Target Image Shot Detection Using Color Subtraction and Verification
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Solution Overview
Problem
Existing shooting target systems require manual inspection to determine shot locations and scores, which can be cumbersome and limit real-time competition participation, especially when participants are not in the same physical location.
Innovation Solution
An automated image recognition system using a camera and computing device to analyze target images, identify shot locations, and communicate scores to remote users through a mobile device, employing image processing techniques like quantization, registration, and verification algorithms to accurately detect and verify shots on a target.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection is used to determine shot locations and scores, then the system is simple and requires minimal equipment, but the process is cumbersome and cannot support real-time competition across multiple locations
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated optical system. A camera captures images of the target, and image processing algorithms automatically identify shot locations and calculate scores, eliminating the need for manual target inspection while enabling real-time scoring and remote competition participation.
Solution Approach 2:
The system enables the target itself to provide scoring information automatically. By embedding machine-readable indicators on the target and using image recognition to read these indicators, the target self-identifies its shot locations and scores without requiring external manual inspection, thus improving productivity while maintaining relative system simplicity.
2Ease of operation
If automated image recognition is implemented to identify shot locations, then real-time scoring and remote competition are enabled, but the system becomes more complex requiring cameras, processing devices, and software algorithms
Solution Approach 1:
The patent introduces machine-readable indicators as an intermediary between the physical target and the digital scoring system. These indicators serve as a bridge that can be reliably detected by camera systems, enabling automated recognition while keeping the overall system architecture manageable through standardized communication between components.
Solution Approach 2:
The system performs preliminary image processing steps including color quantization to create palettes, registration to align images, and preprocessing to enhance features before final shot detection. These preliminary actions simplify the main detection task and make the system more robust, improving ease of operation while organizing the complexity into manageable processing stages.
3Measurement precision
If color quantization and palettes are used to identify shots, then accurate shot detection is achieved, but processing time and computational requirements increase
Solution Approach 1:
The patent applies color quantization to reduce the full color spectrum to a manageable palette of representative colors. This partial processing approach focuses computational effort on the most significant color variations that indicate shots, achieving accurate shot detection while limiting the time and computational resources required by processing only the essential color information rather than all pixel data.
Solution Approach 2:
The image processing is segmented into distinct stages: color quantization to create palettes, registration to align images, preprocessing to enhance features, and final shot detection. This segmentation allows each stage to be optimized independently, with color quantization reducing data complexity early in the process to minimize overall processing time while maintaining detection accuracy.
Data Source
AI summary
Aberrations on a surface, such as shots on a target, are identified through image processing techniques using color subtraction. An image of the surface is first initialized by shrinking the image and identifying pixel colors. A quantization algorithm is used to identify K number of colors, and acceptable variations for those colors are identified, and then stored in a color palette. Future images are analyzed by subtracting colors that match colors in the color palette. Remaining colors are aged in, and then compared with previously identified aberrations (shots). Image processing techniques then verify the potential aberrations. Data concerning verified aberrations are then displayed on remote user interfaces.