Optical Sensor Edge Detection with Adaptive Shutter Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing edge detection techniques in optical navigation sensors, such as optical mice, suffer from inaccuracy due to changes in shutter frequency, which affect the brightness of captured pixels and lead to variations in determining the edge location, especially when using adaptive shutters.
Innovation Solution
The method calculates the leading edge location using a sensor distribution and adaptive threshold settings based on average pixel values, defining a weighted location in the leading half of the image, rather than relying on fixed thresholds and sensor ranges, thereby reducing inaccuracies across different shutter speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed thresholds are used for edge detection, then the method is simple and easy to implement, but the accuracy deteriorates when shutter frequency changes
Solution Approach 1:
The patent transforms the static fixed thresholds into dynamic adaptive thresholds that automatically adjust based on the shutter frequency. The threshold values are calculated using the formula: threshold1 = (min_pixel_value + max pixel value) / 2 - offset1, and threshold2 = (min pixel value + max pixel value) / 2 + offset2, where the offsets are adjusted according to the detected shutter frequency. This dynamic adaptation maintains edge location accuracy across varying shutter speeds while preserving the simplicity of the overall method.
Solution Approach 2:
The patent changes the threshold parameters dynamically based on shutter frequency conditions. When the shutter frequency is detected to be within a first range, a first set of threshold values is applied; when within a second range, a second set of threshold values is applied. This parameter change strategy allows the system to maintain optimal edge detection accuracy across different operating conditions without complex recalibration.
2Illumination intensity
If adaptive shutter rate is used to adjust brightness, then the image quality improves, but the edge location accuracy deteriorates due to variation in pixel values
Solution Approach 1:
The patent implements a feedback mechanism where the shutter frequency is continuously monitored and fed back into the threshold calculation. The system detects the actual shutter frequency used during image capture and uses this information to dynamically adjust the edge detection thresholds. This feedback loop compensates for the brightness changes introduced by adaptive shuttering, maintaining consistent edge location accuracy across different shutter speeds.
Solution Approach 2:
The patent adjusts the threshold parameters based on the detected shutter frequency ranges. By categorizing shutter frequencies into different ranges and applying corresponding threshold sets, the system compensates for the pixel value variations caused by different exposure times. This parameter adaptation ensures that edge detection remains accurate regardless of the adaptive shutter rate used for brightness optimization.
3Device complexity
If the same thresholds are used for different shutter speeds, then the algorithm remains simple, but the measurement precision varies significantly
Solution Approach 1:
The patent introduces dynamic threshold adjustment based on shutter frequency detection. The system monitors the actual shutter speed and automatically selects appropriate threshold values from predefined sets or calculates adaptive thresholds in real-time. This dynamic approach maintains algorithm simplicity while significantly improving edge location consistency across different shutter speeds, reducing measurement variation from over 1 pixel to under 0.1 pixel accuracy.
Data Source
AI summary
A method for determining leading edge location in an image captured by an optical navigation sensor with auto shutter adaptation includes: plotting average pixel values per sensor column on a graph; determining an average value according to the average pixel values as a threshold value; defining a weighted location in the leading half of the captured image according to the threshold value; and determining the leading edge location according to the defined weighted location.