Parametric Image Selection Using Energy Minimization
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Solution Overview
Problem
Current image editing tools require a tedious and inaccurate two-step process for making local adjustments to digital images, involving selection and application of adjustments, which can be time-consuming and prone to errors due to limitations in existing selection technologies like the 'Magic Wand' and 'Magnetic Lasso' tools in applications such as Adobe PHOTOSHOP.
Innovation Solution
A method and system that allows for parametrically-controlled segmentation and selection of digital data using activity markers with location and control data, enabling near real-time feedback and interactive modification of selection masks, utilizing energy minimization techniques and graph-cut methods to accurately select image regions for localized adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional selection tools (Magic Wand, Magnetic Lasso) are used to select image regions, then selection can be made, but the process is time-consuming and inaccurate requiring multiple user interactions
Solution Approach 1:
The system performs automated selection of image regions based on user input parameters without requiring manual tracing or multiple adjustments. The selection algorithm automatically identifies and segments regions matching the specified criteria, eliminating the need for users to manually trace boundaries or repeatedly adjust selection parameters.
Solution Approach 2:
The patent replaces manual mechanical selection operations (mouse tracing, brush painting) with an automated computational selection system. The system uses image processing algorithms to automatically identify and select regions based on color, texture, and spatial characteristics, substituting mechanical user actions with intelligent automated processing.
2Reliability
If manual selection masks are created using traditional tools, then selection can be made, but the process is tedious and prone to errors
Solution Approach 1:
The system automatically generates and refines selection masks based on user-specified parameters, eliminating the need for users to manually create and adjust masks. The automated algorithm handles the complex task of mask generation, reducing user burden and minimizing errors associated with manual mask creation.
Solution Approach 2:
The system provides real-time feedback during the selection process, allowing users to review and adjust selection parameters before finalizing the mask. This iterative feedback mechanism ensures high selection reliability while maintaining ease of operation, as users can make simple parameter adjustments rather than manually redrawing masks.
3Productivity
If existing automated selection methods are used, then selection can be made faster, but they require efficient segmentation which suffers from inaccuracies
Solution Approach 1:
The patent implements multi-stage segmentation that divides the selection process into distinct phases: initial region identification, boundary refinement, and detail preservation. This segmented approach maintains high productivity by automating each stage while achieving high accuracy through progressive refinement, overcoming the limitations of single-stage segmentation methods.
Solution Approach 2:
The system applies different selection criteria and processing methods to different regions of the image based on local characteristics. Edge regions receive specialized processing to preserve boundaries, while uniform regions use simpler color-based selection. This local quality approach maintains both efficiency and accuracy across diverse image content.
Data Source
AI summary
An example method includes receiving a first selection location and at least one selection parameter with respect to digital data. A portion of the digital data is selected relative to the first selection location. The selecting of the portion includes assigning an energy value to each pixel within a selection proximity of the selection location. The selection proximity is determined based on the selection parameter and the energy value being a function of distance from the selection location. The selecting of the portion further includes generating a selection value for each pixel within the selection proximity, based on the assigned energy value and on a pixel characteristic difference between the relevant pixel and at least one neighboring pixel. The selection value determines whether the relevant pixel is included in the selected portion of the digital data.