Hyperspectral Tag Reading Spatial Spectral Detection
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Solution Overview
Problem
Hyperspectral imaging systems face challenges in storage and processing capabilities due to the need for large amounts of data for location and decoding, which exceeds the capacity of existing readers after object detection.
Innovation Solution
Implementing a process that splits detection and decoding operations, using sequential processing to identify regions of interest by analyzing standard deviation and filtering along the spectral axis, and only storing and processing hyperspectral information from these regions, reducing data requirements and computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral imaging is used for object detection and decoding, then detection accuracy and spectral decoding capability are improved, but storage and processing capabilities are overwhelmed due to large data requirements
Solution Approach 1:
The patent segments the hyperspectral data cube by identifying and isolating regions of interest (ROIs) where tags are located, separating these from the background. By dividing the full spectral data into only the relevant ROI portions, the system reduces data volume while maintaining spectral decoding capability for the tags of interest.
Solution Approach 2:
The patent extracts only the necessary spectral information from regions where tags are detected, removing unnecessary data from areas without tags. This extraction process retrieves only the essential spectral signatures needed for decoding, eliminating redundant data that would overwhelm storage and processing capabilities.
2Measurement precision
If full hyperspectral data is stored and processed, then spectral decoding accuracy is improved, but processing time and computational resources are excessive
Solution Approach 1:
The patent performs preliminary detection to identify tag locations and regions of interest before conducting full spectral decoding. By pre-identifying where tags are present and defining ROIs in advance, the system can then process only the relevant spectral data in those regions, significantly reducing processing time while maintaining decoding accuracy.
Solution Approach 2:
The patent segments the spectral data processing into two stages: first identifying ROIs through detection, then performing detailed spectral decoding only in those segmented regions. This segmentation approach eliminates the need to process the entire hyperspectral cube, reducing computational resources and processing time while preserving accuracy for the tags of interest.
3Ease of operation
If detection and decoding are performed simultaneously on all data, then operational simplicity is maintained, but storage and processing capabilities are exceeded
Solution Approach 1:
The patent segments the operational process into sequential steps: first detection to identify tag locations and ROIs, then decoding only for those regions. This segmentation maintains operational simplicity through automated sequential processing while reducing the complexity requirements for storage and processing capabilities by eliminating unnecessary data handling.
Solution Approach 2:
The patent extracts and processes only the necessary data portions (ROIs containing tags) rather than handling the complete dataset. This extraction approach maintains ease of operation through automated processing while reducing storage and processing capability requirements by eliminating unnecessary data from the operational workflow.
Data Source
AI summary
A system for determining a spectrum includes an interface and a processor. The interface is configured to receive a sample set of intensity data for an array of spatial locations and a set of spectral configurations. The processor is configured to determine a region of interest using the sample set of intensity data and determine a spectral peak for the region of interest.