Neural Network Thin Boundary Prediction via Normal Direction Suppression
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Solution Overview
Problem
Existing computer-implemented techniques for predicting boundaries between objects in images often produce thick boundaries, which require costly and time-consuming post-processing to thin, using machine learning models that rely on expensive resource-intensive computations.
Innovation Solution
A neural network is trained to directly predict thin boundaries by determining normal directions and suppressing non-maximum values, using a loss function that penalizes boundary inaccuracies, allowing for thinner boundary predictions without the need for post-processing, and can operate without labeled normal ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict boundaries, then boundary detection capability is improved, but boundary thickness increases and post-processing complexity increases
Solution Approach 1:
The neural network is trained in advance with a specialized loss function that incorporates normal direction predictions and non-maximum suppression logic. This preliminary training action embeds the thinning capability directly into the model, eliminating the need for separate post-processing steps to reduce boundary thickness.
Solution Approach 2:
The patent combines multiple functions into a single neural network: boundary prediction, normal direction estimation, and non-maximum suppression. By merging these previously separate processing stages into one integrated model, the system achieves thin boundary predictions without requiring additional post-processing complexity.
2Manufacturing precision
If post-processing is applied to thin boundaries, then boundary thickness is reduced, but computational resources and time increase
Solution Approach 1:
The patent replaces the mechanical post-processing system with a neural network-based approach. Instead of applying traditional image processing algorithms after boundary detection, the system uses a trained neural network to directly predict thin boundaries, substituting computational mechanics with learned patterns that achieve the same thinning effect more efficiently.
Solution Approach 2:
The loss function incorporates parameters for normal directions and non-maximum suppression, changing the optimization parameters from simple boundary localization to include directional information and thickness control. This parameter expansion allows the model to directly output thin boundaries during training, eliminating time-consuming post-processing.
3Manufacturing precision
If post-processing is applied to reduce boundary thickness, then boundary precision is improved, but resource requirements increase
Solution Approach 1:
The neural network performs preliminary thinning actions during the forward pass by incorporating non-maximum suppression logic trained into the model. This preliminary action ensures that boundaries are thin from the outset, eliminating the need for resource-intensive post-processing operations that would otherwise be required to achieve the same precision.
Data Source
AI summary
In some implementations a neural network is trained to perform to directly predict thin boundaries of objects in images based on image characteristics. A neural network can be trained to predict thin boundaries of objects without requiring subsequent computations to reduce the thickness of the boundary prediction. Instead, the network is trained to make the predicted boundaries thin by effectively suppressing non-maximum values in normal directions along what might otherwise be a thick predicted boundary. To do so, the neural network can be trained to determine normal directions and suppress non-maximum values based on those determined normal directions.