Spherical Object Orientation Measurement via Great Circle Analysis
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Solution Overview
Problem
Current machine vision techniques for measuring object orientation, particularly in clinical orthopedics for hip socket alignment and sports officiating, face limitations in accuracy and reliability, especially for spherical or hemispherical objects in both reflective and transmissive imaging scenarios.
Innovation Solution
A machine vision system and method that utilize marked or inherent great circles on spherical objects to measure orientation by analyzing imaged semiellipses, employing image preprocessing, model generation, and scoring to accurately determine geometric parameters, applicable to various domains including medical x-ray images, video frames of basketballs, and planetary body navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human identification of landmarks in images is used for orientation measurement, then the system is simple to operate, but accuracy and reliability are inherently fallible
Solution Approach 1:
The system automatically detects and measures orientation of spherical objects without requiring human identification of landmarks. The machine vision system performs self-service by autonomously identifying the object, detecting its orientation, and providing measurements, thereby eliminating human error while maintaining ease of use through automated operation.
Solution Approach 2:
The patent replaces the mechanical/manual process of human landmark identification with an automated machine vision system. The system uses image processing algorithms to automatically detect and measure orientation, substituting human visual inspection and manual measurement with computational automation that provides both accuracy and reliability.
2Device complexity
If traditional image processing techniques are used for orientation measurement, then the device complexity is low, but measurement precision and reliability are insufficient
Solution Approach 1:
The patent segments the image processing task into distinct computational stages: image acquisition, preprocessing, object detection, orientation calculation, and measurement output. This segmentation allows each stage to be optimized independently, achieving high measurement precision through specialized algorithms while keeping the overall system manageable through modular architecture.
Solution Approach 2:
The system changes key processing parameters dynamically, including threshold values for object detection, sampling rates for image analysis, and calculation methods for orientation. By optimizing these parameters for different measurement scenarios, the system achieves high precision without requiring overly complex device architecture.
3Measurement precision
If automated machine vision measurement is implemented, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent creates a universal machine vision system that can measure orientation of any spherical object regardless of size, material, or application context. The system performs multiple functions including detection, measurement, and analysis within a single integrated platform, reducing the need for multiple specialized devices and thereby managing complexity while maintaining high precision across diverse applications.
4Ease of manufacture
If manual landmark identification is used, then the system is easy to implement, but reliability and repeatability are limited
Solution Approach 1:
The patent replaces manual landmark identification with automated machine vision technology that consistently and reliably detects object orientation. This substitution eliminates the variability and subjectivity inherent in manual methods, providing repeatable measurements while maintaining ease of implementation through turnkey automated systems that require minimal setup or calibration.
Data Source
AI summary
Disclosed are methods and systems for measuring orientation parameters of spherical objects by image processing. Geometric shapes detected in a captured image are measured by generating and scoring models of the geometric shapes, where a high numerical score indicates a good match of the model to the image. The orientation parameters are computed from the geometric parameters measured from the image. A fast, precise, and accurate measurement requires generation and scoring be computationally efficient, meaning that many high-scoring models are generated in a short period of time. Disclosed are embodiments for a multistep measurement process for measuring the orientation parameters of an artificial hip joint from an x-ray image, measuring rotation of a basketball from video frame images, and navigating a spacecraft utilizing sunlight incident on a planetary body.