Image Morphing via Edge Gradient Constraints
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Solution Overview
Problem
Conventional image morphing techniques require manual definition and marking of control points, leading to increased operator burden and potential geometric distortions, especially when control points are either too dense or too few.
Innovation Solution
An image morphing method that computes edge gradient parameters and uses a total objective function to generate an intermediate image sequence, incorporating edge constraints to enhance the morphing effect and reduce manual effort by automatically determining optimal control points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If control points are manually marked densely to improve geometric precision, then manufacturing precision improves, but device complexity and operator burden increase
Solution Approach 1:
The system automatically detects edges and determines control points based on image content rather than requiring manual operator input. The edge detection algorithm and objective function optimization process enable the system to self-determine optimal control point locations, eliminating the need for operators to manually mark dense control points while maintaining geometric precision.
Solution Approach 2:
The invention changes the approach from manual control point specification to automatic determination based on edge gradient parameters and objective function optimization. By transforming the problem into an optimization task with computable parameters (edge gradients, objective function values), the system achieves geometric precision through algorithmic parameter optimization rather than manual control point placement.
2Device complexity
If control points are manually marked sparsely to reduce operator burden, then device complexity decreases, but manufacturing precision deteriorates due to geometric distortions
Solution Approach 1:
The system automatically determines control point locations by detecting edges and optimizing an objective function, eliminating the need for operators to manually decide control point density and placement. This self-service approach ensures that control points are optimally distributed based on image content rather than operator judgment.
Solution Approach 2:
The invention replaces the manual mechanical process of control point marking with an automated computational system. The edge detection algorithm and objective function optimization replace human operator actions with computational processes that automatically determine optimal control point locations, ensuring geometric precision without manual intervention.
3Manufacturing precision
If manual control point marking is used to achieve accurate geometric mapping, then manufacturing precision improves, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic edge detection and control point determination without requiring operator intervention. The algorithm independently analyzes image edges, computes gradient parameters, and optimizes control point locations using the objective function, achieving accurate geometric mapping through automated self-service rather than manual operation.
Solution Approach 2:
The invention substitutes the manual mechanical operation of control point marking with an automated computational system. The edge detection and optimization algorithms replace human operators in the geometric mapping process, maintaining accuracy while dramatically improving ease of operation by eliminating manual marking tasks.
4Ease of operation
If automated edge gradient computation is implemented to determine control points, then ease of operation improves and operator burden decreases, but device complexity increases
Solution Approach 1:
The invention replaces manual control point marking operations with automated edge gradient computation and objective function optimization. This substitution improves ease of operation by eliminating manual tasks, while the computational complexity is managed through well-established image processing algorithms and optimization techniques.
Solution Approach 2:
The system transforms the control point determination problem into a parameter optimization task based on edge gradient computations. By changing from manual coordinate specification to automated parameter optimization, the system improves ease of operation while managing complexity through mathematical modeling and computational algorithms.
Data Source
AI summary
An image morphing method is suitable for generating an intermediate image sequence. First, a control point CP={(pi,qi)}i=1 . . . N is specified and marked in a source image Is({right arrow over (x)}) and a destination image Id({right arrow over (x)}′). Next, an edge gradient parameter (Ise({right arrow over (x)}), Ide({right arrow over (x)}′) is computed according to the source image Is({right arrow over (x)}) and the destination image Id({right arrow over (x)}′). Next, a total objective function E(Df,Db) is computed according to the above-mentioned control point CP and edge gradient parameter (Ise({right arrow over (x)}), Ide({right arrow over (x)}′)). The above-mentioned intermediate image sequence is generated by using the total objective function E(Df,Db). The present invention utilizes the edge gradients of the source image Is({right arrow over (x)}) and the destination image Id({right arrow over (x)}′) to enhance the constraint of image morphing. Thus, the image morphing effect is promoted.