Perspective Distortion Correction for Optical Pattern Decoding
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Solution Overview
Problem
Existing methods for decoding optical patterns, such as barcodes, on mobile devices are limited by processing power and accuracy, especially in environments with multiple patterns, varying orientations, and uneven lighting, which increases computational demands and latency.
Innovation Solution
The use of depth data from laser-ranging systems, combined with image data, for semantic image segmentation and perspective distortion correction, enables efficient detection and decoding of optical patterns by segmenting images into foreground and background regions and correcting for perspective distortions, thereby reducing computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image analysis is used to decode barcodes on mobile devices, then decoding capability is provided, but processing power requirements increase and accuracy decreases in complex environments
Solution Approach 1:
The patent segments the image processing task by using depth data to identify and isolate the barcode region from the rest of the image. This segmentation allows the system to focus computational resources only on the relevant barcode area rather than analyzing the entire image, thereby reducing processing power requirements while maintaining decoding capability in complex environments
2Measurement precision
If traditional image analysis methods are used without depth data, then device complexity is lower, but accuracy decreases in environments with multiple patterns, varying orientations, and uneven lighting
Solution Approach 1:
The patent introduces depth data as an intermediary element that mediates between the camera image and the barcode detection process. This depth information acts as an additional dimension that helps distinguish the barcode from other patterns and elements in the scene, improving detection accuracy in complex environments without requiring complex image processing algorithms
3Measurement precision
If semantic image segmentation using depth data is implemented, then detection accuracy improves in complex environments, but computational demands and latency increase
Solution Approach 1:
The patent performs preliminary action by using depth data to pre-identify and segment the barcode region before applying detailed image analysis. This preliminary segmentation based on depth information reduces the amount of data that requires intensive processing, thereby maintaining high detection accuracy while reducing overall processing latency
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
This approach enhances the speed and accuracy of optical pattern detection and decoding on mobile devices, reducing latency and improving performance in complex environments by focusing processing on regions of interest and correcting for distortions, allowing for quicker scanning of multiple patterns.
Implementation Method 1
receive a depth map of the real scene, wherein data for the depth map is acquired by the laser-ranging system
Data Source
AI summary
Depth information from a depth sensor, such as a LiDAR system, is used to correct perspective distortion for decoding an optical pattern in a first image acquired by a camera. Image data from the first image is spatially correlated with the depth information. The depth information is used to identify a surface in the scene and to distort the first image to generate a second image, such that the surface in the second image is parallel to an image plane of the second image. The second image is then analyzed to decode an optical pattern on the surface identified in the scene.