Spectral Profile Matching for Material Identification
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Solution Overview
Problem
Current photographic processes have difficulty distinguishing between similar colors, such as a black velvet jacket and black leather pants, requiring manual intervention by photographers to separate areas for post-processing, which is time-consuming and inefficient.
Innovation Solution
The solution involves matching spectral profiles of objects in a scene to identify materials and storing this information in metadata, allowing for automatic identification of distinct areas during post-capture rendering, using a multi-spectral digital camera that captures and processes spectral data to differentiate between materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral profile matching is performed on the entire scene, then material identification accuracy is improved, but memory consumption and processing time increase
Solution Approach 1:
The patent divides the scene into multiple regions of interest (ROIs) based on visual similarity and spectral characteristics. Instead of processing the entire scene uniformly, the system segments the image into distinct areas that require spectral analysis, thereby reducing the total computational load while maintaining identification accuracy for the regions that matter most.
Solution Approach 2:
The patent applies different processing qualities to different regions of the scene. High-resolution spectral profile matching is applied selectively to regions where material differentiation is critical (such as clothing items with similar colors), while lower-resolution or no processing is applied to homogeneous background regions, optimizing the balance between accuracy and resource consumption.
2Measurement precision
If spectral profile matching is performed on the entire scene, then material identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the scene into multiple regions of interest (ROIs) based on visual similarity and spectral characteristics. Instead of processing the entire scene uniformly, the system segments the image into distinct areas that require spectral analysis, thereby reducing the total computational load while maintaining identification accuracy for the regions that matter most.
Solution Approach 2:
The patent performs preliminary visual analysis and spectral preprocessing to identify and prioritize regions of interest before applying full spectral profile matching. This preliminary action filters out regions that can be easily differentiated or are not relevant, allowing the system to allocate processing time more efficiently to the most challenging and important areas.
3Ease of operation
If manual identification of distinct areas is performed, then post-processing control is improved, but time consumption increases
Solution Approach 1:
The patent implements automatic material identification and region segmentation using spectral profile matching, allowing the system to perform the analysis itself without requiring manual intervention from the photographer. The system self-determines which regions require attention and provides structured metadata that facilitates automated post-processing workflows.
Solution Approach 2:
The patent provides feedback to the user in the form of structured metadata that automatically identifies materials and regions of interest. This feedback enables the user to quickly understand the scene composition and material distribution, allowing for rapid verification or adjustment without requiring manual analysis of the entire image.
Data Source
AI summary
Image data of a scene is captured. Spectral profile information is obtained for the scene. A database of plural spectral profiles is accessed, each of which maps a material to a corresponding spectral profile reflected therefrom. The spectral profile information for the scene is matched against the database, and materials for objects in the scene are identified by using matches between the spectral profile information for the scene against the database. Metadata which identifies materials for objects in the scene is constructed, and the metadata is embedded with the image data for the scene.