Encoded Surface Tile Segmentation for Spatial Dimension Measurement
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Solution Overview
Problem
Current technologies face challenges in accurately determining the spatial dimensions of encoded surfaces, such as shipping boxes and containers, using digital watermarking and signal encoding, especially when the encoded signals are printed with low contrast or on materials like black plastic, which can lead to difficulties in signal detection and measurement.
Innovation Solution
The method involves obtaining an image of the encoded surface, detecting raw data signal tiles, and calculating the height and length by multiplying the total number of tiles in the vertical and horizontal directions by the side length of each tile, using a camera and multi-core processors to process the image and determine the spatial dimensions, and incorporating synchronization signals for accurate tile alignment and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If digital watermarking is used to encode signals on surfaces with low contrast or dark materials, then data capacity and tracking capability are improved, but signal detection accuracy and measurement precision deteriorate
Solution Approach 1:
The encoded surface is divided into multiple discrete tiles, each containing synchronization signals and data signals. This segmentation allows the system to detect and measure individual tiles independently, improving overall measurement precision even when the entire encoded surface has low contrast. The tile structure enables localized detection that is less affected by background interference.
Solution Approach 2:
The patent employs synchronization signals with distinct visual characteristics that differ from the background surface. These synchronization signals use contrasting colors or patterns that stand out against dark or low-contrast materials, enabling reliable detection and measurement without requiring the entire encoded surface to have high contrast.
2Area of stationary object
If multiple tiles are used to cover larger surface areas, then encoding capacity and dimensionality are improved, but device complexity and processing requirements increase
Solution Approach 1:
The large encoded surface is divided into multiple standardized tiles, each with a fixed size and structure containing synchronization and data signals. This segmentation allows the processing system to handle each tile independently using the same detection algorithms, reducing overall complexity compared to processing a single large continuous encoded area.
Solution Approach 2:
Each tile is designed with a universal structure that includes synchronization signals for alignment and data signals for encoding. This standardized tile design allows the same detection and measurement processes to be applied universally across all tiles, simplifying the processing requirements for large encoded surfaces.
3Adaptability or versatility
If tile-based encoding is used to determine spatial dimensions, then manufacturing flexibility and adaptability are improved, but detection and measurement difficulty increase
Solution Approach 1:
Synchronization signals are designed with distinct visual characteristics that contrast with the surrounding tiles and background. This visual distinction makes it easier for detection systems to identify tile boundaries and positions, reducing the difficulty of detecting and measuring tiles even as the number of tiles increases for greater manufacturing flexibility.
4Extent of automation
If the camera is positioned at a fixed distance from the conveyor belt, then system simplicity and automation are improved, but measurement accuracy deteriorates due to perspective distortion
Solution Approach 1:
The fixed-distance camera system detects individual tiles independently rather than attempting to measure the entire encoded surface at once. This segmentation approach reduces the impact of perspective distortion on overall measurement accuracy, as each tile is measured within a smaller field of view where distortion effects are minimized.
Solution Approach 2:
The system uses synchronization signals with known spatial parameters and patterns to compensate for perspective distortion. By detecting the known structure of synchronization signals in each tile and comparing them to expected patterns, the system can calculate correction factors that restore measurement accuracy despite the fixed camera position and resulting perspective effects.
Data Source
AI summary
The present disclosure relates generally to signal encoding for containers such as shipping boxes, food containers, wrapped items such as pallets. One aspect of the technology relates to an image processing method for determining spatial dimensions of an encoded surface. The method comprises: obtaining an image depicting the encoded surface, in which the encoded surface comprises one or more raw data signal tiles printed thereon, with each tile comprising a side length N in inches or centimeters; detecting each of the one or more raw data signal tiles from the obtained image; determining a total number of tiles in a vertical direction, and determining a total number of tiles in a horizontal direction; determining a height and length of the encoded surface by multiplying each of the total number of tiles in a vertical direction and the total number of tiles in a horizontal direction by the side length N. Other aspects are described as well.