Iterative Segmentation Refinement Model for Image Mask Accuracy
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Solution Overview
Problem
Conventional neural networks generate inaccurate segmentation masks for images, leading to significant type I and type II classification errors, especially for objects with complex, non-uniform, and asymmetrical shapes, limiting their utility in semantic segmentation.
Innovation Solution
A refined segmentation mask is generated through an iterative refinement process using a segmentation refinement model, which corrects errors and refines boundaries by concatenating and rescaling image data and segmentation masks, employing a convolutional encoder-decoder neural network trained with supervised machine learning methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks are used to generate segmentation masks, then the process is simple and fast, but the segmentation accuracy is low with significant type I and type II errors
Solution Approach 1:
The patent applies segmentation by dividing the segmentation process into two distinct stages: a coarse segmentation stage that provides an initial approximation, and a refinement stage that corrects errors and improves boundaries. This two-stage approach resolves the contradiction by achieving high accuracy through the refinement model while keeping the overall process manageable through clear separation of functions.
Solution Approach 2:
The coarse segmentation mask serves as an intermediary between the simple conventional neural network and the refinement model. It provides a starting point that the refinement model can improve upon, allowing the system to achieve high accuracy without requiring the initial model to be perfectly complex.
2Manufacturing precision
If conventional neural networks generate segmentation masks, then the processing is quick, but the boundaries are inaccurate especially for complex shapes
Solution Approach 1:
The coarse segmentation model performs preliminary action by generating an initial segmentation mask that captures the general structure and approximate boundaries. This preliminary segmentation allows the refinement model to focus computational resources on correcting specific errors and refining boundaries, achieving high precision without excessive processing time.
Solution Approach 2:
The refinement model applies local quality by focusing computational effort on specific regions where the coarse segmentation has errors, particularly at boundaries and for complex shapes. Rather than uniformly processing the entire image, the model concentrates refinement where it is most needed, improving boundary precision efficiently.
3Reliability
If a single-stage segmentation model is used, then the system is simple to implement, but it cannot effectively correct type I and type II errors
Solution Approach 1:
The system architecture is segmented into two specialized models: a coarse segmentation model for initial classification and a refinement model for error correction. This segmentation enables the system to achieve high reliability by addressing different aspects of segmentation accuracy in separate stages, while the modular architecture keeps implementation manageable.
Solution Approach 2:
The refinement model receives feedback from the coarse segmentation mask and uses it to identify and correct type I and type II errors. The feedback mechanism allows the system to iteratively improve segmentation accuracy, with the refinement model learning from the errors present in the coarse segmentation to produce more reliable results.
Data Source
AI summary
Enhanced methods and systems for the semantic segmentation of images are described. A refined segmentation mask for a specified object visually depicted in a source image is generated based on a coarse and/or raw segmentation mask. The refined segmentation mask is generated via a refinement process applied to the coarse segmentation mask. The refinement process correct at least a portion of both type I and type II errors, as well as refine boundaries of the specified object, associated with the coarse segmentation mask. Thus, the refined segmentation mask provides a more accurate segmentation of the object than the coarse segmentation mask. A segmentation refinement model is employed to generate the refined segmentation mask based on the coarse segmentation mask. That is, the segmentation model is employed to refine the coarse segmentation mask to generate more accurate segmentations of the object. The refinement process is an iterative refinement process carried out via a trained neural network.