Hough-to-Radon Neural Network Layer for Efficient Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Convolutional neural networks face inefficiencies in processing spatially distant concepts, leading to increased computational complexity and resource usage, particularly in image-segmentation tasks, due to the reliance on local features and inefficient use of pre-trained filters.
Innovation Solution
Incorporating a Hough-to-Radon Transform layer in the neural network architecture to convert images into a parameter space using (s,t) and (ρ,φ) coordinates, reducing the size of feature maps and minimizing computational costs while maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks process all image pixels using local features, then they can extract spatial information, but they fail to establish dependencies between distant pixels and increase computational complexity
Solution Approach 1:
The patent transforms the image from spatial coordinates (x,y) to parameter space coordinates (ρ,φ), where lines in the original image are represented as points in the transformed space. This dimensional transformation enables the network to capture global dependencies between distant pixels by representing them in a different coordinate system where their relationships become more apparent and computationally efficient.
2Productivity
If convolutional networks use pre-trained feature filters, then they can process local features, but high-level features may not be present in the image making the filters inefficient
Solution Approach 1:
The patent changes the parameter space representation from (s,t) coordinates to (ρ,φ) coordinates, fundamentally altering how features are represented and processed. This parameter transformation allows the network to adapt to different image contents more effectively by reparameterizing the feature space, making pre-trained filters more versatile and efficient across diverse imaging tasks.
3Loss of information
If convolutional layers process local features, then they can extract spatial information, but they struggle to establish dependencies between distant pixels requiring increased model depth and complexity
Solution Approach 1:
By transforming the image to parameter space using Hough transform and then to Radon transform, the patent creates a new dimensional representation where distant pixels in the original image are repositioned to reveal their relationships. This allows the network to capture long-range dependencies without increasing model depth, as the transformed space naturally organizes pixel relationships.
Data Source
AI summary
While convolutional neural networks have been prized for image-processing tasks, they have limitations. Embodiments introduce a new Hough-to-Radon Transform (HRT) layer into an artificial neural network, such as a convolutional neural network, to address one or more of these limitations, without compromising on the accuracy of the artificial neural network for image-processing tasks. The HRT layer converts an input image from a first parameter space into a second parameter space of reduced complexity. Inner layers may operate on the image in this second parameter space, instead of in the first parameter space, to reduce the overall computational cost of the artificial neural network.