XR Device Clothes Lifetime Estimation via Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is no method in existing technologies to objectively estimate the lifetime of clothes, leading to user inconvenience in determining when favorite clothes need to be replaced due to wear and tear.
Innovation Solution
An XR device with a wireless communication module, camera, and display that captures images of clothes, generates damage information by comparing initial and current images, and estimates the clothes' lifetime based on this information, using light distortion and image binarization to determine bending and shape changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If no objective measurement method is used for clothes lifetime estimation, then the process is simple, but the measurement precision is poor
Solution Approach 1:
The clothes image is segmented into multiple regions including contour regions, bending regions, and damage regions. The controller divides the image processing task into distinct segments: contour extraction, bending detection through light distortion analysis, and damage identification, allowing each segment to be processed with appropriate algorithms for accurate lifetime estimation
Solution Approach 2:
Light distortion analysis serves as an intermediary method to detect clothes bending. The system uses light distortion patterns as a mediator to indirectly measure the degree of bending in clothes, which then contributes to the overall damage assessment and lifetime estimation without requiring direct physical contact with the fabric
2Measurement precision
If detailed image analysis is performed to extract clothes damage information, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The system performs preliminary actions by first extracting the contour of the clothes and identifying bending regions through light distortion analysis before conducting detailed damage detection. This preliminary segmentation prepares the image data in advance, allowing the subsequent damage detection to focus only on relevant regions rather than processing the entire image, thus reducing overall processing time while maintaining accuracy
Solution Approach 2:
The system applies partial action by focusing detailed image analysis only on specific regions of interest such as bending areas and potential damage zones identified through light distortion patterns, rather than uniformly processing the entire clothes image. This selective approach maintains measurement precision in critical areas while reducing unnecessary processing time in unaffected regions
Data Source
AI summary
An extended reality (XR) device can include a wireless communication module configured to transceive data with an external entity; a camera configured to capture a first image in front of the XR device; a display configured to include a transparent portion and display the first image; and a controller configured to extract a first clothes image based on the first image, generate first clothes damage information corresponding to the first clothes image based on the first clothes image and a second clothes image corresponding to an initial state of clothes in the first clothes image, generate a first augmented reality (AR) clothes image based on the first clothes image and the second clothes image, and display the first AR clothes image and the first clothes damage information.