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  • Open Access

    ARTICLE

    CNN-Based Fast HEVC Quantization Parameter Mode Decision

    Liming Chen1, Bosi Wang1,*, Weijie Yu1, Xu Fan1

    Journal of New Media, Vol.1, No.3, pp. 115-126, 2019, DOI:10.32604/jnm.2019.08581

    Abstract With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.… More >

  • Open Access

    ARTICLE

    A Review on Deep Learning Approaches to Image Classification and Object Segmentation

    Hao Wu1, Qi Liu2, 3, *, Xiaodong Liu4

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 575-597, 2019, DOI:10.32604/cmc.2019.03595

    Abstract Deep learning technology has brought great impetus to artificial intelligence, especially in the fields of image processing, pattern and object recognition in recent years. Present proposed artificial neural networks and optimization skills have effectively achieved large-scale deep learnt neural networks showing better performance with deeper depth and wider width of networks. With the efforts in the present deep learning approaches, factors, e.g., network structures, training methods and training data sets are playing critical roles in improving the performance of networks. In this paper, deep learning models in recent years are summarized and compared with detailed discussion of several typical networks… More >

  • Open Access

    ARTICLE

    Binaural Sound Source Localization Based on Convolutional Neural Network

    Lin Zhou1,*, Kangyu Ma1, Lijie Wang1, Ying Chen1,2, Yibin Tang3

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 545-557, 2019, DOI:10.32604/cmc.2019.05969

    Abstract Binaural sound source localization (BSSL) in low signal-to-noise ratio (SNR) and high reverberation environment is still a challenging task. In this paper, a novel BSSL algorithm is proposed by introducing convolutional neural network (CNN). The proposed algorithm first extracts the spatial feature of each sub-band from binaural sound signal, and then combines the features of all sub-bands within one frame to assemble a two-dimensional feature matrix as a grey image. To fully exploit the advantage of the CNN in extracting high-level features from the grey image, the spatial feature matrix of each frame is used as input to train the… More >

  • Open Access

    RETRACTION

    RETRACTED: Automatic Arrhythmia Detection Based on Convolutional Neural Networks

    Zhong Liu1,2, Xinan Wang1,*, Kuntao Lu1, David Su3

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 497-509, 2019, DOI:10.32604/cmc.2019.04882

    Abstract ECG signal is of great importance in the clinical diagnosis of various heart diseases. The abnormal origin or conduction of excitation is the electrophysiological mechanism leading to arrhythmia, but the type and frequency of arrhythmia is an important indicator reflecting the stability of cardiac electrical activity. In clinical practice, arrhythmic signals can be classified according to the origin of excitation, the frequency of excitation, or the transmission of excitation. Traditional heart disease diagnosis depends on doctors, and it is influenced by doctors' professional skills and the department's specialty. ECG signal has the characteristics of weak signal, low frequency, large variation,… More >

  • Open Access

    ARTICLE

    Image Augmentation-Based Food Recognition with Convolutional Neural Networks

    Lili Pan1, Jiaohua Qin1,*, Hao Chen2, Xuyu Xiang1, Cong Li1, Ran Chen1

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 297-313, 2019, DOI:10.32604/cmc.2019.04097

    Abstract Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer learning technology based on deep… More >

  • Open Access

    ARTICLE

    An Ensemble Based Hand Vein Pattern Authentication System

    M. Rajalakshmi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 209-220, 2018, DOI:10.3970/cmes.2018.114.209

    Abstract Amongst several biometric traits, Vein pattern biometric has drawn much attention among researchers and diverse users. It gains its importance due to its difficulty in reproduction and inherent security advantages. Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics. However, most implementations have been based on small datasets due to the difficulties in obtaining samples. In this paper, a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks (CNN) using a larger dataset. Also, modifications are suggested for implementation using ensemble methods. Ensembles were used… More >

  • Open Access

    ARTICLE

    Text Detection and Recognition for Natural Scene Images Using Deep Convolutional Neural Networks

    Xianyu Wu1, Chao Luo1, Qian Zhang2, Jiliu Zhou1, Hao Yang1, 3, *, Yulian Li1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 289-300, 2019, DOI:10.32604/cmc.2019.05990

    Abstract Words are the most indispensable information in human life. It is very important to analyze and understand the meaning of words. Compared with the general visual elements, the text conveys rich and high-level moral information, which enables the computer to better understand the semantic content of the text. With the rapid development of computer technology, great achievements have been made in text information detection and recognition. However, when dealing with text characters in natural scene images, there are still some limitations in the detection and recognition of natural scene images. Because natural scene image has more interference and complexity than… More >

  • Open Access

    ARTICLE

    Few-Shot Learning with Generative Adversarial Networks Based on WOA13 Data

    Xin Li1,2, Yanchun Liang1,2, Minghao Zhao1,2, Chong Wang1,2,3, Yu Jiang1,2,*

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1073-1085, 2019, DOI:10.32604/cmc.2019.05929

    Abstract In recent years, extreme weather events accompanying the global warming have occurred frequently, which brought significant impact on national economic and social development. The ocean is an important member of the climate system and plays an important role in the occurrence of climate anomalies. With continuous improvement of sensor technology, we use sensors to acquire the ocean data for the study on resource detection and disaster prevention, etc. However, the data acquired by the sensor is not enough to be used directly by researchers, so we use the Generative Adversarial Network (GAN) to enhance the ocean data. We use GAN… More >

  • Open Access

    ARTICLE

    Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

    Feng Xu1, Xuefen Zhang2,*, Zhanhong Xin1, Alan Yang3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 697-709, 2019, DOI:10.32604/cmc.2019.05375

    Abstract Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far… More >

  • Open Access

    ARTICLE

    Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

    Min Long1,2,*, Yan Zeng1

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 493-504, 2019, DOI:10.32604/cmc.2019.04378

    Abstract Aim to countermeasure the presentation attack for iris recognition system, an iris liveness detection scheme based on batch normalized convolutional neural network (BNCNN) is proposed to improve the reliability of the iris authentication system. The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris, including convolutional layer, batch-normalized (BN) layer, Relu layer, pooling layer and full connected layer. The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels, and then the iris features are extracted by BNCNN. With these features, the genuine iris and fake iris are determined by… More >

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