Color Object Detection Using Noise-Added YCbCr and HSV Thresholding
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Solution Overview
Problem
Existing image processing methods for detecting color objects in digital images face challenges in robustness against environmental changes and illumination variations, leading to difficulties in accurate detection, especially when using cameras with varying performance.
Innovation Solution
The method involves generating a noise image by adding an additive color to the source image, converting it into YCbCgCr and HSV formats, and using threshold values to detect objects in both formats, allowing for robust color object detection by combining the results from these images, while the light emitting apparatus adjusts its color and brightness to facilitate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional color-based object detection methods are used, then detection speed is improved, but detection accuracy and robustness against environmental changes deteriorate
Solution Approach 1:
The patent segments the color detection process into two independent parallel paths: YCbCr space detection (using luminance Y and chrominance CbCr) and HSV space detection (using hue S and value V). Each path processes color information independently with its own thresholding strategy, and the final detection result combines both paths. This segmentation allows each path to be optimized for its specific strength while maintaining overall detection accuracy across varying environmental conditions.
2Device complexity
If simple threshold-based detection is used, then processing complexity is reduced, but detection precision under varying illumination conditions deteriorates
Solution Approach 1:
The patent transforms the detection problem by changing the color parameter space from standard RGB to two alternative representations: YCbCr (luminance-chrominance) and HSV (hue-saturation-value). Each transformation uses different parameter combinations that are more robust to specific types of illumination variations. The YCbCr transformation separates luminance and chrominance, making it resistant to brightness changes, while HSV separates hue from intensity, making it resistant to shading effects. This parameter transformation maintains relatively simple threshold-based processing while significantly improving detection precision.
Data Source
AI summary
A system for detecting a light emitting apparatus, the system including a light emitting apparatus for emitting a color, a camera configured to obtain a source image that includes a target object having the color, a memory apparatus storing software configured to detect the target object by generating a noise image by adding an additive color to the source image, detecting objects in a YCbCgCr image and an HSV image with respect to the noise image by using respective threshold values of the YCbCgCr image and the HSV image, and combining the YCbCgCr image and the HSV image in which the objects have been detected, and a control circuit configured to detect the target object in the source image by using the software.