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  • Open Access


    New Fragile Watermarking Technique to Identify Inserted Video Objects Using H.264 and Color Features

    Raheem Ogla1,*, Eman Shakar Mahmood1, Rasha I. Ahmed1, Abdul Monem S. Rahma2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3075-3096, 2023, DOI:10.32604/cmc.2023.039818

    Abstract The transmission of video content over a network raises various issues relating to copyright authenticity, ethics, legality, and privacy. The protection of copyrighted video content is a significant issue in the video industry, and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media. However, there are still many unresolved challenges. This paper aims to address those challenges by proposing a new technique for detecting moving objects in digital videos, which can help prove the credibility of video content by detecting any fake… More >

  • Open Access


    Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

    Vidit Kumar1,*, Hemant Petwal2, Ajay Krishan Gairola1, Pareshwar Prasad Barmola1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047

    Abstract Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to… More >

  • Open Access


    Machine Learning-based Detection and Classification of Walnut Fungi Diseases

    Muhammad Alyas Khan1, Mushtaq Ali1, Mohsin Shah2, Toqeer Mahmood3, Muneer Ahmad4, NZ Jhanjhi5, Mohammad Arif Sobhan Bhuiyan6,*, Emad Sami Jaha7

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 771-785, 2021, DOI:10.32604/iasc.2021.018039

    Abstract Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using… More >

  • Open Access


    The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection

    Lei Feng1,2, Haibin Li1,*, Yakun Gao1, Yakun Zhang1

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 831-839, 2020, DOI:10.32604/iasc.2020.010117

    Abstract In the paper, we apply the sparse reconstruction algorithm of improved background dictionary to saliency detection. Firstly, after super-pixel segmentation, two bottom features are extracted: the color information of LAB and the texture features of the image by Gabor filter. Secondly, the convex hull theory is used to remove object region in boundary region, and K-means clustering algorithm is used to continue to simplify the background dictionary. Finally, the saliency map is obtained by calculating the reconstruction error. Compared with the mainstream algorithms, the accuracy and efficiency of this algorithm are better than those of More >

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