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  • Open Access


    AF-Net: A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

    Guimin Hou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1877-1891, 2021, DOI:10.32604/cmc.2021.017481

    Abstract Medical image segmentation is an important application field of computer vision in medical image processing. Due to the close location and high similarity of different organs in medical images, the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation. To address these challenges, we propose a medical image segmentation network (AF-Net) based on attention mechanism and feature fusion, which can effectively capture global information while focusing the network on the object area. In this approach, we add dual attention blocks (DA-block) to the backbone network, which comprises parallel channels and spatial attention branches, to adaptively calibrate and weigh… More >

  • Open Access


    Feature-Enhanced RefineDet: Fast Detection of Small Objects

    Lei Zhao*, Ming Zhao

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jihpp.2021.010065

    Abstract Object detection has been studied for many years. The convolutional neural network has made great progress in the accuracy and speed of object detection. However, due to the low resolution of small objects and the representation of fuzzy features, one of the challenges now is how to effectively detect small objects in images. Existing target detectors for small objects: one is to use high-resolution images as input, the other is to increase the depth of the CNN network, but these two methods will undoubtedly increase the cost of calculation and time-consuming. In this paper, based on the RefineDet network framework,… More >

  • Open Access


    Encoder-Decoder Based Multi-Feature Fusion Model for Image Caption Generation

    Mingyang Duan, Jin Liu*, Shiqi Lv

    Journal on Big Data, Vol.3, No.2, pp. 77-83, 2021, DOI:10.32604/jbd.2021.016674

    Abstract Image caption generation is an essential task in computer vision and image understanding. Contemporary image caption generation models usually use the encoder-decoder model as the underlying network structure. However, in the traditional Encoder-Decoder architectures, only the global features of the images are extracted, while the local information of the images is not well utilized. This paper proposed an Encoder-Decoder model based on fused features and a novel mechanism for correcting the generated caption text. We use VGG16 and Faster R-CNN to extract global and local features in the encoder first. Then, we train the bidirectional LSTM network with the fused… More >

  • Open Access


    Classification of COVID-19 CT Scans via Extreme Learning Machine

    Muhammad Attique Khan1, Abdul Majid1, Tallha Akram2, Nazar Hussain1, Yunyoung Nam3,*, Seifedine Kadry4, Shui-Hua Wang5, Majed Alhaisoni6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1003-1019, 2021, DOI:10.32604/cmc.2021.015541

    Abstract Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors; this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an Extreme Learning Machine (ELM) served… More >

  • Open Access


    Multiple Faces Tracking Using Feature Fusion and Neural Network in Video

    Boxia Hu1,2,*, Huihuang Zhao1, Yufei Yang1,3, Bo Zhou4, Alex Noel Joseph Raj5

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1549-1560, 2020, DOI:10.32604/iasc.2020.011721

    Abstract Face tracking is one of the most challenging research topics in computer vision. This paper proposes a framework to track multiple faces in video sequences automatically and presents an improved method based on feature fusion and neural network for multiple faces tracking in a video. The proposed method mainly includes three steps. At first, it is face detection, where an existing method is used to detect the faces in the first frame. Second, faces tracking with feature fusion. Given a video that has multiple faces, at first, all faces in the first frame are detected correctly by using an existing… More >

  • Open Access


    Deep Learning-Based Classification of Fruit Diseases: An Application for Precision Agriculture

    Inzamam Mashood Nasir1, Asima Bibi2, Jamal Hussain Shah2, Muhammad Attique Khan1, Muhammad Sharif2, Khalid Iqbal3, Yunyoung Nam4, Seifedine Kadry5,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945

    Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >

  • Open Access


    Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification

    Anju Asokan1, J. Anitha1, Bogdan Patrut2, Dana Danciulescu3, D. Jude Hemanth1,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364

    Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then… More >

  • Open Access


    An Improved Deep Fusion CNN for Image Recognition

    Rongyu Chen1, Lili Pan1, *, Cong Li1, Yan Zhou1, Aibin Chen1, Eric Beckman2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1691-1706, 2020, DOI:10.32604/cmc.2020.011706

    Abstract With the development of Deep Convolutional Neural Networks (DCNNs), the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs. Previous studies have shown that the deeper the network is, the more abstract the features are. However, the recognition ability of deep features would be limited by insufficient training samples. To address this problem, this paper derives an improved Deep Fusion Convolutional Neural Network (DF-Net) which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited datasets. Specifically, DF-Net organizes two… More >

  • Open Access


    Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

    Xiaorui Shao1, Chang Soo Kim1, *, Dae Geun Kim2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 543-561, 2020, DOI:10.32604/cmc.2020.011108

    Abstract Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel… More >

  • Open Access


    Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion

    Yuyang Sun1, Peizhou Yan2, *, Zhengzheng Li2, Jiancheng Zou3, Don Hong4

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1563-1574, 2020, DOI:10.32604/cmc.2020.09763

    Abstract Real-time detection of driver fatigue status is of great significance for road traffic safety. In this paper, a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock. The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard. The landmarks of the driver’s face were labeled and the eye-area wassegmented. By calculating the aspect ratios of the eyes, the duration of eye closure, frequency of blinks and PERCLOS of both colored and infrared, fatigue can be detected. Based on the change of… More >

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