systems
Data Source
AI summary
The present disclosure provides a method, an apparatus, a device for evaluating image tracking effectiveness and a readable storage medium. The method includes: extracting feature points from a target image according to a predefined algorithm; determining first texture information for the target image according to the feature points; processing the target image according to a predefined processing policy to derive a contrast image, and determining second texture information for the target image according to the contrast image; and rating the target image according to the first texture information and the second texture information. In the solution provide by the present disclosure, the tracking effectiveness for the target image can be known without having to place the target image into any actual product, nor having to wait until the product has been finalized before the target image tracking effectiveness is determined. Moreover, efficiency can be enhanced when evaluating image tracking effectiveness.