Symbol Evaluation via Bit Depth Conversion
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Solution Overview
Problem
Existing systems for evaluating symbols, such as barcodes, on moving objects or in non-ideal lighting conditions require multiple image acquisitions and iterative adjustments, which can be impractical and disrupt production processes.
Innovation Solution
A method that involves acquiring a single image with a higher bit depth and deriving a second image with a lower bit depth, where the saturation threshold is determined to preserve a target relationship between saturation and image brightness, allowing for accurate symbol evaluation without additional image acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple image acquisitions and iterative adjustments are used to evaluate symbols in non-ideal conditions, then measurement precision is improved, but productivity deteriorates due to disrupted production processes
Solution Approach 1:
The system performs preliminary determination of saturation thresholds and brightness measurements from the first image before generating the second image. This preliminary analysis allows the system to pre-calculate the mapping parameters needed for symbol evaluation, eliminating the need for multiple iterative image acquisitions and adjustments during production.
Solution Approach 2:
The system creates a derived second image from the first acquired image through digital processing and bit depth conversion. This copying approach allows multiple evaluations to be performed on the derived image without requiring additional physical image acquisitions, thus maintaining productivity while enabling precise symbol evaluation.
2Productivity
If a single image with higher bit depth is acquired and processed, then productivity is improved by eliminating multiple acquisitions, but device complexity increases due to bit depth conversion processing
Solution Approach 1:
The system changes the bit depth parameter of the image data from a higher bit depth (e.g., 12-bit or 16-bit) to a lower bit depth (e.g., 8-bit) through deterministic processing. This parameter transformation allows the system to acquire only a single high-bit-depth image while still producing output compatible with standard processing pipelines, improving productivity without requiring multiple acquisitions.
Solution Approach 2:
The system introduces an intermediary processing step that deterministically converts the high-bit-depth first image into a lower-bit-depth second image using pre-determined saturation thresholds and brightness measurements. This intermediary processing layer simplifies the overall system architecture by handling the complexity of bit depth conversion in a single deterministic operation rather than requiring multiple acquisition devices or complex real-time adjustment mechanisms.
3Ease of operation
If bit depth is reduced from first image to second image, then ease of operation is improved by simplifying processing, but loss of information may occur during conversion
Solution Approach 1:
The system applies local quality assessment by determining saturation thresholds and brightness measurements specific to each image before conversion. By analyzing the actual content and characteristics of the first image, the system preserves critical information during bit depth reduction while discarding only redundant data. This localized approach ensures that symbol evaluation information is maintained even as overall bit depth is reduced for easier processing.
Solution Approach 2:
The system performs preliminary determination of saturation thresholds and brightness measurements from the first image before the bit depth conversion occurs. This preliminary analysis allows the system to pre-calculate the optimal mapping parameters that will preserve essential image information during the conversion to lower bit depth, minimizing information loss while simplifying subsequent processing operations.
Data Source
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AI summary
Evaluating a symbol on an object can include acquiring a first image of the object, including the symbol. A second image can be derived from the first image based upon determining a saturation threshold for the second image and possibly scaling of pixel values to a reduced bit-depth for the second image.