Second Derivative Edge Detection with Orientation Analysis
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Solution Overview
Problem
Current edge detection methods using first and second order derivatives in image processing produce thick edges and require expensive post-processing to determine orientation, which complicates object identification and increases system resource usage.
Innovation Solution
An image processing device and method that utilizes the second order derivative of the intensity distribution function to identify edges and determine edge orientation directly, without additional post-processing, by separating gradient changes into horizontal and vertical components and using angle masks to classify edge orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If first order derivative is used to identify edges, then edge location and orientation can be obtained, but the edges produced are thick which increases complexity in downstream processing
Solution Approach 1:
The patent segments the edge detection process into two distinct stages: first using second order derivative to obtain thin edge locations, then applying separate orientation detection methods (such as gradient analysis or Hough transform) to determine edge angles. This segmentation allows each stage to optimize for its specific function, producing thin edges that are easier to process downstream while still providing orientation information through the segmented approach.
2Device complexity
If second order derivative is used to identify edges, then thin edges are produced, but the zero-crossings are directionless and lack inherent edge orientation information
Solution Approach 1:
The patent uses an intermediary approach by introducing additional processing steps that mediate between the thin edges produced by second order derivative and the need for orientation information. This may involve using gradient calculations as an intermediary to derive orientation from the edge locations, or applying Hough transform as an intermediary mechanism to extract orientation data from the thin edge maps generated by second order derivative methods.
3Loss of information
If post-processing is applied to determine edge orientation from second order derivative, then orientation information can be obtained, but it requires significant extra processing that reduces real-time capability
Solution Approach 1:
The patent applies preliminary action by performing orientation detection in parallel with or immediately following the second order derivative calculation, rather than as a separate post-processing step. This may involve calculating gradients simultaneously with the Laplacian or using pre-computed gradient information to determine orientations from the zero-crossings, thereby reducing the overall processing time and maintaining real-time capability while still extracting orientation information.
Data Source
AI summary
An improved algorithm for edge detection is provided that utilizes the second order derivative of the intensity distribution function of an image to provide edge orientation information for the detected edges. An exemplary embodiment of the improved algorithm includes determining the second order derivative of the intensity distribution function, identifying zero-crossings in the horizontal and vertical directions, assigning angle information to the identified zero-crossings in the horizontal and vertical directions; and identifying an edge orientation based the assigned angle information for the horizontal and vertical zero-crossing of the edge and adjacent edges.