Image Selection Tunnel Region for Edge Snapping
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Solution Overview
Problem
Existing image editing tools face challenges in selecting specific sections of images due to difficulties in defining borders, especially when multiple colors are involved, and lack the ability to correct mistakes during the selection process, leading to frustration and inefficient editing.
Innovation Solution
An image editing method that identifies edges in an image, applies a de-noise algorithm to maintain relevant edges, and allows for border definition by snapping to edges as the cursor moves, with features like adjustable search windows and tunnel generation for smooth transitions from foreground to background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user draws a border by manually moving the cursor to define the selection area, then the selection border can be precisely defined, but the operation becomes very difficult and time-consuming requiring slow and careful cursor movement
Solution Approach 1:
The patent replaces the manual mechanical cursor movement system with an automated edge detection algorithm. The system uses image processing techniques to automatically identify edges and boundaries in the image, substituting the need for manual cursor tracing with computational edge detection, thereby maintaining precision while dramatically improving ease of operation
Solution Approach 2:
The system enables self-service by allowing the image itself to provide the border definition through its inherent edge information. The edge detection algorithm automatically identifies boundaries within the image content, allowing the image to define its own selection borders without requiring manual intervention, thus resolving the contradiction between precision and ease of operation
2Productivity
If a user must complete the entire border definition in one continuous cursor movement without mistakes, then the selection process can be completed, but the process becomes frustrating and time-consuming when mistakes occur requiring restart
Solution Approach 1:
The patent implements feedback mechanisms that allow users to review and correct border definitions. The system provides visual feedback during the selection process and allows users to modify borders after initial definition, enabling mistake correction without restarting the entire process, thus improving both productivity and ease of operation
Solution Approach 2:
The system performs preliminary edge detection and border suggestion before final selection confirmation. By pre-identifying potential border locations through edge detection algorithms, the system allows users to make informed decisions and correct mistakes easily, preventing the need to restart the entire selection process
3Stability of the object's composition
If a simple softening effect is applied to the selection border, then the transition from foreground to background is smoother, but the transition does not account for the actual nature of the border and cannot preserve important details like hair strands
Solution Approach 1:
The patent applies local quality by using the detected edge information to create non-uniform transition zones. Instead of applying a uniform softening effect, the system adjusts the transition characteristics based on the local border properties identified through edge detection, allowing different parts of the border to have different transition qualities. This preserves important details like hair strands in areas where edges are detected while still providing smooth transitions where appropriate
Data Source
AI summary
Some embodiments provide a method of selecting a section of interest in an image that includes numerous pixels. the method draws a curvilinear boundary about the section of interest. From the curvilinear boundary, the method generates a two-dimensional transition tunnel region about the section of interest. The method analyzes image data based on the tunnel region to identify a subset of pixels in the region that should be associated with the section of interest. In some embodiments, the tunnel region includes a pair of curves bounding the tunnel region. In some embodiments, the curvilinear boundary has a particular shape, and generating the tunnel region includes determining whether the tunnel can be generated at a specified width with both curves of the tunnel having the same particular shape as the defined border. In some embodiments, analyzing image data includes comparing pixels inside the transition tunnel region to pixels outside the transition tunnel region.