Mobile Image Measurement Using Depth Sensing and Reference Surfaces
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Solution Overview
Problem
Existing image processing systems, such as Apple ARKit, lack accuracy in object detection and scale determination, leading to inconsistencies in measurements, which are unsuitable for applications requiring high precision like footwear fitting and custom fabrication.
Innovation Solution
A method using a mobile device to capture images with metadata, including acceleration and orientation data, and employing depth sensors to calculate the scale and position relative to a reference surface, combined with machine learning classifiers for precise object detection and measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing systems (e.g., Apple ARKit) are used for object detection and measurement, then the system complexity remains low and ease of operation is maintained, but measurement precision deteriorates with inconsistencies exceeding 2 millimeters
Solution Approach 1:
The patent combines multiple data sources including image data, accelerometer data, gyroscope data, and depth sensor data into a unified measurement system. This integration of multiple sensors and data types enables the system to achieve millimeter-level precision by compensating for individual sensor limitations through data fusion and cross-validation
Solution Approach 2:
The patent introduces machine learning classifiers as intermediary processing layers between raw sensor data and final measurements. These classifiers analyze patterns in the combined sensor data and generate corrected measurement values, acting as a mediator that transforms raw data into precise measurements while managing system complexity through algorithmic processing
2Productivity
If automated object detection and measurement systems are implemented, then productivity increases by eliminating manual measurement processes, but measurement precision deteriorates due to lack of professional measurement expertise
Solution Approach 1:
The patent implements a self-service measurement system where the mobile device automatically captures images, collects sensor data, processes measurements, and generates results without requiring manual intervention. The system performs self-calibration and self-correction using onboard sensors and machine learning algorithms, enabling automated operation while maintaining precision that would otherwise require trained professionals
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors measurement data quality and adjusts processing parameters accordingly. Machine learning classifiers analyze measurement results and provide feedback for iterative improvement, allowing the system to learn from each measurement and maintain high precision across diverse measurement scenarios
3Measurement precision
If detailed instructions and alignment indicators are provided to users, then measurement precision improves through proper device positioning, but ease of operation deteriorates due to increased complexity of the measurement process
Solution Approach 1:
The patent introduces alignment indicators and instructional interfaces as intermediary elements that guide users through the measurement process. These visual aids serve as mediators between the complex measurement requirements and the user, translating technical positioning requirements into simple visual cues that users can follow without specialized knowledge
Solution Approach 2:
The patent implements automatic device alignment and positioning assistance where the system provides real-time feedback to guide users in achieving proper measurement conditions. The mobile device automatically detects and indicates alignment status, allowing users to self-correct their positioning based on visual feedback without requiring training in measurement techniques
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
Achieves accurate object measurements within 2 millimeters of the actual size, enabling personalized product recommendations and custom fabrication.
Implementation Method 1
By measuring the movement of the mobile device to the floor and during pick up, the height from which the image was taken and the movement graph of the mobile device during the way from the hand to the ground can be calculated
Implementation Method 2
A method using a mobile device to capture images with metadata, including acceleration and orientation data
Data Source
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AI summary
Systems, methods, and apparatuses described herein may provide image processing, including displaying, by a mobile device, an image of an object located perpendicular to a reference object, calculating, based on at least one depth measurement determined using a depth sensor in the mobile device, the predicted height of the mobile device when the image was captured, calculating scale data for the image based on the predicted height, determining a reference line identifying the location of the object and the reference object in the image, segmenting pixels in the object in the image from pixels in the image outside the object, measuring the object based on the pixels in the object and the scale data, and generating model data comprising the object, the scale data, and the measurements.