Pupil Identification via Connected Graph Weighting
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Solution Overview
Problem
Current methods for pupil identification in images, such as those used in eyeball tracking systems, face challenges in accurately locating the pupil without manual judgment or external apparatus, and lack flexibility in adapting to varying image conditions.
Innovation Solution
A method and device that preprocess images to build a grey-scale image, detect edges, and identify pupils by categorizing connected graph pairs based on grey-scale values, calculating centroid locations and axis ratios, and selecting the pair with the maximum weighted value, allowing for flexible adaptation to image variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pupil identification methods are used, then the system can locate pupil features, but the accuracy is insufficient without manual judgment or external apparatus
Solution Approach 1:
The method segments the image processing into distinct stages: grey-scale conversion, edge detection, connected component analysis, and pupil identification. By dividing the complex task of pupil localization into these manageable segments, the system achieves high accuracy without requiring manual intervention or external apparatus, resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The method performs preliminary actions by pre-processing the image through grey-scale conversion and edge detection before pupil identification. Connected graphs are pre-marked and categorized by grey-scale value ranges (100-200 and over 200), creating a structured foundation that enables accurate pupil localization without complex real-time processing or external devices
2Adaptability or versatility
If conventional pupil identification methods are used, then basic pupil detection is possible, but the adaptability to varying image conditions is limited
Solution Approach 1:
The method adapts to varying image conditions by changing parameters: it categorizes connected graphs into two grey-scale value ranges (100-200 and over 200), calculates multiple geometric parameters (centroid location, major axis length, minor axis length), and uses a composite evaluation formula W=L-m*R-n*P that incorporates distance, ratio, and positional information. This multi-parameter approach enables the system to maintain high precision across different image conditions
Solution Approach 2:
The method introduces dynamic adaptability through the weighted evaluation formula where m and n are adjustable weight factors. This allows the system to dynamically adjust the importance of different parameters (distance L, ratio R, position P) based on specific image conditions, enabling both high adaptability and maintained precision
3Measurement precision
If detailed image processing is performed to improve pupil identification accuracy, then the location precision increases, but the processing time increases
Solution Approach 1:
The method extracts only the essential features needed for pupil identification: edge pixels are extracted to form connected graphs, and only relevant geometric parameters (centroid, major axis, minor axis) are calculated. By extracting and focusing on these key features rather than processing the entire image in detail, the system achieves high precision while minimizing processing time
Data Source
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AI summary
A method and device for identifying a pupil in an image is provided. The method comprises four steps including: A. image preprocessing, B. edge detecting, C. building connected graphs, and D. screening connected graph pairs.