Multi-FOV Image Partitioning for Mirrored Object Decoding
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Solution Overview
Problem
Scanning systems with multiple fields of view (FOVs) face challenges in handling different perspectives due to increased optical components and lengthened paths, particularly when dealing with mirrored objects, which complicate visual functionalities such as indicia decoding and optical character recognition.
Innovation Solution
An imaging device that captures and analyzes image data from multiple FOVs, determining constraints based on whether partitions are representative or mirrored, and performs image analysis operations by decoding and combining partial payloads from these views, using calibrated split lines and flags to correct mirrored perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple fields of view (FOVs) are incorporated to expand the range of view, then the coverage area is improved, but the number of optical components and path length increase
Solution Approach 1:
The imaging device divides the image data into multiple partitions corresponding to different FOVs captured by different optical assemblies. Each partition is processed independently with appropriate decoding constraints applied based on its perspective orientation, allowing the system to handle multiple views without requiring a single complex optical path
2Area of stationary object
If multiple fields of view (FOVs) are incorporated to expand the range of view, then the coverage area is improved, but the path length increases
Solution Approach 1:
The system segments the imaging function into multiple optical assemblies, each with its own FOV and path length. By processing partitions from different assemblies independently and combining results, the system achieves expanded coverage without requiring a single excessively long optical path
3Adaptability or versatility
If unequal number of mirrors are used in different portions to achieve differing directions of view, then the viewing perspective is improved, but the difficulty of performing visual operations increases
Solution Approach 1:
The system applies different decoding constraints to different partitions of the image data based on their local perspective characteristics. Each partition is identified as representing either a representative view or a mirrored view, and appropriate decoding algorithms are applied locally to each partition, making the system adaptable to different perspectives without overwhelming complexity
Solution Approach 2:
The system performs preliminary identification of partition orientation (representative vs. mirrored) before decoding. By determining the perspective characteristics of each partition in advance and applying appropriate constraints beforehand, the system simplifies the subsequent decoding process rather than attempting to handle all perspectives uniformly
4Area of stationary object
If multiple fields of view with different perspectives are incorporated, then the viewing coverage is improved, but the accuracy of visual operations decreases
Solution Approach 1:
The system ensures decoding accuracy by applying perspective-appropriate constraints to each partition individually. By identifying whether each partition represents a mirrored or representative view and applying the corresponding decoding algorithm, the system maintains high accuracy for visual operations like indicia decoding and OCR across all FOVs despite the variety of perspectives
Data Source
AI summary
Imaging devices, systems, and methods for performing image analysis operations on an object that is partially mirrored are described herein. An example device includes: an imaging assembly configured to capture image data of a first field of view (FOV) and a second FOV; and a computer-readable media storing machine readable instructions that, when executed, cause the imaging device to: capture the image data of the payload-encoding indicia, wherein an at least partially mirrored perspective of the second FOV is mirrored compared to a representative perspective of the first FOV; determine one or more constraints for at least some of the one or more partitions; and perform one or more image analysis operations on the image data based at least on the one or more constraints for the at least some of the one or more partitions.