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  • Open Access

    ARTICLE

    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 - 22 September 2021

    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 >

  • Open Access

    ARTICLE

    Recognition and Tracking of Objects in a Clustered Remote Scene Environment

    Haris Masood1, Amad Zafar2, Muhammad Umair Ali3, Muhammad Attique Khan4, Salman Ahmed1, Usman Tariq5, Byeong-Gwon Kang6, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1699-1719, 2022, DOI:10.32604/cmc.2022.019572 - 07 September 2021

    Abstract Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based… More >

  • Open Access

    ARTICLE

    The Crime Scene Tools Identification Algorithm Based on GVF‐Harris‐SIFT and KNN

    Nan Pan1, Dilin Pan2, Yi Liu2

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 413-419, 2019, DOI:10.31209/2019.100000103

    Abstract In order to solve the cutting tools classification problem, a crime tool identification algorithm based on GVF-Harris-SIFT and KNN is put forward. The proposed algorithm uses a gradient vector to smooth the gradient field of the image, and then uses the Harris angle detection algorithm to detect the tool angle. After that, the descriptors of the eigenvectors in corresponding feature points were using SIFT to obtained. Finally, the KNN machine learning algorithms is employed to for classification and recognition. The experimental results of the comparison of the cutting tools show the accuracy and reliability of More >

  • Open Access

    ARTICLE

    Ground-Based Cloud Recognition Based on Dense_SIFT Features

    Zhizheng Zhang1, Jing Feng1,*, Jun Yan2, Xiaolei Wang1, Xiaocun Shu1

    Journal of New Media, Vol.1, No.1, pp. 1-9, 2019, DOI:10.32604/jnm.2019.05937

    Abstract Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for More >

  • Open Access

    ARTICLE

    A Robust Zero-Watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain

    Jialing Liu1, Jingbing Li1,2,*, Yenwei Chen3, Xiangxi Zou1, Jieren Cheng1,2, Yanlin Liu1, Uzair Aslam Bhatti1,2

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 363-378, 2019, DOI:10.32604/cmc.2019.06037

    Abstract Remote medical diagnosis can be realized by using the Internet, but when transmitting medical images of patients through the Internet, personal information of patients may be leaked. Aim at the security of medical information system and the protection of medical images, a novel robust zero-watermarking based on SIFT-DCT (Scale Invariant Feature Transform-Discrete Cosine Transform) for medical images in the encrypted domain is proposed. Firstly, the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images. Then, the SIFT-DCT is used to extract the feature More >

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