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Search Results (11)
  • 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 the distinction and quality of… More >

  • Open Access


    Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

    A. Selvi*, S. Thilagamani

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2973-2987, 2023, DOI:10.32604/iasc.2022.029850

    Abstract Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman filter and scale-invariant feature transform… More >

  • Open Access


    Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

    Sandeep Kaur*, Bharat Bhushan Sagar

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1541-1559, 2023, DOI:10.32604/cmc.2023.032857

    Abstract Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars, stones, or leaves. Optical recognition systems can help in preserving, sharing, and accelerate the study of the ancient scripts, but lack of standard dataset for such scripts is a major constraint. Although many scholars and researchers have captured and uploaded inscription images on various websites, manual searching, downloading and extraction of these images is tedious and error prone. Web search queries return a vast number of irrelevant results, and manually extracting images for a specific script is not scalable. This paper proposes a… More >

  • Open Access


    Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

    J. S. Sujin1,*, S. Sophia2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 157-171, 2023, DOI:10.32604/csse.2023.023747

    Abstract Digital picture forgery detection has recently become a popular and significant topic in image processing. Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases. The practice of modifying original photographic images to generate a forged image is known as digital image forging. A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery. In order to make the forgeries real and inconspicuous, geometric or post-processing techniques are… More >

  • Open Access


    Deep Learning Framework for Precipitation Prediction Using Cloud Images

    Mirza Adnan Baig*, Ghulam Ali Mallah, Noor Ahmed Shaikh

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4201-4213, 2022, DOI:10.32604/cmc.2022.026225

    Abstract Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers… More >

  • Open Access


    Robust Watermarking of Screen-Photography Based on JND

    Siyu Gu1, Jin Han1,*, Xingming Sun1,2, Yi Cao1,3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4819-4833, 2022, DOI:10.32604/cmc.2022.023955

    Abstract With the popularity of smartphones, it is often easy to maliciously leak important information by taking pictures of the phone. Robust watermarking that can resist screen photography can achieve the protection of information. Since the screen photo process can cause some irreversible distortion, the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high. Therefore, this paper proposes a new screen-photography robust watermark. In terms of embedding region selection, the intensity-based Scale-invariant feature transform (SIFT) algorithm used for the construction of feature regions based on the density of feature points,… 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 points when the distance is… More >

  • Open Access


    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

    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 MACH filter fails, the algorithms… More >

  • Open Access


    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 the algorithm. More >

  • Open Access


    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 classification and recognition. Based on… More >

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