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Search Results (15)
  • Open Access

    ARTICLE

    Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

    Usman Ali, Muhammad Tariq Mahmood*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1597-1611, 2022, DOI:10.32604/cmc.2022.022219

    Abstract Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operator is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on local binary pattern (LBP) with adaptive threshold for blur detection. The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur, that may… More >

  • Open Access

    ARTICLE

    Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

    Raniyah Wazirali1, Rami Ahmed2,*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 305-320, 2022, DOI:10.32604/csse.2022.020504

    Abstract The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required… More >

  • Open Access

    ARTICLE

    A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

    Shiqi Wang1, Mingfang Jiang2,*, Jiaohua Qin1, Hengfu Yang2, Zhichen Gao3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2979-2993, 2021, DOI:10.32604/cmc.2021.017094

    Abstract In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content,… More >

  • Open Access

    ARTICLE

    Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

    Diyar Qader Zeebaree1, Adnan Mohsin Abdulazeez2, Dilovan Asaad Zebari3,*, Habibollah Haron4, Haza Nuzly Abdull Hamed4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314

    Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level… More >

  • Open Access

    ARTICLE

    Color Image Segmentation Using Soft Rough Fuzzy-C-Means and Local Binary Pattern

    R.V.V. Krishna1,*, S. Srinivas Kumar2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 281-290, 2020, DOI:10.31209/2019.100000121

    Abstract In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the one -against-all multi class support vector machine (MSVM) classifier for segmentation. Local Binary Pattern is used for extracting the textural features and L*a*b color model is used for obtaining the color features. The MSVM is trained using the samples obtained from a novel soft rough fuzzy c-means (SRFCM) clustering. The fuzzy set based membership functions capably handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness… More >

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