Spherical Object Edge Refinement Using Radial Pixel Paths
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Solution Overview
Problem
Existing edge detection algorithms struggle to accurately identify the boundaries of spherical objects in images, particularly when they are obscured by shadows or illumination issues, leading to inaccurate edge detection and potential omission of edges.
Innovation Solution
A method involving pixel intensity difference analysis, thresholding, and filtering is employed to refine the edges of spherical objects by segmenting foreground and background pixels, selecting a subset of background pixels along radial paths, and applying a filter to remove false positives, thereby defining the object's boundary accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing edge detection algorithms are used, then processing speed is maintained, but edge detection accuracy deteriorates for spherical objects obscured by shadows or illumination issues
Solution Approach 1:
The patent segments the image into multiple radial paths extending from the center of the spherical object. Each path is processed independently to identify edge pixels, allowing the system to handle complex illumination patterns by dividing the problem into simpler radial segments rather than processing the entire object boundary at once
Solution Approach 2:
The patent introduces a radial dimension for edge detection by analyzing pixels along paths that extend outward from the object center at different angular degrees. This transforms the traditional 2D edge detection problem into a multi-dimensional analysis that captures edges more accurately even when obscured by shadows or illumination variations
2Productivity
If traditional edge detection methods are applied, then computational time is reduced, but the number of detected edges decreases due to false positives being omitted
Solution Approach 1:
The patent performs preliminary actions by first identifying the center of the spherical object and establishing radial paths before detecting edges. This preliminary setup allows the subsequent edge detection to be more efficient and accurate, reducing false positives while maintaining fast processing speeds
Solution Approach 2:
The patent applies different processing quality to different regions by analyzing pixels along radial paths with varying angular degrees. Each radial path is processed according to its specific characteristics, allowing the system to maintain high edge detection quality while optimizing computational efficiency for each local region
3Reliability
If edge pixels are detected without filtering, then processing speed is maintained, but false positive edges are included in the results
Solution Approach 1:
The patent extracts and removes false positive edge pixels by filtering the detected edges based on their proximity to the object center and their position along radial paths. This extraction of incorrect edges improves reliability while the filtering process is integrated into the radial path analysis framework, minimizing additional complexity
Data Source
AI summary
Pixel intensity differences between pixel values of a region in a first image frame and the region in a second image frame are determined, where the region in the first image frame includes an image of a spherical object, with no overlapping spherical object in the second image frame. Based on the pixel intensity differences, the region of the first image frame is thresholded into foreground pixels and background pixels until the background pixels make up more than a predefined percentage of the region of the first image frame. From the background pixels, a subset of the background pixels located along a plurality of paths that radially extend outward from a center of the region in different angular degrees is selected. The subset of the background pixels is filtered by applying a filter, where the filtered subset is detected as edge pixels.