Multispectral Laser Speckle Imaging for Color-Bias-Free Activity Prediction
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Solution Overview
Problem
Conventional multispectral imaging methods are sensitive to color bias, leading to inaccurate activity predictions in applications like seed quality analysis and fungal infection detection, as they fail to address color bias effectively.
Innovation Solution
A portable multispectral laser speckle imager combines RGB imaging with a custom image processing algorithm, segmenting images, performing distance-based clustering, and generating color-coded activity maps to eliminate color bias in activity prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single wavelength laser imaging is used, then device simplicity is maintained, but measurement precision deteriorates due to color bias sensitivity
Solution Approach 1:
The patent segments the imaging process into multiple wavelength acquisitions (405nm, 532nm, 650nm) with dedicated processing for each wavelength channel. This segmentation allows precise measurement at each wavelength while managing complexity through modular processing of separate spectral components
Solution Approach 2:
The patent transitions from single-wavelength 2D imaging to multi-wavelength 3D spectral-spatial imaging. By adding the wavelength dimension, the system achieves color bias-free activity prediction through analysis of speckle patterns across multiple spectral dimensions
2Measurement precision
If laser spot imaging with limited scan area is used, then device complexity is reduced, but measurement precision deteriorates due to restricted observation area
Solution Approach 1:
The patent merges multiple laser spot measurements into a comprehensive multispectral image by capturing speckle patterns from multiple wavelengths simultaneously across the entire sample area, combining local spot measurements into a global activity map
3Adaptability or versatility
If conventional methods focusing on healthcare domain are used, then device complexity is minimized, but adaptability deteriorates due to domain-specific optimization
Solution Approach 1:
The patent creates a universal multispectral laser speckle imaging system that can predict activity across diverse applications (seed germination, fungal infection detection, healthcare) by implementing domain-agnostic processing algorithms that work with any sample type
4Measurement precision
If single spectrum imaging is used, then device complexity is reduced, but measurement precision deteriorates due to color bias in samples
Solution Approach 1:
The patent uses composite multispectral imaging by combining images from multiple wavelengths (405nm, 532nm, 650nm) to create a composite activity map that eliminates color bias through spectral diversity
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
The method achieves accurate activity prediction by selecting optimal color-coded regions of interest, effectively eliminating color bias and enhancing prediction accuracy.
Implementation Method 1
Laser speckle analysis is a non-contact technique widely used in applications such as seed quality analysis, detection of fungal infections in fresh products, estimation of blood flow velocities
Data Source
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AI summary
Multispectral laser speckle analysis techniques have been widely used for applications such as the analysis of seed qualities, detection of fungal infections in fresh produce, estimation of blood flow velocities, and the like. However, the conventional methods fail to address color bias in images which is a major concern in terms of accuracy in applications like activity prediction. and the present disclosure provides a novel image processing algorithm which performs activity prediction without any color bias. Here, an RGB input image is segmented, and mask is generated. Distance based clustering is performed on the masked image to obtain cluster map. Simultaneously, color coded activity map is generated for each color component of the image. Further, the color coded activity map is multiplied with cluster contrast values and thereby an optimal color coded activity region is selected for activity map generation.