Red-Eye Object Classification in Digital Images
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Solution Overview
Problem
Current red-eye detection and correction technologies face challenges in distinguishing true red-eye objects from false ones, especially in complex visual scenes with varying illumination, low image quality, and background changes, leading to high false detection rates.
Innovation Solution
The methods employ various image and object characteristics such as luminance, chrominance, contrast, smoothness, binary patterns, and feature spatial distributions to classify candidate red-eye objects, using techniques like RGB to YUV conversion, standard deviation analysis, binarization, and color-ratio-based redness evaluation to differentiate true from false red-eye objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional red-eye detection methods are used, then detection speed is maintained, but false detection rate increases significantly in complex visual scenes
Solution Approach 1:
The patent segments the red-eye detection process into multiple specialized stages: initial candidate detection using color thresholding, followed by separate evaluation of color characteristics, structural characteristics, and geometric characteristics. Each stage filters candidates independently, allowing complex multi-factor verification without requiring a single overly complex detection algorithm.
Solution Approach 2:
The patent applies different evaluation criteria to different aspects of candidate objects: color characteristics (redness, saturation) are evaluated separately from structural characteristics (pixel intensity distribution, edge patterns) and geometric characteristics (aspect ratio, position relative to face). This localized quality assessment allows each feature type to be optimized independently for its specific discrimination task.
2Reliability
If multiple evaluation criteria are applied to reduce false red-eye objects, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary filtering using computationally inexpensive color thresholding to generate candidate objects before applying more expensive structural and geometric evaluations. The color-based candidate selection serves as a pre-filter that eliminates clearly non-red-eye regions, reducing the number of candidates that require intensive multi-criteria evaluation.
Solution Approach 2:
The patent implements a cascaded evaluation structure where not all candidates undergo all evaluation stages. Candidates that fail early color or structural checks are eliminated before reaching geometric analysis, applying partial evaluation only where necessary. This selective multi-criteria approach reduces overall processing time while maintaining high false positive rejection rates.
3Measurement precision
If color and structural characteristics are used for classification, then true red-eye objects can be identified, but many false red-eye objects remain due to similar characteristics
Solution Approach 1:
The patent moves beyond two-dimensional color space analysis by incorporating geometric dimension (spatial relationships, aspect ratios, position relative to facial features) and structural dimension (edge density, pixel intensity gradients, texture patterns). This multi-dimensional feature space allows differentiation of objects that may share similar color characteristics but differ in their geometric or structural properties.
Data Source
AI summary
Automatic red-eye object classification in digital photographic images. A method for classifying a candidate red-eye object in a digital photographic image includes several acts. First, a candidate red-eye object in a digital photographic image is selected. Next, RGB pixels of the candidate red-eye object are converted into YUV pixels. Then, the YUV pixels satisfying a constraint that is a function of the YUV pixels are summed. Next, the sum is determined to be greater than or equal to a scaled version of the total number of YUV pixels in the candidate red-eye object. Finally, the candidate red-eye object is transformed into a true red-eye object.