Image Recognition Grid Gateways for Low-Memory Pathfinding
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Solution Overview
Problem
Large grayscale maps with numerous pixels overwhelm memory and processor resources, leading to slow processing or degradation in system performance, and existing scaling techniques result in loss of boundary pixels.
Innovation Solution
The method involves recognizing an image as grids with pixels exceeding a threshold, dividing regions into rectangles, determining gateways between adjacent rectangles, generating a graphical model based on these gateways, and determining a target path using the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the map is scaled down to reduce data volume, then memory and processor resources are conserved, but boundary pixels are lost resulting in reduced accuracy
Solution Approach 1:
The patent segments the map into a hierarchical structure with multiple levels of detail. The map is divided into regions, with boundary regions retained at full resolution and interior regions scaled down. This segmentation allows the system to conserve memory and processor resources by reducing overall data volume while preserving boundary pixel accuracy in critical areas through selective retention of high-resolution data.
2Measurement precision
If the map is not scaled down to maintain accuracy, then boundary pixels are preserved, but memory and processor resources are excessively consumed and processing speed decreases
Solution Approach 1:
The patent applies local quality by assigning different resolution levels to different regions of the map. Boundary regions, which require high accuracy for pathfinding decisions, are maintained at full resolution. Interior regions are scaled down to lower resolution. This local differentiation ensures that processing speed is improved through reduced overall data volume while boundary pixel accuracy is preserved in critical areas.
3Measurement precision
If the map is not scaled down to maintain accuracy, then boundary pixels are preserved, but memory and processor resources are excessively consumed
Solution Approach 1:
The patent segments the map into boundary regions and interior regions, applying different data retention strategies to each. Boundary regions are kept at full resolution to preserve accuracy, while interior regions are scaled down significantly. This segmentation reduces the total quantity of data stored in memory and processed by the processor, conserving resources while maintaining necessary accuracy at boundaries.
Data Source
AI summary
The present application discloses an image recognition method and apparatus. The method comprises: recognizing an image to be recognized as a first type of grids and a second type of grids, wherein a pixel of the first/second type of grids is greater than a pixel threshold; dividing a region consisting of the first type of grids into a plurality of rectangles based on preset rules, and determining an adjacent edge of any two adjacent rectangles as a gateway, wherein the gateway is used to determine whether a target object can enter a second rectangle from a first rectangle via an gateway between the first rectangle and the second rectangle; generating a graphical model based on the gateway, wherein a vertex of the graphical model is the gateway; and determining a target path in the image to be recognized based on the graphical model, a starting point and an end.