Ear Impression Feature Classification via Shape Context Analysis
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Solution Overview
Problem
The manual identification of features on ear impressions for hearing aid shell design is time-consuming and costly, limiting the efficiency of computer-assisted manufacturing processes.
Innovation Solution
Automatically labeling points on an ear impression model by determining shape contexts and comparing them to average shape contexts from a reference ear impression atlas, using a cost function to classify points into anatomical feature regions, thereby reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of features on ear impressions is used, then feature identification accuracy can be achieved, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical identification of ear features with an automated computer-based system that uses shape context analysis and image processing algorithms to automatically identify and classify anatomical features on ear impressions, eliminating the need for manual inspection while maintaining identification accuracy
Solution Approach 2:
The system enables the ear impression data to self-identify features through automated shape context comparison and classification algorithms, where the computational system processes the feature identification without requiring human intervention, making the system self-sufficient in performing the identification task
2Manufacturing precision
If manual processing of ear impressions is used, then detailed feature analysis can be performed, but manufacturing efficiency decreases
Solution Approach 1:
The patent replaces manual processing operations with automated computer-based image processing and shape analysis algorithms that can analyze ear impression features at high speed while maintaining detailed analysis capability through sophisticated computational methods
Solution Approach 2:
The system performs preliminary automated classification and identification of ear features before the manufacturing process begins, preparing the data in advance for subsequent manufacturing steps, which improves overall manufacturing efficiency by eliminating manual processing bottlenecks
3Productivity
If automated scanning is used to create digitized models, then manufacturing speed increases, but automatic feature classification remains challenging
Solution Approach 1:
The patent replaces complex manual feature classification with automated shape context analysis algorithms that computationally compare scanned ear impression data against reference databases, enabling automatic classification without increasing physical device complexity
Solution Approach 2:
The system introduces shape context descriptors as an intermediary computational representation that bridges the gap between raw scanned data and feature classification, enabling automated identification by comparing geometric relationships rather than raw pixel data
Data Source
AI summary
A method and apparatus is disclosed whereby a point on an ear impression model to be labeled is selected and a shape context is determined for that point. This shape context is then compared to average shape contexts for different regions on a reference ear impression model, also referred to herein as an ear impression shape atlas. A cost function is used to determine the minimum cost between the shape context for the selected point and one of the average shape contexts. Once the minimized cost is determined, the region label corresponding to the average shape context having a minimized cost is assigned to that point. In this way, points on the surface of an ear impression are classified and labeled as being located in regions corresponding to the regions on the ear impression shape atlas.