Markerless Component Identification via 1D Depth Profile Extraction
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Solution Overview
Problem
Existing markerless tracking methods for object identification in industrial production face challenges with translation invariance, requiring precise alignment of reference and query images, which is difficult for objects lacking visual references or having complex geometries, leading to inefficiencies and inaccuracies.
Innovation Solution
A method that calculates unique image features using a specified calculation rule, allowing for translation invariance and scalability, enabling identification even with small overlaps between reference and query images, and is applicable to objects previously unmanageable with fingerprint methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fingerprint methods are used for markerless tracking, then object identification can be achieved, but precise alignment of reference and query images is required which is difficult for objects lacking visual references
Solution Approach 1:
The patent transforms the image representation by converting 2D image data into 1D depth profiles through vertical integration. This parameter transformation changes the dimensionality and nature of the data being compared, making the identification process invariant to horizontal translations and simplifying the alignment requirement while maintaining identification accuracy.
Solution Approach 2:
The patent extracts the essential depth information from the full 2D image by integrating vertically and retaining only the horizontal profile. This extraction process removes redundant vertical information while preserving the unique depth characteristics needed for identification, thereby simplifying the comparison process and reducing alignment sensitivity.
2Adaptability or versatility
If the translation region is increased to handle objects with small overlaps, then identification coverage is improved, but computational effort increases significantly
Solution Approach 1:
The patent replaces the conventional 2D image correlation approach with a 1D depth profile comparison method. This substitution dramatically reduces the computational complexity from O(N*M) for 2D correlation to O(N) for 1D profile comparison, enabling efficient processing even when the translation region must be large to accommodate objects with small overlaps.
Solution Approach 2:
The patent reduces the problem from two dimensions (2D image correlation) to one dimension (1D depth profile) by integrating vertically. This dimensional reduction maintains the essential identification information while significantly decreasing the computational burden, allowing for larger translation regions without proportionally increasing processing time.
3Measurement precision
If DMC marking is used for traceability, then identification can be achieved, but laser engraving produces smoke residue that contaminates components
Solution Approach 1:
The patent uses the naturally occurring surface texture of the component as a unique identifier, copying the existing microscopic depth variations rather than adding an artificial mark. This approach leverages the component's inherent characteristics to create a unique fingerprint for traceability without requiring any additional marking processes that would generate contamination.
Solution Approach 2:
The component's own surface texture serves as the identification feature, eliminating the need for external marking systems. The natural variations in surface depth and texture provide unique identifiers that are inherent to each component, allowing the component to identify itself without requiring laser engraving or other contaminating marking methods.
4Measurement precision
If DMC is applied to small components, then identification is possible, but some components are too small to accommodate DMC application
Solution Approach 1:
The patent extracts the essential identification information from a minimal area of the component surface by converting 2D image data into 1D depth profiles. This extraction process concentrates the unique identifying characteristics into a compact representation that requires very little surface area, enabling identification of small components that cannot accommodate traditional DMC markings.
Solution Approach 2:
The patent replaces the space-intensive DMC marking system with a compact 1D depth profile representation. This substitution reduces the required surface area from the several millimeters needed for DMC to potentially sub-millimeter scales, as the unique identification is encoded in the depth variations rather than requiring a two-dimensional code pattern.
Data Source
AI summary
A method for ascertaining whether a specified image detail of a first image appears in a second image. The method begins with ascertaining at least one image feature for a plurality of pixels within the specified image detail. This is followed by ascertaining at least one image feature for a plurality of pixels of the second image. This is followed by comparing the ascertained image features of the second image with the ascertained image features of the first image to determine whether the two have an identical value. In the method, a rotationally symmetrical area is determined from the second image and this area is transformed into a rectangular area and in that the image features of the pixels of the second image are calculated from pixels within the transformed area.