Digital Image Analysis via Edge Intersection Detection
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Solution Overview
Problem
Conventional digital image recognition methods require significant computing resources and are limited by processing power, leading to slow processing speeds and reduced accuracy due to the need to analyze all pixel values of a digital image.
Innovation Solution
A digital image analyzing device and method that reduces computing resource consumption by allowing users to input auxiliary information, such as image edges and shapes, to enable the processor to identify intersections of lines with image edges, thereby focusing analysis and improving recognition speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital image recognition methods analyze all pixel values, then comprehensive image analysis is achieved, but computing resource consumption increases significantly
Solution Approach 1:
The patent segments the image analysis process by dividing the image into multiple regions of interest (ROIs) based on predetermined conditions. Instead of analyzing all pixels uniformly, the system selectively processes only those regions that meet specific criteria (such as containing edges or features of interest), thereby reducing overall computing resource consumption while maintaining recognition accuracy for critical areas.
Solution Approach 2:
The patent applies local quality by assigning different processing strategies to different regions of the image. High-priority regions (those meeting predetermined conditions) receive detailed analysis, while other regions receive simplified or no processing. This allows the system to concentrate computing resources where they are most needed, reducing overall energy consumption while preserving measurement precision in critical areas.
2Measurement precision
If conventional methods process all pixel values, then complete image coverage is achieved, but processing speed decreases
Solution Approach 1:
The patent divides the image processing task into segments by identifying and prioritizing specific regions. By segmenting the processing workload and focusing computational effort only on regions meeting predetermined conditions, the system achieves faster processing speeds without sacrificing the accuracy of feature detection in those critical regions.
Solution Approach 2:
The patent applies partial action by processing only a subset of image regions rather than the entire image. By performing analysis selectively on regions that meet predetermined conditions (partial processing), the system achieves sufficient measurement precision for the application while dramatically improving processing speed through reduced computational workload.
3Loss of information
If image texture and shape features are analyzed using conventional methods, then detailed image characteristics are obtained, but recognition accuracy deteriorates due to computational limitations
Solution Approach 1:
The patent applies local quality by enhancing the analysis of image features (texture, shape, edges) specifically in regions that meet predetermined conditions. By concentrating processing power on these selected regions, the system can perform more detailed and accurate feature analysis locally, thereby improving overall recognition accuracy without being constrained by global computational limitations.
Solution Approach 2:
The patent introduces an intermediary step that identifies and selects regions of interest based on predetermined conditions before performing detailed feature analysis. This intermediary region-selection mechanism acts as a filter that guides subsequent processing, ensuring that computational resources are directed toward analyzing image features in the most relevant areas, thus improving recognition accuracy while managing information loss.
Data Source
AI summary
A computer program product capable of enabling a computer to perform a digital image analyzing operation, wherein the digital image analyzing operation comprises: receiving settings of a plurality of lines corresponding to one or more image edges of a digital image; and identifying a plurality of intersections of the plurality of lines and the one or more image edges of the digital image.