Pupil Detection via Parameterized Shape Fitting
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Solution Overview
Problem
Existing digital image processing methods for locating pupils, such as correcting the red-eye effect, require initial user hints and are not accurate without prior information about pupil size, making them unreliable for automatic detection.
Innovation Solution
A method that identifies candidate features in an image by fitting a parameterized shape, such as an ellipse, using iterative processes to maximize the difference between inner and outer pixel values, allowing for accurate detection of pupils without prior size information and adapting to surroundings and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user hints are required for pupil location detection, then detection reliability improves, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic pupil detection without requiring user hints or manual input. The algorithm independently identifies pupils by analyzing color patterns and geometric characteristics in the image, allowing the system to serve itself rather than requiring user guidance for detection.
Solution Approach 2:
The system performs preliminary analysis of the entire image to identify potential pupil locations before final detection. By pre-processing the image to highlight candidate regions based on color and shape criteria, the system prepares the data structure needed for accurate pupil localization without user intervention.
2Measurement precision
If prior information about pupil size is provided, then detection precision improves, but device complexity deteriorates
Solution Approach 1:
The system dynamically adjusts detection parameters based on the image content rather than relying on pre-provided pupil size information. By changing parameters such as search region size, color threshold ranges, and geometric constraints adaptively, the system achieves precise detection without requiring prior size information or complex user input.
Solution Approach 2:
The detection algorithm is designed to be dynamic and adaptive to different imaging conditions. The system automatically adjusts its detection criteria based on the actual pupil characteristics in the image, making the system flexible and precise without requiring rigid pre-provided parameters or complex configuration.
3Measurement precision
If iterative shape fitting is applied, then measurement precision improves, but use of energy deteriorates
Solution Approach 1:
The system applies iterative shape fitting only to candidate regions that have been pre-identified as potential pupils, rather than performing exhaustive search across the entire image. By limiting the iterative optimization to promising candidate areas, the system achieves high precision for detected features while reducing overall computational energy consumption.
Solution Approach 2:
The detection process is segmented into distinct stages: initial candidate identification using simpler criteria, followed by iterative shape fitting only for promising candidates. This segmentation allows the system to use computational energy efficiently by applying complex iterative optimization only where necessary, rather than uniformly across the entire image.
Data Source
AI summary
Methods, systems, and computer program products used to locate a feature in an image, including identifying one or more candidate features in an image, where each candidate feature is a group of pixels in the image that satisfies a pattern-matching criterion. A best candidate feature is selected from the one or more candidate features, and a parameterized shape is fit to the image in the region of the best candidate feature to compute a feature shape corresponding to the best candidate feature. Particular implentations can include one or more of the following features. The candidate feature is a candidate pupil and the feature shape is an ellipse. Fitting the parameterizes shape to the mage includes applying an iterative process varying shape parameters. The parameterized shape encloses pixels in the image, and fitting the parameterized shape to compute an inner value, summing functions of values of pixels in the image outside of the parameterized shape to compute an outer value, and maximizing a difference between the inner value and the outer value.