Projective Invariant Pattern Detection for Camera Calibration
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Solution Overview
Problem
Image analysis, particularly pattern detection and camera calibration, is challenging due to variations in image scenes, lighting, color, and camera distortions, making it difficult to automate the process and accurately modify or categorize images across different cameras.
Innovation Solution
A method for detecting patterns in images using geometrical-invariant properties under projective or near projective transformations, involving the detection of shapes like circles and ellipses, which are invariant under these transformations, to calibrate and correct distortions in optical devices such as digital cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image processing is implemented, then productivity is improved, but reliability deteriorates due to variations in image scenes, lighting, color, and camera distortions
Solution Approach 1:
The patent transforms image data into projective invariant parameters that remain constant under projective transformations. By detecting shapes and their geometric properties (such as conic sections, intersection points, and relative positions) that are invariant under projective transforms, the system achieves reliable automated processing across varying camera conditions without requiring manual calibration for each image
Solution Approach 2:
The patent introduces projective invariant geometric properties as intermediaries between the raw image data and the final analysis results. These invariant properties serve as a stable reference frame that mediates the transformation from distorted camera images to accurate spatial relationships, enabling reliable automated processing despite variations in lighting, color, and camera distortions
2Measurement precision
If camera calibration is performed to correct distortions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential projective invariant geometric properties from images (such as conic section parameters, intersection points of lines, and relative positional relationships) that are sufficient for accurate measurement and calibration. By taking out only these critical invariant features rather than processing entire images or using complex calibration routines, the system achieves high measurement precision with reduced computational complexity
Solution Approach 2:
The patent creates a projective invariant representation (a simplified geometric model) that copies only the essential spatial relationships from the original image. This invariant copy contains all necessary information for accurate measurement and calibration while being much simpler to process than the original image data, thereby reducing device complexity while maintaining measurement precision
Data Source
AI summary
Detecting a pattern in an image by receiving the image of a pattern and storing the image in a memory, where the pattern is composed of shapes that have geometrical properties that are invariant under near projective transforms. In some embodiments the process detects shapes in the image using the geometrical properties of the shapes, determines the alignment of the various shapes, and, corresponds or matches the shapes in the image with the shapes in the pattern. This pattern detection process may be used for calibration or distortion correction in optical devices.