Highly Contrast Image Correction for Dim Boundary Separation of Image Semantic Segmentation

Highly Contrast Image Correction for Dim Boundary Separation of Image Semantic Segmentation

The efficiency and accuracy of the image semantic segmentation algorithm represent a trade-off relationship, and the loss of accuracy tends to increase as the model structure simplifies to improve efficiency. Developing more efficient and accurate algorithms requires methods to complement them. In t...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 64142 - 64152
Main Authors: Choi, Jinyeob, Choi, Byeongdae
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Artificial neural networks
Brightness
Computational modeling
Convolution
Convolutional neural networks
exponential function
Feature extraction
gamma correction
Gamma function
Image compression
Image contrast
Image edge detection
Image segmentation
image semantic segmentation
logarithmic function
Model accuracy
Object recognition
Semantic segmentation
Semantics
Training
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Summary:The efficiency and accuracy of the image semantic segmentation algorithm represent a trade-off relationship, and the loss of accuracy tends to increase as the model structure simplifies to improve efficiency. Developing more efficient and accurate algorithms requires methods to complement them. In this study, we applied the logarithmic-exponential mixture (LEM) function for gamma correction of images to improve the accuracy of image semantic segmentation. The basic model used in this work was produced by constructing a full convolution neural network based on MobileNetV2. To avoid the noise of input compression, we corrected training and validation images with gamma from 1/8 to 8 (7 different levels) before doing convolution. We evaluated models using Tensorflow deep-learning library based on Python. We compared models using LEM function to models using conventional gamma function. The prediction masks of the proposed model using the LEM function had relatively small fluctuations of accuracy upon gamma change. For images that have shadows overlapped on the object, the object was better distinguished in small gamma values. For dark images, the increase in accuracy was more effective. The results indicated that the proposed gamma correction could improve image segmentation accuracy in images with unclear edges. We believe that the presented results will guide further studies for accuracy improvement of image recognition algorithms applicable to future devices, such as autonomous vehicles and mobile robots.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3075084