Image Correction for Cutting Pattern Creation
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Solution Overview
Problem
Existing cutting machines struggle with accurately correcting acquired images for perspective, rotation, and positioning errors, especially when images are distorted or captured in cluttered environments, which hinders the creation of precise cutting patterns for handicrafts.
Innovation Solution
A method that identifies registration marks within acquired images to apply inverse transformations, converting distorted images into corrected formats aligned with physical coordinates, allowing for the generation of accurate cut paths for cutting machines, even in noisy or cluttered conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image correction is performed using traditional methods, then some distortion compensation is achieved, but accuracy deteriorates when images are captured in cluttered environments or with significant perspective errors
Solution Approach 1:
The patent extracts and isolates the registration marks from the cluttered background by converting the image to grayscale and applying thresholding to create a binary image. This separates the high-contrast registration marks from interfering elements, allowing accurate detection even in noisy environments.
Solution Approach 2:
The patent transforms the color image to grayscale and then to a binary image through thresholding. This color/intensity transformation enhances the visibility of registration marks by exploiting their high contrast properties, making them distinguishable from the cluttered background.
2Adaptability or versatility
If manual identification of registration marks is used, then flexibility is maintained, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The system performs automatic identification of registration marks through image processing algorithms including thresholding, contour detection, and geometric validation. This self-service approach eliminates manual intervention while maintaining accuracy through programmed validation of mark positions and configurations.
Solution Approach 2:
The patent replaces manual visual inspection and identification with automated computer vision algorithms. The processing steps (thresholding, contour detection, coordinate extraction) substitute human cognitive and manual operations, dramatically increasing productivity while maintaining adaptability through programmable logic.
3Shape
If perspective correction is applied without accurate registration mark identification, then image distortion is reduced, but manufacturing precision deteriorates due to incorrect transformation parameters
Solution Approach 1:
The patent performs preliminary identification and validation of registration mark positions before applying perspective correction. By detecting contours, calculating centroids, and verifying geometric relationships of registration marks first, the system ensures accurate transformation parameters are derived, preventing propagation of errors into the final cutting pattern.
Solution Approach 2:
The system uses the detected registration mark positions to calculate perspective transformation parameters, applies the correction, and can validate the results. The feedback loop ensures that the correction accurately maps the distorted image to the intended coordinate system, maintaining manufacturing precision for cutting patterns.
Data Source
AI summary
The production of a cutting pattern of a graphic placed upon a cutting mat is disclosed. A source image of the graphic overlaid on a cutting mat is received, and includes a plurality of registration marks as well as one or more distortions introduced during acquisition. The registration marks are identified from the source image by matching candidate sets of a plurality of center points of regions of adjacent groupings of pixels within the source image against predetermined positional relationships thereof corresponding to an actual arrangement of the registration marks on the cutting mat. An inverse transformation of the source image with values derived from the registration marks is applied. A corrected image aligned to physical coordinates of the cutting mat and referenced to the cutting machine is generated. A cut path is defined from vectors of the corrected image, and transmitted to the cutting machine for execution thereon.