Gradient Run Identification for Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying objects in electronically acquired images struggle to effectively distinguish and group information-bearing pixels, particularly along object boundaries, due to reliance on first and second derivatives and threshold levels, which can lead to inefficiencies in edge detection and object recognition.
Innovation Solution
The method identifies one-dimensional gradient runs based on brightness trends and thresholds, forming second-order sets of gradient runs to systematically account for all possible interpretations using predefined rules, with a tree structure to validate consistent interpretations and ignore invalid ones, allowing for the identification of objects within pixel arrays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If first and second derivatives with threshold levels are used for boundary discovery, then edge detection can be performed, but the method leads to inefficiencies in edge detection and object recognition
Solution Approach 1:
The patent changes the fundamental parameters used for edge detection from derivative-based threshold methods to gradient run-based methods. Instead of computing first and second derivatives with fixed thresholds, the invention uses brightness gradient runs with defined start and end criteria, fundamentally altering the detection parameters to achieve both efficiency and accuracy
Solution Approach 2:
The patent substitutes the mechanical derivative calculation system with a gradient run tracking system. Rather than mechanically computing derivatives and applying thresholds, the invention tracks continuous gradient runs through the image, replacing the computational mechanism with a more efficient tracking approach
2Measurement precision
If gradient runs are used to identify objects, then object identification accuracy is enhanced, but the complexity of processing increases
Solution Approach 1:
The patent segments the image processing task into distinct gradient run identification stages. By dividing the processing into separate steps (identifying gradient runs, finding Grenze Sets, determining objects), the complexity is managed through modular segmentation rather than monolithic processing
Solution Approach 2:
The patent performs preliminary identification of gradient runs before forming Grenze Sets. This preliminary action organizes the data structure in advance, reducing the complexity of subsequent object identification steps by having gradient runs pre-identified and ready for grouping
Data Source
AI summary
A system and method for identifying an object in an electronically acquired image that includes at least one two-dimensional array of pixels. A plurality of one-dimensional gradient runs oriented along a common direction in the two-dimensional array of pixels is identified. A second-order set of gradient runs is formed by selecting a group of previously identified one-dimensional gradient runs. Each of the one-dimensional gradient runs in the group has a pixel that is offset along an axis perpendicular to the common direction from a pixel in a neighboring one-dimensional gradient run in the group. The object is identified in the array using the second-order set of gradient runs.