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Image Shadow Detection and Removal Based on Region Matching of Intelligent Computing.

Junying Feng^1,2, Yong Kwan Kim², Peng Liu¹

¹School of Intelligent Manufacturing, Weifang University of Science and Technology, Shandong, Weifang 261000, China.

Computational Intelligence and Neuroscience

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces a novel intelligent computing method for shadow detection and removal in computer vision. The approach enhances subsequent tasks by accurately identifying and eliminating shadows without prior training.

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Area of Science:

Computer Vision
Pattern Recognition
Image Processing

Background:

Shadows in images degrade information for moving objects, impacting tasks like object detection and segmentation.
Existing methods may require training or affect other image features during shadow removal.

Purpose of the Study:

To propose an intelligent computing method for detecting and removing image shadows.
To improve the performance of computer vision tasks affected by shadows.
To offer a training-free shadow detection solution.

Main Methods:

Treating each image as a small sample for analysis.
Utilizing material matching and intelligent computing between image regions.
Developing a method for both shadow detection and removal.

Main Results:

The proposed method achieves direct shadow detection without requiring training.
It ensures consistency across similar image regions during detection.
Shadow removal minimizes the impact on other features within the shadow area.

Conclusions:

The novel approach demonstrates promising performance in shadow detection and removal.
It offers a significant improvement over existing advanced shadow detection methods.
The method enhances the reliability of computer vision applications by addressing shadow interference.