Collectable Image Grading via Segmentation and ML Models
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Solution Overview
Problem
Existing methods for grading images of collectables are labor-intensive and costly, lacking efficiency and accuracy in assessing the value of assets.
Innovation Solution
A method involving image preprocessing, where boundaries of collectables are detected, perspective warp transformations are applied, and unnecessary portions are removed, followed by training machine learning models on these preprocessed images to generate grades for surface, edge, corner, and centering conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual grading methods are used to assess collectables, then detailed evaluation of surface, edge, corner, and centering conditions can be performed, but the process becomes labor-intensive and costly
Solution Approach 1:
The patent replaces manual mechanical grading processes with an automated image analysis system using machine learning models. The system captures images of collectables and uses trained models to automatically evaluate surface conditions, edge conditions, corner conditions, and centering conditions, substituting human expert assessment with computational analysis to improve both speed and consistency.
Solution Approach 2:
The patent creates digital copies (images) of physical collectables and performs grading operations on these copies rather than handling the physical objects. This allows multiple analyses to be performed on the same object without risk of damage, and enables parallel processing of multiple collectables, significantly improving productivity while maintaining grading precision.
2Productivity
If automated image analysis is implemented for grading collectables, then grading speed and efficiency improve, but the complexity of the system increases
Solution Approach 1:
The patent divides the grading task into separate specialized machine learning models for different aspects: surface condition assessment, edge condition assessment, corner condition assessment, and centering evaluation. Each model is trained independently on specific features, and their results are combined to produce the overall grade. This segmentation reduces the complexity of individual models while maintaining high grading speed and accuracy.
Solution Approach 2:
The patent performs preliminary image preprocessing steps including capturing multiple images, selecting the best image quality, and preparing images for analysis before applying the grading models. This preliminary preparation ensures that the automated system receives optimized input data, reducing the complexity required in the actual grading algorithms while improving overall system efficiency.
3Reliability
If multiple grade classification labels are assigned to each image (surface, edge, corner, centering conditions), then comprehensive assessment is achieved, but the amount of data processing and model training increases
Solution Approach 1:
The patent implements separate machine learning models for each grading dimension (surface, edge, corner, centering), allowing parallel processing of different aspects simultaneously. Each model processes its specific feature set independently and produces results that are combined to form the comprehensive grade, reducing overall processing time while maintaining thorough assessment.
Solution Approach 2:
The patent focuses each specialized model on its specific aspect of grading rather than requiring a single model to analyze all features. This partial specialization allows each model to be more efficient and faster at its specific task, and the combination of multiple quick specialized assessments equals a comprehensive evaluation completed in less time than a single general-purpose analysis would require.
Data Source
AI summary
In some embodiments, a method can include augmenting a set of images of collectables to generate a set of synthetic images of collectables. The method can further include combining the set of images of collectables and the set of synthetic images of collectables to produce a training set. The method can further include training a set of machine learning models based on the training set. Each machine learning model from the set of machine learning models can generate a grade for an image attribute from a set of image attributes. The set of image attributes can include an edge, a corner, a center, or a surface. The method can further include executing, after training, the set of machine learning models to generate a set of grades for an image of collectable not included in the training set.