Microscopic Object Segmentation Under Uneven Illumination
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Solution Overview
Problem
Existing micro-operation target segmentation methods suffer from low precision due to shadows caused by uneven illumination and image noise, compromising the accuracy of target recognition and positioning in micro-manipulation tasks.
Innovation Solution
A micro-operation-based target segmentation method using improved Otsu and edge operators, involving enhanced bilateral filtering and Otsu threshold segmentation to denoise and accurately separate foreground from background, while preserving edge information and correcting shadows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image processing techniques are used to enhance contrast and remove shadows, then the applicability is improved, but the computational time increases
Solution Approach 1:
The patent applies bilateral filtering as a preliminary step before segmentation to pre-process the image and remove shadows caused by uneven illumination. This preliminary action simplifies subsequent segmentation operations by eliminating the need for complex shadow correction during the main processing stage, thereby reducing overall computational time while maintaining broad applicability across different imaging conditions
2Measurement precision
If deep learning methods are used for shadow recognition and correction, then recognition accuracy is improved, but data support requirements increase
Solution Approach 1:
The patent employs traditional image processing algorithms (bilateral filtering, Otsu thresholding, Canny edge detection) that require minimal computational resources and no large training datasets. These algorithms can be executed with simple, lightweight computational models rather than complex deep learning systems, achieving satisfactory shadow correction and segmentation accuracy without requiring extensive data support or powerful hardware resources
3Extent of automation
If threshold-based segmentation is used in micro-manipulation systems, then automation is achieved, but segmentation accuracy deteriorates due to shadows and noise
Solution Approach 1:
The patent divides the segmentation process into multiple distinct stages: first applying bilateral filtering to remove shadows and noise, then using Otsu thresholding for initial segmentation, followed by Canny edge detection to refine boundaries. This multi-stage segmentation approach maintains automation while significantly improving segmentation accuracy by addressing shadow and noise issues at each stage
Solution Approach 2:
The patent introduces bilateral filtering as an intermediary processing step between image acquisition and threshold-based segmentation. This intermediary operation removes shadows and noise that would otherwise interfere with the thresholding process, enabling accurate automated segmentation even in the presence of uneven illumination
Data Source
AI summary
This invention presents an enhanced micro-operation target segmentation method utilizing improved Otsu and edge operators, addressing the limitations of conventional techniques in microscopic visual environments. The proposed approach effectively mitigates accuracy constraints caused by image noise and shadow effects from uneven illumination. The methodology comprises three key phases: Applying an optimized bilateral filtering algorithm to perform noise reduction on grayscale images, producing denoised images; Segmenting micro-operation targets using the refined Otsu algorithm on these denoised images to obtain initial segmentation results; Directly applying the final segmentation result when meeting predefined criteria, or conducting iterative segmentation through the enhanced edge operator and Otsu algorithm if necessary. This innovative method demonstrates exceptional performance in micro-operation target detection applications