Image Processing Graph Traversal for Region Reasoning
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Solution Overview
Problem
Existing image processing technologies face complexity and inefficiency in propagating image processing parameters through directed acyclic graphs, particularly when dealing with nodes having multiple parents, which complicates the calculation of regions of requested node output data.
Innovation Solution
The method involves identifying an image processing graph with nodes corresponding to image processing operations, traversing the graph in a first order based on depth-first search post-order, storing this order, and then reversing it for a second traversal to determine the region of requested node output data, thereby simplifying the propagation of image processing parameters without the need for breadth-first search or node marking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If breadth-first search with node marking is used to propagate parameters through the graph, then all nodes can be visited systematically, but the device complexity and computational overhead increase due to requiring mark arrays and multiple traversal passes
Solution Approach 1:
The patent inverts the traditional breadth-first search approach by using depth-first search post-order traversal instead. This inversion eliminates the need for node marking and complex queue management, as the post-order traversal naturally processes child nodes before parent nodes, ensuring all dependencies are resolved without requiring additional data structures for tracking visited nodes.
Solution Approach 2:
The patent extracts and removes the unnecessary node marking mechanism from the graph traversal process. By using depth-first search post-order traversal, the method eliminates the mark array and associated complexity while maintaining the ability to systematically propagate parameters through the graph, keeping only the essential traversal logic.
2Ease of operation
If depth-first search post-order traversal is used to identify domains of defined node output data, then the traversal order is simplified, but the region of requested node output data calculation becomes more challenging due to the non-intuitive processing order
Solution Approach 1:
The patent applies the inversion principle twice: first by using post-order traversal for domain identification, then by reversing this same traversal order for region calculation. This double inversion ensures that both operations use consistent, simplified logic where the same traversal sequence serves both purposes, eliminating the need for separate complex calculation mechanisms.
Solution Approach 2:
The traversal order identified through depth-first search post-order traversal serves multiple functions: it is used both for identifying domains of defined node output data and for calculating regions of requested node output data. This multi-functional use of the same traversal sequence simplifies the overall process by eliminating the need for separate traversal mechanisms.
3Manufacturing precision
If the graph is traversed multiple times with different traversal orders, then accurate region calculation is achieved, but the processing time and computational resources increase
Solution Approach 1:
The patent makes the depth-first search post-order traversal sequence serve dual purposes: first for domain identification and then for region calculation. By reusing the same traversal order for both operations, the method achieves accurate region calculation without requiring separate traversal passes, thereby reducing processing time while maintaining precision.
Data Source
AI summary
Systems, methods, and apparatus, including software tangibly stored on a computer readable medium, involve image processing. An image processing graph includes multiple nodes that each correspond to an image processing operation. Input image data relating to a particular input image is received. Region of interest data defining an output image region to be generated based on the input image data is received. A domain of defined node output data is identified for each of the nodes using the input image data, and the graph is traversed in a first traversal order. An identification of the first traversal order is stored. A region of requested node output data is identified for each of the nodes using the region of interest data, and the graph is traversed in a second traversal order. The second traversal order is based at least in part on the stored identification of the first traversal order.