Image Processing With Polygon Clustering for Symbol Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques are inefficient and error-prone when distinguishing between similar symbols due to treating pixels as isolated entities, leading to memory-intensive and time-consuming brute force scans.
Innovation Solution
The method involves scanning an input image to form clusters of polygons based on pixel states, using a 1-bit per pixel representation and generating a complete polygon profile to accurately identify characteristics, reducing ambiguity and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pixels are treated as isolated entities and brute force scanning is used to distinguish similar symbols, then processing can be performed, but the process becomes memory-intensive and time-consuming
Solution Approach 1:
The patent segments the image processing task by introducing polygon objects that group multiple pixels into unified structural units. Instead of processing pixels in isolation, the system creates polygons from sequences of pixels that share common characteristics, thereby reducing the number of discrete processing units and improving efficiency while maintaining accuracy.
Solution Approach 2:
The patent introduces polygons as intermediary structures between raw pixel data and final symbol identification. These polygons serve as mediators that organize and relate pixels in meaningful ways, enabling the system to distinguish similar symbols more efficiently by operating at the polygon level rather than the pixel level.
2Productivity
If pixels are treated as isolated entities, then processing is simple, but accuracy in distinguishing similar symbols deteriorates
Solution Approach 1:
The patent merges multiple pixels into polygon objects that represent unified structural elements. By combining pixels that are spatially adjacent and share common characteristics into single polygon entities, the system achieves both processing efficiency and improved accuracy in distinguishing similar symbols, as the polygon-level representation captures structural relationships that isolated pixels cannot convey.
3Measurement precision
If brute force scanning is used to analyze image characteristics, then comprehensive analysis can be performed, but memory usage increases significantly
Solution Approach 1:
The patent extracts the essential structural information from pixel data by forming polygons that capture the topological relationships between pixels. This extraction process identifies and isolates the key characteristics needed for symbol distinction, eliminating the need to store and process all pixel data comprehensively, thereby reducing memory requirements while maintaining measurement precision.
Data Source
AI summary
A device includes at least one processor and memory including instructions that when executed by the at least one processor, cause the at least one processor to scan an input image including pixels that have either a first state or a second state opposite the first state, initiate a first cluster and a first polygon that belongs to the first cluster upon encountering a first pixel of the input image that has the first state, execute a first set of operations to form the first polygon, generate an indication of one or more characteristics of the input image based on at least the first cluster, and output the indication.