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  • Open Access


    Improved HardNet and Stricter Outlier Filtering to Guide Reliable Matching

    Meng Xu1, Chen Shen2, Jun Zhang2, Zhipeng Wang3, Zhiwei Ruan2, Stefan Poslad1, Pengfei Xu2,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4785-4803, 2023, DOI:10.32604/cmc.2023.034053

    Abstract As the fundamental problem in the computer vision area, image matching has wide applications in pose estimation, 3D reconstruction, image retrieval, etc. Suffering from the influence of external factors, the process of image matching using classical local detectors, e.g., scale-invariant feature transform (SIFT), and the outlier filtering approaches, e.g., Random sample consensus (RANSAC), show high computation speed and pool robustness under changing illumination and viewpoints conditions, while image matching approaches with deep learning strategy (such as HardNet, OANet) display reliable achievements in large-scale datasets with challenging scenes. However, the past learning-based approaches are limited to… More >

  • Open Access


    Person Re-Identification Using LBPH and K-Reciprocal Encoding

    V. Manimaran*, K. G. Srinivasagan

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1109-1121, 2022, DOI:10.32604/iasc.2022.023145

    Abstract Individual re-identification proof (Re-ID) targets recovering an individual of interest across different non-covering cameras. With the recent development of technological algorithm and expanding request of intelligence video observation, it has acquired fundamentally expanded interest in the computer vision. Person re-identification is characterized as the issue of perceiving an individual caught in different occasions and additionally areas more than a few nonoverlapping camera sees, thinking about a huge arrangement of up-and-comers. This issue influences essentially the administration of disseminated, multiview observation frameworks, in which subjects should be followed across better places, either deduced or on-the-fly when… More >

  • Open Access


    An Optimized Scale-Invariant Feature Transform Using Chamfer Distance in Image Matching

    Tamara A. Al-Shurbaji1, Khalid A. AlKaabneh2, Issam Alhadid3,*, Ra’ed Masa’deh4

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 971-985, 2022, DOI:10.32604/iasc.2022.019654

    Abstract Scale-Invariant Feature Transform is an image matching algorithm used to match objects of two images by extracting the feature points of target objects in each image. Scale-Invariant Feature Transform suffers from long processing time due to embedded calculations which reduces the overall speed of the technique. This research aims to enhance SIFT processing time by imbedding Chamfer Distance Algorithm to find the distance between image descriptors instead of using Euclidian Distance Algorithm used in SIFT. Chamfer Distance Algorithm requires less computational time than Euclidian Distance Algorithm because it selects the shortest path between any two More >

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