Deviation Magnification for Sub-Pixel Geometric Detection
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Solution Overview
Problem
Existing methods struggle to reveal and visualize subtle geometric deviations from idealized shapes in images, especially when these deviations are small or obscured by texture, limiting their application in fields like construction, engineering, and astronomy.
Innovation Solution
The method fits parametric models to objects in an image, generates a deviation function from the residual, and amplifies these deviations to produce a warped output image, using sub-pixel sampling and image matting to preserve the deviation signal while removing texture effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to detect geometric deviations, then the detection process is simple, but the measurement precision is insufficient for sub-pixel level deviations
Solution Approach 1:
The patent segments the image processing into distinct modules: edge detection, parametric model fitting, residual calculation, and deviation magnification. This segmentation allows each module to specialize in one aspect of the problem, achieving sub-pixel precision through coordinated operation of multiple specialized components rather than a single complex algorithm
Solution Approach 2:
The patent introduces intermediate representations including parametric models (lines, circles, ellipses) that serve as mediators between the raw image data and the final deviation measurement. These parametric forms provide a mathematical framework that enables precise sub-pixel deviation detection while maintaining computational tractability
2Reliability
If image matting is applied to remove texture effects, then the deviation signal is preserved, but the processing time increases
Solution Approach 1:
The patent performs image matting as a preliminary step before deviation detection. By removing texture and background effects in advance, the subsequent parametric fitting and deviation calculation operate on cleaned data, improving accuracy while allowing optimization of the matting process itself to minimize time loss
3Measurement precision
If parametric models are fitted to objects, then subtle geometric deviations can be revealed, but the method cannot handle objects without idealized geometries
Solution Approach 1:
The patent implements a universal parametric modeling framework that can represent multiple geometric forms (lines, circles, ellipses) within a unified mathematical structure. This universality allows the same deviation detection algorithm to work across different object types and idealized geometries, making the method broadly applicable while maintaining precision
Data Source
AI summary
Geometries of the structures and objects deviate from their idealized models, while not always visible to the naked eye. Embodiments of the present invention reveal and visualize such subtle geometric deviations, which can contain useful, surprising information. In an embodiment of the present invention, a method can include fitting a model of a geometry to an input image, matting a region of the input image according to the model based on a sampling function, generating a deviation function based on the matted region, extrapolating the deviation function to an image wide warping field, and generating an output image by warping the input image according to the warping. In an embodiment of the present invention, Deviation Magnification inputs takes a still image or frame, fits parametric models to objects of interest, and generates an output image exaggerating departures from ideal geometries.