Feature-Based Color Adjustment for Digital Images
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Solution Overview
Problem
Conventional image processing programs lack effective methods for accurately adjusting colors in digital images, particularly in regions with complex features like the human eye, leading to incomplete or visually unpleasing corrections of defects such as the redeye effect.
Innovation Solution
The method involves identifying a target region in an image using probability functions and spatial profile functions to determine the likelihood that pixels correspond to specific features, allowing for precise color adjustments that blend with representative colors, while avoiding modifications to incorrect areas, such as skin or sclera, and applying additional corrections to specific eye regions like the ciliary margin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color adjustment tools operate by changing pixel values throughout the image or in selected regions, then color adjustment can be applied, but the adjustment lacks accuracy in regions with complex features like the human eye, leading to unwanted modifications
Solution Approach 1:
The patent applies different adjustment strategies to different regions of the image based on their semantic content. By identifying whether a region contains eye features, skin, or other elements, the system applies appropriate correction parameters locally. This allows precise redeye correction in eye regions while preserving other areas, resolving the contradiction between overall color adjustment and localized accuracy.
Solution Approach 2:
The patent introduces an intermediary classification system that first identifies the semantic content of regions before applying color adjustments. This intermediary layer of analysis acts as a mediator between the raw image and the final adjusted image, preventing direct application of corrections to inappropriate regions and thus avoiding unwanted modifications.
2Adaptability or versatility
If predefined adjustment algorithms are used to address specific situations, then common defects can be corrected, but the algorithms cannot handle complex features like the human eye effectively
Solution Approach 1:
The patent transitions from static predefined algorithms to a dynamic adjustment system that adapts to the specific characteristics of each image region. By analyzing the semantic content and spatial properties of regions in real-time, the system dynamically selects and applies appropriate correction parameters, enabling effective handling of complex features like the human eye while maintaining reliability.
Solution Approach 2:
The patent segments the image into distinct regions based on semantic identification (eye regions, skin regions, etc.). This segmentation allows the system to apply specialized correction algorithms to each segment according to its specific characteristics, thereby improving both adaptability to complex features and the reliability of corrections.
3Measurement precision
If individual pixels are inspected and predefined templates are applied to identify redeye sites, then redeye correction can be applied, but the method cannot accurately distinguish between different eye regions and other features
Solution Approach 1:
The patent adds semantic dimensionality to the pixel analysis by classifying regions based on their visual and spatial characteristics. Instead of only analyzing pixel values in the traditional two-dimensional grid, the system incorporates semantic labels and spatial profiles to differentiate between eye regions, skin, and other features, thereby improving identification accuracy without excessive complexity.
Solution Approach 2:
The patent creates a composite identification system that combines multiple analysis techniques (color analysis, spatial profile analysis, semantic classification) to identify pixel regions. This composite approach integrates the strengths of each technique, achieving accurate differentiation between eye regions and other features while managing computational complexity through efficient integration.
4Ease of operation
If general color adjustment is applied without probability functions and spatial profiles, then the process is simpler, but the control over desired regions and reduction of unwanted modifications is limited
Solution Approach 1:
The patent performs preliminary analysis of the image to identify regions and their semantic content before applying color adjustments. By pre-classifying regions and calculating probability functions and spatial profiles in advance, the system prepares the necessary information to guide subsequent adjustments, thereby improving control over desired regions while managing complexity through efficient pre-processing.
Data Source
AI summary
Methods and apparatus implementing a technique for adjusting the color of pixels in an image. The technique includes identifying a target region of pixels in the image that represent an object, the object having a shape and a predefined set of features; and adjusting the color of one or more pixels in the target region by an amount determined based on one or more probability functions and one or more spatial profile functions, each probability function defining a probability value at each of the one or more pixels in the target region, the probability value representing the probability that the pixel corresponds to one or more features of the object, each spatial profile function being defined based on one or more spatial properties of the object or one or more of its features.