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  • Open Access

    ARTICLE

    Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization

    Mei Wang1,*, Hao Xu2, Yadang Chen1

    Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447

    Abstract Anomaly detection in images has attracted a lot of attention in the field of computer vision. It aims at identifying images that deviate from the norm and segmenting the defect within images. However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios. We proposes a simple framework for unsupervised anomaly detection. Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the multivariate Gaussian (MVG), and determine… More >

  • Open Access

    ARTICLE

    Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

    Ying Ma1, Jiaqi Zhang2,3,4, Pengyu Liu1,2,3,4,*, Zhihao Wei5, Lingfei Zhang1, Xiaowei Jia6

    Journal of New Media, Vol.4, No.4, pp. 165-177, 2022, DOI:10.32604/jnm.2022.026684

    Abstract Plateau forest plays an important role in the high-altitude ecosystem, and contributes to the global carbon cycle. Plateau forest monitoring request in-suit data from field investigation. With recent development of the remote sensing technic, large-scale satellite data become available for surface monitoring. Due to the various information contained in the remote sensing data, obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges. Recent developed deep learning (DL) models such as deep convolutional neural network (CNN) has been widely used in image processing tasks, and shows possibility for remote sensing segmentation. However, due to the unique characteristics… More >

  • Open Access

    ARTICLE

    Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

    Yun Tan1,2, Weizhao Wu2, Ling Tan3, Haikuo Peng2, Jiaohua Qin2,*

    Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113

    Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination… More >

  • Open Access

    ARTICLE

    Microphone Array-Based Sound Source Localization Using Convolutional Residual Network

    Ziyi Wang1, Xiaoyan Zhao1,*, Hongjun Rong1, Ying Tong1, Jingang Shi2

    Journal of New Media, Vol.4, No.3, pp. 145-153, 2022, DOI:10.32604/jnm.2022.030178

    Abstract Microphone array-based sound source localization (SSL) is widely used in a variety of occasions such as video conferencing, robotic hearing, speech enhancement, speech recognition and so on. The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments. In order to improve localization performance, a novel SSL algorithm using convolutional residual network (CRN) is proposed in this paper. The spatial features including time difference of arrivals (TDOAs) between microphone pairs and steered response power-phase transform (SRP-PHAT) spatial spectrum are extracted in each Gammatone sub-band. The spatial features of different sub-bands with a frame are combine into a… More >

  • Open Access

    ARTICLE

    Menu Text Recognition of Few-shot Learning

    Xiaoyu1,2, Tian Zhenzhen2, Xin Zihao2, Liu Suolan2, Chen Fuhua3, Wang Hongyuan2,*

    Journal of New Media, Vol.4, No.3, pp. 137-143, 2022, DOI:10.32604/jnm.2022.027890

    Abstract Recent advances in OCR show that end-to-end (E2E) training pipelines including detection and identification can achieve the best results. However, many existing methods usually focus on case insensitive English characters. In this paper, we apply an E2E approach, the multiplex multilingual mask TextSpotter, which performs script recognition at the word level and uses different recognition headers to process different scripts while maintaining uniform loss, thus optimizing script recognition and multiple recognition headers simultaneously. Experiments show that this method is superior to the single-head model with similar number of parameters in end-to-end identification tasks. More >

  • Open Access

    ARTICLE

    No-Reference Stereo Image Quality Assessment Based on Transfer Learning

    Lixiu Wu1,*, Song Wang2, Qingbing Sang3

    Journal of New Media, Vol.4, No.3, pp. 125-135, 2022, DOI:10.32604/jnm.2022.027199

    Abstract In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. At the same time, the… More >

  • Open Access

    ARTICLE

    Research on Image Quality Enhancement Algorithm Using Hessian Matrix

    Xi Chen1, Yanpeng Wu2,*, Chenxue Zhu2, Hongjun Liu3

    Journal of New Media, Vol.4, No.3, pp. 117-123, 2022, DOI:10.32604/jnm.2022.027060

    Abstract The Hessian matrix has a wide range of applications in image processing, such as edge detection, feature point detection, etc. This paper proposes an image enhancement algorithm based on the Hessian matrix. First, the Hessian matrix is obtained by convolving the derivative of the Gaussian function. Then use the Hessian matrix to enhance the linear structure in the image. Experimental results show that the method proposed in this paper has strong robustness and accuracy. More >

  • Open Access

    ARTICLE

    Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

    Liguo Zhang1,2, Zilin Tian1, Yan Zhang3,*, Tong Shuai4, Shuo Liang4, Zhuofei Wu5

    Journal of New Media, Vol.4, No.2, pp. 59-71, 2022, DOI:10.32604/jnm.2022.029360

    Abstract In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just a small number of training data are available. In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition, in this paper, we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model (RBnet) for SAR image target recognition. In the RBnet, a… More >

  • Open Access

    ARTICLE

    Skeleton Keypoints Extraction Method Combined with Object Detection

    Jiabao Shi1, Zhao Qiu1,*, Tao Chen1, Jiale Lin1, Hancheng Huang2, Yunlong He3, Yu Yang3

    Journal of New Media, Vol.4, No.2, pp. 97-106, 2022, DOI:10.32604/jnm.2022.027176

    Abstract Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point extraction method combined with object… More >

  • Open Access

    ARTICLE

    Blood Sample Image Classification Algorithm Based on SVM and HOG

    Tianyi Jiang1, Shuangshuang Ying2, Zhou Fang1, Xue Song1, Yinggang Sun2, Dongyang Zhan3,4, Chao Ma2,*

    Journal of New Media, Vol.4, No.2, pp. 85-95, 2022, DOI:10.32604/jnm.2022.027175

    Abstract In the medical field, the classification and analysis of blood samples has always been arduous work. In the previous work of this task, manual classification maneuvers have been used, which are time consuming and laborious. The conventional blood image classification research is mainly focused on the microscopic cell image classification, while the macroscopic reagent processing blood coagulation image classification research is still blank. These blood samples processed with reagents often show some inherent shape characteristics, such as coagulation, attachment, discretization and so on. The shape characteristics of these blood samples also make it possible for us to recognize their classification… More >

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