Rapid Image Registration for Halftone Pattern Recovery
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Solution Overview
Problem
Mobile device cameras often capture images with perspective distortion due to non-frontoplanar orientations, making it difficult to recover information from halftone images or patterns effectively.
Innovation Solution
A computer-implemented process using rapid image registration techniques, including discrete Fourier transform and gradient descent methods, to estimate and correct affine transformations, thereby rectifying images and removing perspective distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobile device cameras are used to capture halftone images, then imaging quality and resolution improve, but perspective distortion occurs due to non-frontoplanar orientation
Solution Approach 1:
The patent applies preliminary action by performing affine transformation and perspective correction on the captured image before proceeding with halftone dot position analysis. The system pre-processes the distorted image to restore it to its original planar configuration, enabling accurate recovery of embedded data from halftone patterns.
Solution Approach 2:
The patent utilizes parameter changes by adjusting transformation parameters (scale, rotation, skew, translation) to correct the distorted image. The system iteratively optimizes these parameters to minimize distortion and achieve proper alignment with the original image plane.
2Reliability
If traditional image registration methods are used, then image alignment can be achieved, but the process is computationally intensive and time-consuming
Solution Approach 1:
The patent applies segmentation by dividing the image registration process into distinct stages: first performing affine transformation to correct linear distortions, then applying perspective transformation to correct projective distortions. This segmented approach reduces computational complexity compared to attempting full non-linear optimization at once.
Solution Approach 2:
The patent uses preliminary action by performing affine transformation as a preliminary step before more complex perspective correction. This preliminary alignment brings the distorted image closer to its target configuration, reducing the computational burden of subsequent refinement steps.
Data Source
AI summary
An example method of rapid image registration includes recovering an affine transform of a quasi-periodic object based on peak locations of Discrete Fourier Transform (DFT) in a captured image. The example method also includes filtering a region of the captured image to match a filtered version of a reference image including the quasi periodic object. The example method also includes recovering translation parameters to reduce image differences between the reference image and the captured image for a subset of the image locations of the filtered image and outputting an approximate transform including translation.