3D Imaging Peak Detection Algorithm Selection for Mixed Surfaces
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Solution Overview
Problem
Conventional 3D imaging systems based on light triangulation struggle to accurately capture the positions of intensity peaks on objects with mixed surfaces, such as opaque and translucent or transparent materials, leading to reduced accuracy in 3D image representation.
Innovation Solution
Implementing a method that uses two distinct computing algorithms to determine the position of intensity peaks, one for center detection on opaque surfaces and another for edge detection on translucent or transparent surfaces, based on the characteristics of the peaks, to provide more accurate 3D imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single computing algorithm is used to detect intensity peak positions, then the system is simple to operate, but measurement precision deteriorates on objects with mixed surface types
Solution Approach 1:
The patent applies local quality by selecting different computing algorithms based on the local characteristics of each intensity peak. For peaks corresponding to opaque surfaces, a center-based algorithm is used, while for peaks corresponding to translucent or transparent surfaces, an edge-based algorithm is used. This local adaptation of the detection method to match the local surface properties resolves the contradiction by maintaining simplicity of operation through automated selection while improving measurement precision for each specific case.
2Device complexity
If conventional peak detection is used on translucent or transparent surfaces, then the device complexity is low, but measurement precision deteriorates due to light diffusion
Solution Approach 1:
The patent changes the parameter of the computing algorithm based on the optical properties of the surface being measured. By detecting whether a surface is translucent or transparent and switching between center-based and edge-based algorithms, the system adapts to the changing optical parameters without increasing device complexity. This resolves the contradiction by using software-based parameter adaptation rather than hardware complexity.
3Measurement precision
If edge detection algorithm is used for all surfaces, then measurement precision improves on translucent surfaces, but manufacturing precision deteriorates on opaque surfaces
Solution Approach 1:
The patent implements a dynamic selection mechanism that automatically chooses between edge detection and center-based algorithms based on the real-time optical characteristics of each surface being measured. This dynamic adaptation ensures that manufacturing precision is maintained on opaque surfaces by using center-based detection, while measurement precision on translucent surfaces is improved through edge-based detection, resolving the contradiction through conditional algorithm selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more accurate 3D image capture of objects with mixed surface types, improving the representation of height variations and material properties by selecting the appropriate algorithm for each peak characteristic, thereby enhancing the precision of 3D imaging systems.
Implementation Method 1
image data produced by an image sensor in response to light sensed by the image sensor after the light has reflected on an object
Data Source
AI summary
Provision of a position of an intensity peak in image data produced by an image sensor in response to light sensed by the image sensor after the light has reflected on an object as part of light triangulation performed in a three-dimensional imaging system. Devices are configured to obtain the image data and determine which of first and other, second, peak characteristics that the intensity peak is associated with. The position is provided according to a first computing algorithm if the intensity peak is associated with the first peak characteristics and according to a different, second computing algorithm if the intensity peak is associated with the second peak characteristics.