Multi-spectrum Segmentation for AR Object Tracking
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Solution Overview
Problem
Augmented reality (AR) systems face challenges in efficiently processing and analyzing multi-spectrum images due to the computational intensity of computer vision tasks, particularly on mobile devices with limited resources, which hinders the ability to track objects in real-time without overwhelming power constraints.
Innovation Solution
Implementing a multi-spectrum segmentation module that divides video feeds into smaller segments using a lightweight algorithm, allowing a secondary computer vision algorithm to process only relevant areas, thereby reducing processing complexity and optimizing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision algorithms process complete multi-spectrum images, then object tracking accuracy is improved, but power consumption increases and processing speed decreases
Solution Approach 1:
The patent divides the complete image into multiple segments or regions of interest, allowing the computer vision algorithm to process only relevant portions rather than the entire image. This segmentation reduces the computational load and power consumption while maintaining tracking accuracy in the critical regions.
Solution Approach 2:
The patent applies different processing qualities or levels of analysis to different regions of the image. High-precision processing is applied only to regions containing objects of interest, while other regions receive minimal or no processing, thereby reducing overall power consumption while preserving tracking accuracy where needed.
2Measurement precision
If computer vision algorithms analyze complete images, then tracking precision is improved, but processing time increases
Solution Approach 1:
The patent segments the image processing task into multiple smaller sub-tasks that can be processed in parallel or sequentially with reduced computational overhead. By dividing the complete image into regions of interest, the system achieves the same tracking precision with significantly reduced processing time.
Solution Approach 2:
The patent applies computer vision processing selectively to only the necessary portions of the image rather than analyzing every pixel. This partial action approach maintains tracking precision for objects of interest while avoiding unnecessary processing time spent on irrelevant image regions.
3Measurement precision
If complete image processing is performed, then analysis accuracy is improved, but device complexity requirements increase
Solution Approach 1:
The patent breaks down the complex task of complete image analysis into multiple simpler segmentation steps. Each segment can be processed with less complex algorithms, reducing the overall device complexity requirements while maintaining analysis accuracy through the systematic processing of divided regions.
4Measurement precision
If multi-spectrum image analysis is performed, then object identification accuracy is improved, but power consumption increases
Solution Approach 1:
The patent segments the multi-spectrum image data and processes only the relevant spectral bands and spatial regions for object identification. This selective processing maintains identification accuracy by focusing computational resources on discriminative features while reducing power consumption through minimized data processing volume.
Data Source
AI summary
A device for multi-spectrum segmentation for computer vision is described. A first optical sensor operates within a first spectrum range and generates first image data corresponding to a first image captured by the first optical sensor. A second optical sensor operates within a second spectrum range different from the first spectrum range and generates second image data corresponding to a second image captured by the second optical sensor. The device identifies a first region in the first image, maps a first portion of the first image to a second portion of the second image data, provides the second portion of the second image data to a server that generates augmented reality content based on the second portion of the second image data. The device displays the augmented reality content.