Ying Li^1,2, Guanghong Gong¹, Dan Wang¹, Ni Li^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2237-2265, 2023, DOI:10.32604/cmes.2023.025193

Abstract There are two types of methods for image segmentation. One is traditional image processing methods, which are sensitive to details and boundaries, yet fail to recognize semantic information. The other is deep learning methods, which can locate and identify different objects, but boundary identifications are not accurate enough. Both of them cannot generate entire segmentation information. In order to obtain accurate edge detection and semantic information, an Adaptive Boundary and Semantic Composite Segmentation method (ABSCS) is proposed. This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances. It includes adaptively dividing and modifying the… More > Graphic Abstract

Qaisar Abbas¹, Abdul Rauf Baig^1,*, Ayyaz Hussain²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1483-1499, 2023, DOI:10.32604/cmc.2023.036366

Abstract The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is to design and train the… More >

Young-Man Kwon, Sunghoon Bae, Dong-Keun Chung, Myung-Jae Lim^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 683-696, 2023, DOI:10.32604/cmc.2023.033370

Abstract Recently, semantic segmentation has been widely applied to image processing, scene understanding, and many others. Especially, in deep learning-based semantic segmentation, the U-Net with convolutional encoder-decoder architecture is a representative model which is proposed for image segmentation in the biomedical field. It used max pooling operation for reducing the size of image and making noise robust. However, instead of reducing the complexity of the model, max pooling has the disadvantage of omitting some information about the image in reducing it. So, this paper used two diagonal elements of down-sampling operation instead of it. We think that the down-sampling feature maps… More >

Chuanlong Sun, Hong Zhao^*, Liang Mu, Fuliang Xu, Laiwei Lu

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 787-801, 2023, DOI:10.32604/cmes.2023.025119

Abstract Image semantic segmentation has become an essential part of autonomous driving. To further improve the generalization ability and the robustness of semantic segmentation algorithms, a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism (SE) and Depthwise Separable Convolution (DSC) is designed. Meanwhile, Adam-GC, an Adam optimization algorithm based on Gradient Compression (GC), is proposed to improve the training speed, segmentation accuracy, generalization ability and stability of the algorithm network. To verify and compare the effectiveness of the algorithm network proposed in this paper, the trained network model is used for experimental verification and comparative test on the Cityscapes semantic segmentation… More >

Tao Duan¹, Yue Liu¹, Jingze Li¹, Zhichao Lian^2,*, Qianmu Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 223-239, 2023, DOI:10.32604/cmes.2023.024742

Abstract The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology. Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis. Semantic segmentation is also a challenging technology for image understanding and scene parsing. We focused on the challenging task of real-time semantic segmentation in this paper. In this paper, we proposed a novel fast architecture for real-time semantic segmentation named DuFNet. Starting from the existing work of Bilateral Segmentation Network (BiSeNet), DuFNet proposes a novel Semantic Information Flow (SIF) structure for context information… More > Graphic Abstract

S. Gnanapriya^1,*, K. Rahimunnisa²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1105-1119, 2023, DOI:10.32604/iasc.2023.032832

Abstract The performance of Hand Gesture Recognition (HGR) depends on the hand shape. Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks. The crucial segmentation task is extremely complicated because of the background complexity, variation in illumination etc. The proposed modified UNET and ensemble model of Convolutional Neural Networks (CNN) undergoes a two stage process and results in proper hand gesture recognition. The first stage is segmenting the regions of the hand and the second stage is gesture identification. The modified UNET segmentation model is trained using… More >

Jieren Cheng^1,3, Hua Li^2,*, Dengbo Li³, Shuai Hua², Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1941-1957, 2023, DOI:10.32604/cmc.2023.032757

Abstract Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation… More >

Yingpeng Dai¹, Junzheng Wang¹, Jing Li^1,*, Lingfeng Meng², Songfeng Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 135-156, 2023, DOI:10.32604/cmes.2022.021525

Abstract It is important for the autonomous system to understand environmental information. For the autonomous system, it is desirable to have a strong generalization ability to deal with different complex environmental information, as well as have high accuracy and quick inference speed. Network ensemble architecture is a good choice to improve network performance. However, it is unsuitable for real-time applications on the autonomous system. To tackle this problem, a new neural network ensemble named partial-shared ensemble network (PSENet) is presented. PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks together to accelerate the inference… More >

A. Deepak Kumar^1,2,*, T. Sasipraba¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2273-2288, 2023, DOI:10.32604/iasc.2023.028996

Abstract Diabetic Retinopathy is a disease, which happens due to abnormal growth of blood vessels that causes spots on the vision and vision loss. Various techniques are applied to identify the disease in the early stage with different methods and parameters. Machine Learning (ML) techniques are used for analyzing the images and finding out the location of the disease. The restriction of the ML is a dataset size, which is used for model evaluation. This problem has been overcome by using an augmentation method by generating larger datasets with multidimensional features. Existing models are using only one augmentation technique, which produces… More >

Siddiqui Muhammad Yasir¹, Hyunsik Ahn^2,*

Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235

Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific problems of processing point clouds… More >