Foreground Object Extraction Using Deep Neural Network and Graph Cut
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Solution Overview
Problem
Current image processing technologies face challenges in precisely extracting foreground objects from images with complex natural backgrounds, particularly in separating objects from intricate environments.
Innovation Solution
An image processing method utilizing a deep neural network to extract a foreground region, followed by pixel identification and mask formation to accurately separate the foreground object from the background, ensuring precise extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to extract foreground objects, then the processing speed is faster, but the extraction precision deteriorates when dealing with complex natural backgrounds
Solution Approach 1:
The patent divides the image processing task into two distinct stages: first extracting a foreground region using a deep neural network, then performing refined separation using a graph cut algorithm on the extracted region. This segmentation of the processing task allows each algorithm to focus on its strength, improving overall precision without requiring the entire system to be overly complex
Solution Approach 2:
The patent introduces an intermediate foreground region extraction step that serves as a mediator between the initial image and the final foreground object. This intermediate step simplifies the subsequent graph cut operation by reducing the search space, thereby improving precision while managing computational complexity
2Measurement precision
If a deep neural network is used for foreground region extraction, then the extraction precision is improved, but the processing time increases
Solution Approach 1:
The patent segments the processing workflow so that the computationally intensive deep neural network is applied only to extract a foreground region, not to process the entire image. This reduces the overall processing time while maintaining precision in the critical extraction phase
Solution Approach 2:
The patent applies the deep neural network partially - only to extract the foreground region rather than performing complete foreground object extraction. This partial application of the complex algorithm reduces processing time while still achieving high precision where it matters most
Data Source
AI summary
An image processing method and a related apparatus are provided. The method is applied to an image processing device, and includes: obtaining an original image, the original image including a foreground object; extracting a foreground region from the original image through a deep neural network; identifying pixels of the foreground object from the foreground region; forming a mask according to the pixels of the foreground object, the mask including mask values corresponding to the pixels of the foreground object; and extracting the foreground object from the original image according to the mask.