Dynamic Threshold Setting for AR Marker Detection
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Solution Overview
Problem
Existing augmented reality technologies face challenges in accurately detecting marker images, particularly in varying lighting conditions, where the contrast of the marker object within the frame image changes, making it difficult to set an optimal threshold for binarization processes.
Innovation Solution
A threshold setting device and method that acquires ratio information related to the colors present in a specific object, using this information to set a threshold for binarization processes, ensuring proper detection and representation of the marker object's shape features, even in environments with changing light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed threshold is used for binarization, then the processing speed is fast, but the detection accuracy deteriorates under varying lighting conditions
Solution Approach 1:
The patent dynamically changes the binarization threshold parameter based on the color ratio of the marker object. Instead of using a fixed threshold, the system calculates the ratio of red, green, and blue colors in the marker region and adjusts the threshold accordingly to maintain detection accuracy under varying lighting conditions
Solution Approach 2:
The system implements feedback by continuously monitoring the color ratio of the marker object and using this information to adjust the binarization threshold. The color ratio calculation provides feedback about the current lighting conditions, which is then used to optimize the threshold for accurate marker detection
2Measurement precision
If adaptive threshold adjustment is implemented, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent extracts only the essential color ratio information from the marker region to determine the threshold, rather than performing complex adaptive thresholding on the entire image. This extraction of key features (color ratios) maintains detection accuracy while significantly reducing processing time
Solution Approach 2:
The system segments the image processing into distinct steps: first identifying the marker region, then calculating color ratios within that region, and finally applying the threshold. This segmentation allows efficient processing by focusing computational resources only on the relevant marker area
3Measurement precision
If color ratio information is utilized, then threshold setting accuracy improves, but the complexity of the binarization process increases
Solution Approach 1:
The color ratio calculation serves multiple functions: it characterizes the marker object, indicates lighting conditions, and determines the binarization threshold. This multi-functionality reduces the need for separate processing steps, maintaining simplicity while improving accuracy
Solution Approach 2:
The marker object itself provides the information needed for threshold setting through its color ratio. The system uses the inherent color properties of the marker to automatically determine the appropriate threshold, eliminating the need for external calibration or manual intervention
Data Source
AI summary
A threshold setting device, an object detection device, a threshold setting method, and a computer readable storage medium are shown. According to one implementation, the threshold setting device includes, an image acquisition unit, a ratio acquisition unit, and a setting unit. The image acquisition unit acquires an image including a specific object. The ratio acquisition unit acquires ratio information related to a ratio of a plurality of colors present in the specific object. The setting unit sets, based on the ratio information acquired by the ratio acquisition unit, a threshold used in a binarization process performed on the image including the specific object acquired by the image acquisition unit.