Optical Pattern Decoding via Region Localization and Zoom
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Solution Overview
Problem
Mobile devices struggle to efficiently decode optical patterns in real scenes due to issues like latency, auto-focus and auto-exposure features, and limited processing power, which can result in underexposed, overexposed, or out-of-focus images, especially when dealing with multiple patterns in complex environments.
Innovation Solution
The system employs a camera with processors and memory to acquire and process images by localizing optical patterns, adjusting field of view, and implementing exposure and focus control algorithms to improve decoding performance, including automatic exposure control, zoom control, and multi-threading strategies to reduce latency and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard device features like auto-focus and auto-exposure are used, then ease of operation is improved, but latency increases and decoding performance deteriorates
Solution Approach 1:
The patent extracts and disables standard device features like auto-focus and auto-exposure that cause latency. By removing these automatic processing steps, the system achieves faster decoding performance while still maintaining operational simplicity through the core scanning functionality.
Solution Approach 2:
The system changes operational parameters by disabling automatic features and using manual or pre-configured focus and exposure settings. This parameter adjustment reduces processing latency while maintaining ease of use through simplified operation modes.
2Ease of operation
If standard device features like auto-focus and auto-exposure are used, then ease of operation is improved, but decoding accuracy worsens due to underexposed, overexposed, or out-of-focus images
Solution Approach 1:
The patent removes problematic auto-focus and auto-exposure features that produce degraded images. By extracting these harmful automatic adjustments, the system prevents underexposed, overexposed, and out-of-focus images, thereby improving decoding accuracy.
Solution Approach 2:
The system converts the potential harm of automatic features by deliberately disabling them and using fixed or manually optimized settings. This approach transforms what could be harmful automatic adjustments into beneficial consistent, predictable imaging parameters that improve decoding reliability.
3Adaptability or versatility
If image processing is performed on the entire scene, then comprehensive analysis is improved, but computational resources and processing time increase
Solution Approach 1:
The patent segments the image processing task by first analyzing the entire scene to locate optical patterns, then focusing processing resources only on regions containing patterns. This segmentation reduces overall computational resources while maintaining comprehensive analysis capability.
Solution Approach 2:
The system performs preliminary action by first scanning the entire scene to identify pattern locations, then uses this preliminary information to guide subsequent focused processing. This preliminary detection step enables efficient resource allocation and reduces overall processing energy consumption.
4Adaptability or versatility
If the camera captures a wide field of view to detect multiple patterns, then adaptability is improved, but the ability to focus on specific patterns for accurate decoding deteriorates
Solution Approach 1:
The patent segments the field of view into multiple regions, each potentially containing optical patterns. By dividing the wide scene into detectable segments, the system maintains adaptability to detect multiple patterns while enabling focused analysis of individual pattern regions for accurate decoding.
Solution Approach 2:
The system transitions from a two-dimensional wide field of view to a hierarchical structure that adds a spatial organization dimension. By organizing the scene into regions and focusing on specific pattern locations, the system maintains comprehensive detection while achieving precise pattern recognition through dimensional organization.
Data Source
AI summary
For scanning optical patterns, such as two-dimensional QR codes, with a mobile device at increased distances, a first image is acquired. A region of interest likely containing the optical pattern in the first image is identified. The mobile device then zooms in on the region of interest and a second image is acquired. The optical pattern is then decoded using the second image.