Dynamic Color Mask Generation Using Truncated Gaussian Functions
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Solution Overview
Problem
Existing digital image editing systems face limitations in dynamically generating and modifying masks based on color models, particularly in handling a range of colors and user input, which restricts the precision and flexibility of image editing operations.
Innovation Solution
The system generates a mask based on a color model using truncated Gaussian functions, allowing dynamic modification of the color model and mask in response to user input, enabling the inclusion or exclusion of colors and adjusting color tolerance, thereby facilitating more precise and flexible image editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed number of full Gaussian functions are used in a GMM for modeling color distributions, then the model structure is simple and easy to implement, but the system cannot dynamically adapt to new color samples without rebuilding the entire model
Solution Approach 1:
The patent transforms the static GMM structure into a dynamic one by allowing the number of Gaussian functions to change over time. New Gaussian functions can be added when new color samples are provided, and existing ones can be removed when they no longer represent the data distribution well. This dynamic structure enables the model to adapt to new color samples without complete rebuilding, resolving the contradiction between adaptability and structural simplicity.
Solution Approach 2:
The patent changes the parameter of the GMM from a fixed number of Gaussian functions to a variable number that can be adjusted based on the data. By making the number of components a changeable parameter rather than a static value, the system gains flexibility to adapt to new color samples while maintaining a relatively simple implementation approach.
2Measurement precision
If the entire GMM is rebuilt when new color samples are provided, then the model accuracy is maintained, but the processing time and computational resources increase
Solution Approach 1:
The patent prepares the GMM structure in advance by establishing a framework that can accommodate new Gaussian functions. Instead of starting from scratch when new samples arrive, the pre-established structure allows for incremental updates, reducing the time and computational resources needed while maintaining modeling accuracy.
Solution Approach 2:
The patent enables continuous updating of the GMM by allowing incremental addition of new Gaussian functions based on new color samples. This continuous action approach replaces the discontinuous complete rebuilding process, maintaining accuracy while reducing processing time and resource consumption through step-by-step model evolution.
3Ease of operation
If a magic wand tool selects pixels based on a color range, then the selection process is simple and fast, but the selection precision is limited when dealing with complex color variations
Solution Approach 1:
The patent segments the color selection process into two levels: a simple user interface for specifying color ranges (maintaining ease of operation) and a complex GMM-based processing layer for accurate pixel selection (improving precision). This segmentation allows the simple magic wand interface to leverage the powerful GMM model, resolving the contradiction between operational simplicity and selection precision for complex color variations.
Solution Approach 2:
The patent introduces the GMM model as an intermediary between the simple color range specification and the actual pixel selection process. This intermediary layer translates simple user inputs into precise selections by modeling the color distribution, enabling the magic wand tool to handle complex color variations accurately while maintaining its simple and fast operation characteristics.
Data Source
AI summary
A method, system, and computer-readable storage medium are disclosed for generating a mask based on a color model. In one embodiment, a selection of at least one color in an input image is received. A color model may be determined based on the selection of the at least one color, wherein the color model comprises one or more truncated Gaussian functions. A mask may be generated based on the color model. The mask may comprise a respective value indicating a degree of membership in the mask for each of the plurality of pixels in the input image, wherein the degree of membership in the mask is a function of a similarity in color to the selection.