Object Surface Matching Template for Flight Spin Measurement
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Solution Overview
Problem
Conventional systems face limitations in accurately measuring spin rate and spin axis of objects in flight, leading to inaccurate reconstruction of 3D flight paths and difficulty in tracking features like seams on symmetrical objects, such as baseballs, due to high rotation rates and overlapping region challenges.
Innovation Solution
The implementation of an object surface matching system using a template for flight parameter measurement, which defines a region of interest, normalizes energy levels, and generates rotation vectors to accurately measure spin and spin axis by comparing cropped images against pre-generated template images, accounting for symmetrical objects and optimizing image capture timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used to measure in-flight parameters, then basic speed measurement is possible, but spin rate and spin axis measurement accuracy deteriorates
Solution Approach 1:
The patent divides the measurement task into distinct segments: capturing multiple images at different time points, extracting features from each image separately, and then computing spin parameters through comparison. This segmentation allows for precise spin measurement by breaking down the complex motion analysis into manageable image processing steps.
Solution Approach 2:
The system performs preliminary actions by capturing multiple images before completing the spin measurement. Images are captured at predetermined time intervals during the object's flight, and feature extraction is performed in advance on each image. This preliminary capture and processing enables accurate spin rate and axis calculation through subsequent comparison of the pre-captured images.
2Productivity
If high rotation rates are used for tracking features like seams on baseballs, then more rotation information is captured, but tracking accuracy deteriorates due to overlapping regions
Solution Approach 1:
The system uses feedback by comparing features across multiple images captured at different time points. The spin measurement is derived from the relative rotation between images, and this comparative feedback allows the system to accurately track high-speed rotation by measuring the angular displacement between successive image frames rather than attempting to track continuous motion.
Solution Approach 2:
The patent captures more images than the minimum single pair needed for spin measurement. By capturing multiple images at predetermined time intervals throughout the flight, the system obtains excessive redundant data that improves measurement reliability and allows for selection of the best image pairs for comparison, thereby maintaining tracking accuracy even at high rotation rates.
3Measurement precision
If multiple images are captured for spin measurement, then spin accuracy improves, but processing time increases
Solution Approach 1:
The system captures multiple images at predetermined time intervals during flight, obtaining more data than the minimum required for spin measurement. This excessive action provides redundancy that allows for selection of optimal image pairs with the best feature visibility and least motion blur, thereby improving spin measurement accuracy while the predetermined timing optimizes processing efficiency.
Solution Approach 2:
Images are captured in advance at predetermined time intervals during the object's flight trajectory. This preliminary capture allows for offline processing where spin parameters can be calculated from the captured image sequence without time pressure, enabling accurate feature extraction and comparison to determine spin rate and axis from the pre-captured multiple images.
Data Source
AI summary
A method of object surface matching includes identifying an object in-flight in an image; identifying a feature on the object that is in a first spatial position; comparing the feature with set of template images; identifying a first template image in the set of template images that matches the feature on the object that is in the first spatial position; determining first coordinates for the first spatial position based on the first template image; identifying a second image of the object that includes the feature on the object that is in a second spatial position; identifying a second template image in the set of template images that matches the feature on the object that is in the second spatial position; determining second coordinates for the second spatial position based on the second template image; and generating a spin value for the object based on the first and second coordinates.