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  • Open Access


    A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

    Mu Ye1,2,3,4, Li Ji1, Luo Tianye1, Li Sihan5, Zhang Tong1, Feng Ruilong1, Hu Tianli1,2,3,4, Gong He1,2,3,4, Guo Ying1,2,3,4, Sun Yu1,2,3,4, Thobela Louis Tyasi6, Li Shijun7,8,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6195-6205, 2022, DOI:10.32604/cmc.2022.026880

    Abstract Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of the model to 98%. The… More >

  • Open Access


    Research on Tracking and Registration Algorithm Based on Natural Feature Point

    Tingting Yang1,*, Shuwen Jia1, Boxiong Yang1, Chenxi Kan2

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 683-692, 2021, DOI:10.32604/iasc.2021.017235

    Abstract In the augmented reality system, the position and direction of the user’s point of view and line of sight in the real scene is acquired in real-time. The position and direction information will determine the exact position of the virtual object of the real scene. At the same time, various coordinate systems are established according to the user’s line of sight. So registration tracking technology is very important. The paper proposes an accurate, stable, and effective augmented reality registration algorithm. The method adopts the method of ORB (oriented FAST and rotated BRIEF) features matching combined with RANSAC (random sample consensus)… More >

  • Open Access


    Text Detection and Classification from Low Quality Natural Images

    Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775

    Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal… More >

  • Open Access


    Methods to Automatically Build Point Distribution Models for Objects like Hand Palms and Faces Represented in Images

    Maria João M. Vasconcelos1, João Manuel R. S. Tavares1

    CMES-Computer Modeling in Engineering & Sciences, Vol.36, No.3, pp. 213-242, 2008, DOI:10.3970/cmes.2008.036.213

    Abstract In this work we developed methods to automatically extract significant points of objects like hand palms and faces represented in images that can be used to build Point Distribution Models automatically. These models are further used to segment the modelled objects in new images, through the use of Active Shape Models or Active Appearance Models. These models showed to be efficient in the segmentation of objects, but had as drawback the fact that the labelling of the landmark points was usually manually made and consequently time consuming. Thus, in this paper we describe some methods capable to extract significant points… More >

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