V. Sudha^1,*, T. R. Ganeshbabu²

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 827-842, 2021, DOI:10.32604/cmc.2020.012008

Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital… More >

Shangjun Luo¹, Junwei Luo¹, Wei Lu^1,*, Yanmei Fang¹, Jinhua Zeng², Shaopei Shi², Yue Zhang^3,4

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 647-657, 2021, DOI:10.32604/cmc.2020.012869

Abstract The estimation of image resampling factors is an important problem in image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest. However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information. In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image. Firstly, the resampling process will introduce correlations between neighboring pixels. In this case, a set of periodic pixels that are correlated to their neighbors… More >

Anju Asokan¹, J. Anitha¹, Bogdan Patrut², Dana Danciulescu³, D. Jude Hemanth^1,*

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 >

Yuanyuan Liu^1,2, Shuo Zhang¹, Haiye Yu³, Yueyong Wang^4,*, Yuehan Feng¹, Jiahui Sun¹, Xiaokang Zhou¹

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 247-262, 2021, DOI:10.32604/cmc.2020.012328

Abstract Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual structure, and the whole network… More >

S. Velliangiri^1,*, J. Premalatha²

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 625-645, 2020, DOI:10.32604/cmes.2020.010869

Abstract Different devices in the recent era generated a vast amount of digital video. Generally, it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice. Many kinds of researches on forensic detection have been presented, and it provides less accuracy. This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network (CNN). In the initial stage, the input video is taken as of the dataset and then converts the videos into image frames. Next, perform pre-sampling using the… More >

Hui Li¹, Wei Zeng^1,*, Guorong Xiao², Huabin Wang¹

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 841-846, 2020, DOI:10.32604/iasc.2020.010118

Abstract Recent progress on image colorization is substantial and benefiting mostly from the great development of the deep convolutional neural networks. However, one type of object can be colored by different kinds of colors. Due to the uncertain relationship between the object and color, the deep neural network is unstable and difficult to converge during the training process. In order to solve this problem, this paper proposes an instance-aware automatic image colorization algorithm, which uses the semantic features of the object instance as prior knowledge to guide the deep neural network to do the colorization task. Meanwhile, we design a discrete… More >

Fei Yang^1,2,#, Xiaoqing Zhang^1,*,#, Yong Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 365-382, 2020, DOI:10.32604/cmes.2020.010798

Abstract Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms (ECG) signals. Over the past years, deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems (CADs), but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods. To tackle this problem, this work proposes a convolutional neural network (CNN) model named PDNet to recognize different types of heart arrhythmias efficiently. In the PDNet, a convolutional block named PDblock is devised, which is comprised of a pointwise convolutional layer… More >

Jiabin Wang^*, Kai Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 345-363, 2020, DOI:10.32604/cmes.2020.011046

Abstract In many existing multi-view gait recognition methods based on images or video sequences, gait sequences are usually used to superimpose and synthesize images and construct energy-like template. However, information may be lost during the process of compositing image and capture EMG signals. Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection. To better solve the problems, a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed. Firstly, the sliding time window method is used to capture EMG signals. Then, the back-propagation learning algorithm is used… More >

Panyu Liu¹, Huilin Ren², Xiaojun Shi³, Yangyang Li^{4, *}, Zhiping Cai¹, Fang Liu⁵, Huacheng Zeng⁶

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2321-2334, 2020, DOI:10.32604/cmc.2020.010522

Abstract Deep learning technology has been widely used in computer vision, speech recognition, natural language processing, and other related fields. The deep learning algorithm has high precision and high reliability. However, the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power. In this paper, we propose MoTransFrame, a general model processing framework for deep learning models. Instead of designing a model compression algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately. By the integration method,… More >

Shuai Wang¹, Xianyi Chen^{2, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2065-2077, 2020, DOI:10.32604/cmc.2020.09857

Abstract With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed adaptive binary coding, our method… More >