Meng Xu¹, Chen Shen², Jun Zhang², Zhipeng Wang³, Zhiwei Ruan², Stefan Poslad¹, Pengfei Xu^2,*

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 >

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 >

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 >

J. S. Sujin^1,*, 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 >

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 >

Siyu Gu¹, Jin Han^1,*, Xingming Sun^1,2, Yi Cao^1,3

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 >

Tamara A. Al-Shurbaji¹, Khalid A. AlKaabneh², Issam Alhadid^3,*, 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 >

Haris Masood¹, Amad Zafar², Muhammad Umair Ali³, Muhammad Attique Khan⁴, Salman Ahmed¹, Usman Tariq⁵, Byeong-Gwon Kang⁶, Yunyoung Nam^6,*

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 >

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 >

Zhizheng Zhang¹, Jing Feng^1,*, 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 >