Gradient Alpha Mask Augmentation for Video Segmentation
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Solution Overview
Problem
Existing image segmentation methods for video match moving, particularly those using trained neural networks, require extensive training and annotation, making them costly and time-consuming, especially when dealing with dynamic backgrounds.
Innovation Solution
The method involves lightly training a neural network to recognize foreground objects in a video sequence, then augmenting the initial low-quality segmentation masks by assigning gradient alpha values to neighboring pixels based on their RGB distance from consistently segmented groups of pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is extensively trained with hundreds of thousands of annotated images to achieve high quality segmentation masks, then the segmentation accuracy is improved, but the training time and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by using a lightly trained neural network to generate initial segmentation masks before applying the enhancement process. This preliminary segmentation provides a starting point that requires less extensive training, thereby reducing training time while maintaining acceptable accuracy that can be further improved through the enhancement method
Solution Approach 2:
The patent introduces an intermediary enhancement process that acts between the lightly trained neural network and the final segmentation output. This intermediary step uses gradient alpha values and neighborhood analysis to refine the initial masks, achieving high quality results without requiring extensive neural network training
2Measurement precision
If a neural network is extensively trained to achieve high quality segmentation masks, then the quality of segmentation masks is improved, but the computational resources and cost increase tremendously
Solution Approach 1:
The patent applies partial action by using a lightly trained neural network (less than full training) to generate initial segmentation masks, then applying a targeted enhancement process that focuses computational resources on refining specific regions. This approach achieves high quality masks without the tremendous computational cost of extensive neural network training
Solution Approach 2:
The enhancement process serves as an intermediary that bridges the gap between lightly trained neural networks and high quality segmentation masks. It uses gradient alpha values and neighborhood pixel analysis to refine masks with minimal computational overhead compared to extensive neural network training
3Productivity
If traditional computer vision techniques such as chroma-keying or motion detection are used for segmentation, then the process is computationally efficient, but the accuracy deteriorates when dealing with dynamic backgrounds
Solution Approach 1:
The patent merges the advantages of both traditional and AI-based approaches by combining the computational efficiency of traditional methods with the accuracy enhancement of AI. The process uses a lightly trained neural network (lower computational cost than fully trained) and enhances its output using traditional image processing techniques such as gradient analysis and neighborhood pixel examination, achieving both efficiency and accuracy
Solution Approach 2:
The enhancement process applies local quality by focusing refinement efforts on specific regions and pixels rather than uniformly processing the entire image. It analyzes neighborhood pixels and applies gradient alpha values selectively, improving segmentation accuracy in critical areas while maintaining processing efficiency
Data Source
AI summary
Systems and methods for producing high-quality image segmentation masks by efficiently augmenting low-quality segmentation masks initially obtained by lightly trained neural networks are disclosed. Sequences of low-quality segmentation masks are transformed into high-quality segmentation masks by first identifying groups of pixels that are consistently segmented in at least three successive video frames. The pixels surrounding the consistently segmented pixels are then examined and assigned gradient alpha values, i.e., degrees of transparency. In one embodiment the alpha values are made proportional to the RGB distance of the pixel from the mean RGB value of the consistently segmented group of pixels.