Frame Field Generation for Complex Image Feature Vectorization
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Solution Overview
Problem
Existing vectorization techniques struggle to accurately represent complex geometries of buildings and road networks in remote sensing imagery, particularly in high-density urban areas, due to challenges in handling intricate topologies and irregular shapes, leading to inaccuracies and the need for more precise edge delineation.
Innovation Solution
A method involving the integration of frame fields into the vectorization process, which encodes local geometric properties, uses a deep segmentation model, and employs multi-task learning to align predicted frame fields with ground truth contours, optimizing paths to minimize losses and simplify edges for precise vector representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional rule-based vectorization algorithms are used, then processing speed is improved, but accuracy in representing complex geometries deteriorates
Solution Approach 1:
The patent introduces frame fields as an intermediary mathematical construct that bridges the gap between simple rule-based algorithms and complex geometric representation. The frame field encodes local geometric properties (orientation, curvature) at each pixel, allowing traditional algorithms to operate while achieving high accuracy through the enriched geometric information provided by the frame field.
Solution Approach 2:
The patent transforms the approach by changing the parameter space from simple pixel intensity values to frame field components that encode geometric properties. This parameter transformation enables the system to represent complex geometries accurately while maintaining processing efficiency, as the frame field provides structured geometric information that guides the vectorization process.
2Device complexity
If rectangle approximation is used, then processing complexity is reduced, but geometric fidelity deteriorates
Solution Approach 1:
The patent changes the representation parameters from simple rectangular approximations to vector-based representations guided by frame fields. This allows the system to maintain low processing complexity while achieving high geometric fidelity, as the frame field provides the necessary geometric information to accurately represent irregular shapes without requiring complex approximation algorithms.
3Measurement precision
If deep segmentation models with frame field output are used, then vectorization accuracy is improved, but computational resources and training complexity increase
Solution Approach 1:
The patent segments the vectorization task into distinct components: a deep segmentation model for initial feature detection and a separate frame field generation component for geometric information extraction. This segmentation allows the system to achieve high accuracy while managing computational complexity by processing tasks in modular stages rather than requiring a single monolithic complex model.
4Ease of operation
If pixel intensity values alone are used for boundary delineation, then processing simplicity is maintained, but boundary accuracy in complex areas deteriorates
Solution Approach 1:
The patent introduces frame fields as an intermediary that enhances the relationship between simple pixel data and accurate boundary delineation. The frame field provides geometric context (orientation, curvature) at each pixel, allowing the system to maintain processing simplicity while achieving high boundary accuracy in complex areas through the enriched geometric information.
Data Source
AI summary
Methods, systems and computer program products are provided for generating shape representations corresponding to features in an image involving receiving image data, a 2-dimensional array of coordinates representing edges of one or more features in the image, and a list of path descriptions indicating the connectivity of points in the 2-D array to form a preliminary skeleton; interpolating orientation coefficients across the entire image using the extracted image line data and preliminary skeleton line data, thereby generating a frame field; and feeding the generated frame field to an optimization processor.