End-to-End Image Segmentation Model with Edge Refinement
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Solution Overview
Problem
Current image segmentation techniques using neural networks, such as CNNs, are limited by low input and output resolutions, preventing end-to-end training due to the separation of CNN processes and up-scaling methods, which restricts the quality and accuracy of segmented image outputs.
Innovation Solution
A computing system employing a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, allowing for end-to-end training by processing high-resolution images through downscaling, semantic segmentation, and refinement to produce refined semantic segmentation masks with higher resolution and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a CNN is used for image segmentation, then the segmentation can be performed automatically, but the input and output resolutions are limited to low resolutions such as 256×256, 300×300, or 400×400 pixels
Solution Approach 1:
The system segments the image processing task into multiple stages: a CNN performs initial semantic segmentation at low resolution, then a CRF layer refines the segmentation at high resolution. This multi-stage segmentation approach allows automatic segmentation while achieving high output resolution by dividing the complex task into manageable parts that can be processed at different resolutions
Solution Approach 2:
A conditional random field (CRF) layer is introduced as an intermediary between the CNN and the final output. The CRF acts as a mediator that takes the low-resolution CNN output and refines it to high resolution while maintaining semantic accuracy. This intermediary component enables the system to overcome the resolution limitation of standard CNNs while preserving automatic segmentation capability
2Manufacturing precision
If an up-scaling method such as bilateral filter or conditional random field is used to increase output resolution, then the segmented image resolution is improved, but the CNN cannot be trained from end to end because the processes are separated
Solution Approach 1:
The system merges the CNN and CRF into a single unified end-to-end trainable model. The CRF layer is implemented with differentiable operations that allow gradients to flow back through the entire network during training. This merging eliminates the separation between CNN and up-scaling methods, enabling end-to-end training while maintaining high resolution output capability
Solution Approach 2:
The traditional non-differentiable CRF optimization process is replaced with a differentiable CRF layer that can be integrated into the neural network training framework. This substitution allows the CRF to be trained using gradient descent alongside the CNN, enabling end-to-end training while preserving the resolution enhancement benefits of CRF-based up-scaling
Data Source
AI summary
The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.