Selective Color Extraction from Digital Image Regions
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Solution Overview
Problem
Conventional techniques for extracting color themes from digital artwork are labor-intensive and prone to errors due to the need for manual selection of colors one at a time and the inability to designate specific areas for color extraction.
Innovation Solution
A graphics editing system allows users to select regions of a digital image for color attribute extraction, identifying visual objects within the selected area and generating a color palette that includes color values, patterns, gradients, and opacity, enabling efficient and accurate color theme extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If eyedropper techniques are used to manually pick colors from digital artwork, then colors can be selected with some precision, but the process becomes labor intensive and time consuming
Solution Approach 1:
The system automatically extracts color palettes from selected regions of digital artwork without requiring manual color picking. The computer executes algorithms that identify and extract colors autonomously, eliminating the need for designers to manually use eyedropper tools while maintaining color accuracy.
Solution Approach 2:
The system pre-processes the selected image region to identify all visual objects and extract their color attributes before presenting the palette to the user. This preliminary extraction and organization of colors from multiple objects occurs automatically, saving the time that would be required to manually sample each color.
2Productivity
If conventional color extraction techniques are used, then color palettes can be extracted from digital artwork, but the techniques are coarse and do not enable designation of specific areas for extraction
Solution Approach 1:
The system allows users to select specific regions of interest within a digital artwork and extracts color palettes only from those designated areas. Different regions can be selected independently, enabling precise control over which portions of the artwork contribute to the extracted color palette, rather than processing the entire image uniformly.
Solution Approach 2:
The system segments the selected region into individual visual objects (such as icons, images, or design elements) and extracts colors from each object separately. This segmentation enables precise control over color extraction sources and allows users to obtain color palettes from specific design elements within a larger composition.
3Reliability
If manual color selection is performed one at a time, then individual colors can be accurately identified, but the process becomes labor intensive and prone to mistakes
Solution Approach 1:
The system merges the color extraction process across multiple visual objects simultaneously. Instead of requiring separate manual sampling of each color from different objects, the system automatically processes all selected objects in parallel, extracting and consolidating their color attributes into a unified palette in a single operation.
Data Source
AI summary
Techniques are described for selective extraction of color attributes from digital images that overcome the challenges experienced in conventional systems for color extraction. In an implementation, a user applies a region selector to a source image to select a portion of the source image for color attribute extraction. A graphics editing system identifies a selected region of the source image as well as visual objects of the source image included as part of the selected region. The graphics editing system iterates through the selected visual objects and extracts color attributes from the visual objects, such as color values, patterns, gradients, gradient stops, opacity, color area, and so forth. The graphics editing system then generates a color palette that includes the extracted color attributes, and the color palette is able to be utilized for various image editing tasks, such as digital image creation and transformation.