Image Partitioning via Free-Form Curve Selection
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Solution Overview
Problem
Current image partitioning methods in drawing software are inefficient, as they often require precise definition of subparts using primitive forms, fail to account for the user's intention in free-form drawings, and are computationally resource-intensive, especially when dealing with complex shapes or multiple selections, leading to unpredictable and time-consuming processes.
Innovation Solution
A computer-implemented method that displays an image with a border, allowing users to draw a curve without linking the start and end points, computes closed areas based on the curve and image border, and enables independent selection of these areas, reducing computational resources and aligning with the user's intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the software automatically closes the drawing by linking the first point to the last one to create a closed boundary, then the image can be separated into inside and outside areas, but the user's intention is not taken into account when the free form is already a closed curve, leading to incorrect partitioning
Solution Approach 1:
The system dynamically adjusts its behavior based on the characteristics of the drawn curve. When the curve is already closed, the software recognizes this and adapts its partitioning logic accordingly, rather than always forcing a connection between start and end points. This dynamic adaptation resolves the contradiction by making the system flexible enough to handle different user intentions.
Solution Approach 2:
The software provides visual feedback to the user about how their drawing will be interpreted. By showing the intended partitioning result before final execution, the user can verify that the system correctly understood their intention. This feedback mechanism allows for correction and ensures accurate partitioning matching user intent.
2Ease of operation
If the software always keeps the inner part of the closed area, then the partitioning process is simple, but the user cannot keep the outer part even when needed
Solution Approach 1:
The software inverts the default behavior by allowing the user to choose between keeping the inner part or the outer part of the closed area. Instead of always keeping the inner part, the system provides an option to invert the selection, giving users flexibility while maintaining simple operation through a clear choice mechanism.
Solution Approach 2:
The system changes the selection parameter from fixed (always inner part) to variable (user-chosen inner or outer part). This parameter change enables versatility in selection while keeping the operation simple through a straightforward option selection interface.
3Manufacturing precision
If the user wants to extract multiple subparts from the image, then complete coverage is achieved, but the process becomes time consuming and tedious requiring repeated import and selection
Solution Approach 1:
The software performs preliminary actions by maintaining the image in memory and keeping the selection tool active after the first extraction. Instead of requiring re-import, the system prepares the workspace in advance for subsequent selections, allowing users to efficiently extract multiple subparts without repeating setup steps.
Solution Approach 2:
The system enables continuous selection operations by maintaining the image and tool state between extractions. The useful action of selecting and extracting subparts continues without interruption, allowing users to extract multiple regions in sequence without breaking the workflow, thus reducing time loss.
4Ease of operation
If the user wants to cut the image in half, then a simple division is needed, but the software requires drawing a closed circle around the part, which is not intuitive and time consuming
Solution Approach 1:
The software segments the cutting operation into different modes: closed area selection for complex regions and open curve division for simple splits. This segmentation allows users to choose the appropriate method for their needs, making simple divisions intuitive through open curves while still supporting complex selections when required.
Solution Approach 2:
Instead of requiring users to think in terms of closed areas for all selections, the system inverts the approach by allowing open curves for simple divisions. This inversion makes the operation more intuitive for common tasks like cutting an image in half, while preserving the closed area option for complex selections.
5Reliability
If the software uses algorithms to detect inside and outside of closed areas, then partitioning can be performed, but significant computing resources are used and results can be unpredictable
Solution Approach 1:
The software extracts and uses the image border as a known reference boundary for partitioning. By taking out the border information and using it as a fixed reference, the system avoids the need for complex algorithms to determine inside and outside areas, significantly reducing computational resources while maintaining reliable partitioning results.
Solution Approach 2:
The image border serves itself as a reference for partitioning operations. Instead of requiring external algorithms to determine boundaries, the existing border structure of the image provides the reference information needed, making the system more efficient and reliable without additional computational overhead.
Data Source
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AI summary
The invention is directed to a computer-implemented method for partitioning an image. The method comprises displaying (S10) an image that comprises a border; drawing (S20) a curve over the image; computing all closed areas delimited by the curve and the border of the image, each closed area being independent from the other computed closed areas and individually selectable.