Polygon Similarity Mapping for Polyline Homogeneity Evaluation
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Solution Overview
Problem
Current object detection systems face challenges in comparing and evaluating the homogeneity of extracted polylines or polygons, particularly in determining their similarity and quality across multiple sets, which is crucial for applications like autonomous driving and video surveillance.
Innovation Solution
A method that renders polylines onto a grid pattern, labels cells to indicate polyline occupancy, and calculates percent composition to represent homogeneity between sets, allowing for a sensitive evaluation of polyline/polygon similarity and purity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used to compare polylines, then the basic object identification function is achieved, but the evaluation of polyline homogeneity and similarity between multiple sets is insufficient
Solution Approach 1:
The patent segments the image space into a grid of cells, and further segments each polyline into discrete cell occupancies. This segmentation allows for precise comparison of polyline homogeneity by evaluating which cells are occupied by which polylines, enabling fine-grained similarity measurement without requiring complex continuous geometric analysis.
Solution Approach 2:
The patent introduces an intermediary data structure that maps polylines to grid cells and tracks cell occupancy by multiple polylines. This intermediary representation serves as a bridge between the raw polyline coordinates and the homogeneity evaluation, enabling efficient comparison by counting how many polylines occupy each cell and calculating percent composition metrics.
2Measurement precision
If detailed polyline comparison is performed to achieve accurate homogeneity measurement, then the evaluation precision is improved, but the computational complexity and processing time increase
Solution Approach 1:
By segmenting the comparison task into discrete grid cell evaluations, the patent enables parallel processing of multiple polylines. Each cell can be independently evaluated for occupancy by different polylines, and these evaluations can be performed simultaneously, maintaining high precision while improving processing throughput through parallelization.
Solution Approach 2:
The patent performs preliminary action by pre-rendering all polylines onto the grid structure and pre-computing the cell occupancy mappings before the actual homogeneity evaluation. This preliminary setup organizes the data in a way that enables rapid comparison, avoiding repeated geometric calculations during the evaluation phase and thus improving processing speed.
3Measurement precision
If multiple sets of polylines are rendered and compared using fine-grained cell labeling, then the homogeneity evaluation accuracy is improved, but the data processing and storage requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for homogeneity evaluation by recording merely which cells are occupied by which polylines, rather than storing or processing the complete polyline coordinate data. This extraction of essential occupancy information reduces data processing volume while maintaining the precision needed for accurate homogeneity determination.
Solution Approach 2:
Instead of comparing polylines directly by their coordinate sequences, the patent inverts the approach by rendering polylines onto a grid and comparing the resulting cell occupancy patterns. This inversion transforms the problem from continuous geometric comparison to discrete set comparison, reducing computational complexity and data processing requirements while preserving evaluation accuracy.
Data Source
AI summary
An approach is provided for an asymmetric evaluation of polygon similarity. The approach, for instance, involves receiving a first polygon representing an object depicted in an image. The approach also involves generating a transformation of the image comprising image elements whose values are based on a respective distance that each image element is from a nearest image element located on a first boundary of the first polygon. The approach further involves determining a subset of the plurality of image elements of the transformation that intersect with a second boundary of a second polygon. The approach further involves calculating a polygon similarity of the second polygon with respect the first polygon based on the values of the subset of image elements normalized to a length of the second boundary of the second polygon.