Image Semantic Segmentation Method Based on Deep Fusion Network and Conditional Random Field

Wang, Shuo; Yang, Yi

doi:10.1155/2022/8961456

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Semantic Segmentation of Underwater (SUIM Net) proposed by M. J. Islam et al [33], improves the performance of semantic segmentation using a fully convolutional encoder-decoder model. Several studies [34][35][36] combined conditional random fields with deep CNNs, with significant improvements in segmentation accuracy and generalization performance. These scene resolution methods can effectively enhance image features, but ignore the similarity of fuzzy features in the classification process is prone to misclassification.…”

Section: Related Workmentioning

confidence: 99%

Global enhancement network underwater archaeology scene parsing method

Pan,

Jia,

Cai

2023

Robotica

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Underwater archaeology is of great significance for historical and cultural transmission and preservation of underwater heritage, but it is also a challenging task. Underwater heritage is located in an environment with high sediment content, objects are mostly buried, and the water is turbid, resulting in some of the features of objects missing or blurred, making it difficult to accurately identify and understand the semantics of various objects in the scene. To tackle these issues, this paper proposes a global enhancement network (GENet) underwater scene parsing method. We introduce adaptive dilated convolution by adding an extra regression layer, which can automatically deduce adaptive dilated coefficients according to the different scene objects. In addition, considering the easy confusion in the process of fuzzy feature classification, an enhancement classification network is proposed to increase the difference between various types of probabilities by reducing the loss function. We verified the validity of the proposed model by conducting numerous experiments on the Underwater Shipwreck Scenes (USS) dataset. We achieve state-of-the-art performance compared to the current state-of-the-art algorithm under three different conditions: conventional, relic semi-buried, and turbidified water quality. The experimental results show that the proposed algorithm performs best in different situations. To verify the generalizability of the proposed algorithm, we conducted comparative experiments on the current publicly available Cityscapes, ADE20K, and the underwater dataset SUIM. The experimental results show that this paper achieves good performance on the public dataset, indicating that the proposed algorithm is generalizable.

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Section: Related Workmentioning

confidence: 99%

Global enhancement network underwater archaeology scene parsing method

Pan,

Jia,

Cai

2023

Robotica

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“…In [68] have presented the linear spectral clustering-based image segmentation using the superpixel based approach. Some other segmentation methods are recently given in [69][70][71]. e wavelet-based fusion strategy has been presented [72] for increasing the quality of medical images.…”

Section: Fuzzy Super Pixelmentioning

confidence: 99%

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Gupta

Bhadauria

2022

Computational Intelligence and Neuroscience

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During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.

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“…e evaluation of semantic segmentation [28][29][30][31][32][33][34][35][36][37][38] standard metric for MIoU, through the intersection of the two numerical sets and set ratio to represent the accuracy, in the two sets are true value and predicted value, the proportion of the two can be expressed as true, false negative, false-positive sum, then calculated in each category of IoU mean.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Analysis of Convolutional Neural Network Segmentation Algorithm of Adenomyoma

Jin

Huang

2022

Computational Intelligence and Neuroscience

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Adenomoma is a common disease occurring in the female uterus. The symptoms and pain of adenomoma seriously troubled the physical and mental health of contemporary women. However, because of the significant advantages of nondestructive and low price, ultrasound examination is used as the main imaging method for clinical diagnosis of gynecological diseases at the present stage and is often used in the initial screening and postoperative diagnosis and treatment of uterine diseases. Imaging provides a very rich information in the medical diagnosis of tumor and is a very important basis for the disease diagnosis and treatment at this stage. Ultrasound images are different from medical images such as X-ray and MRI. Because of the characteristics of imaging principles and noise interference, ultrasound images need to rely on rich clinical experience of doctors in the process of disease diseases, which increases the difficulty and work burden of doctors to some extent. Therefore, the project aims to study the deep learning segmentation method suitable for ultrasonic images. Combined with the Deeplab network in the convolutional neural network, comparing the results of the FCN network, and then finding that the Deeplab network has obvious advantages as an image segmentation model of uterine adenomyoma. In clinical practice, it can reduce the work burden of doctors and try in the direction of uterine adenomyomas ultrasound image segmentation, to fill the gap in this field.

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Image Semantic Segmentation Method Based on Deep Fusion Network and Conditional Random Field

Cited by 3 publications

References 25 publications

Global enhancement network underwater archaeology scene parsing method

Global enhancement network underwater archaeology scene parsing method

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Analysis of Convolutional Neural Network Segmentation Algorithm of Adenomyoma

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