Publications Scientifiques

Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10

Browse

Has files: NoSubject: search.filters.subject.LBP

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Enhanced Signal Processing for Echo Detection Using Support Vector Machine, Weber’s Law Features, and Local Descriptors (LDP and LOOP)
    (Springer Science and Business Media, 2025) Hedir, Mehdia; Messaoui, Ali Zakaria; Messaoui, Aimen Abdelhak; Belaidi, Hadjira; Rouigueb, Abdenebi; Nemra, Abdelkrim
    Removing ground echoes from weather radar images is a critical task due to their substantial influence on the accuracy of processed meteorological data. These echoes often obscure the true atmospheric signals, particularly precipitation, which is essential for weather forecasting and analysis. In this study, we aim to develop advanced methods that not only eliminate ground echoes but also preserve precipitation signals, ensuring accurate meteorological observations. To achieve this, we explore the use of Local Descriptors based on Weber’s Law Descriptor (WLD) and combine it with the Local Binary Pattern (WLBP) descriptor, as well as introducing two novel descriptors: Local Directional Pattern (LDP) and Local Optimal-Oriented Pattern (LOOP). These descriptors are employed to capture various local features and patterns within the radar images that are crucial for distinguishing between ground echoes and precipitation. To automate the classification of these echo types, we leverage Support Vector Machine (SVM) classifiers, which have proven to be effective in high-dimensional pattern recognition tasks. Our proposed methods are rigorously tested at the Setif and Bordeaux sites, allowing for comprehensive evaluation under different weather conditions. The results from these tests demonstrate the effectiveness of the proposed techniques in accurately identifying and eliminating ground echoes while preserving precipitation. Specifically, the integration of LDP and LOOP significantly enhances the ability to differentiate between echoes, improving the robustness of the classifier in challenging environments. The outcomes indicate that these methods show considerable promise for practical applications in meteorological data processing, providing a reliable solution for improving the quality of weather radar data and supporting more accurate weather predictions.
  • No Thumbnail Available
    Item
    Identity recognition based on palmprints : the preliminary results
    (IEEE, 2022) Amrouni, Nadia; Benzaoui, Amir; Adjabi, Insaf
    Private and automatic recognition in many applications, such as forensic, access control, and surveillance systems, has become necessary in recent years. Biometrics, which treats individuals' identification based on physical or behavioral characteristics, has emerged as an effective automated identification technology, offering more properties and advantages than conventional protection. The use of palmprints in biometric authentication has dramatically increased and has been used extensively in management systems for businesses, Internet of Thinks, and individuals. In this field, the palmprint is considered a new modality, a unique entity that is stable over time and has a rich information structure. As part of this work, the local binary pattern descriptor (LBP) was used and tested under several configurations to extract the palmprint modality's optimal and efficient characteristics. As preliminary results, our experiments on the IITD Palmprint V1 database exhibit impressive performance
  • No Thumbnail Available
    Item
    A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative
    (Springer, 2022) Messaoudene, Khadidja; Harrar, Khaled
    Knee OsteoArthritis (KOA) is a disease characterized by a degeneration of cartilage and the underlying bone. It does not evolve uniformly; it can stay silent for a long time and can quickly intensify for several months or weeks. For this reason, it is necessary to develop an automatic system for diagnosis and reduce the subjectivity in the detection of the disease. In this paper, we present a method for detecting knee osteoarthritis based on the combination of histograms of oriented gradient (HOG) and local binary pattern (LBP). Four classifiers including KNN, SVM, Adaboost, and Naïve Bayes were tested and compared for the prediction of the illness. A total of 620 X-Ray images were analyzed, composed of 310 images from healthy subjects (Grade 0), and 310 images from pathological patients (Grade 2). The results obtained reveal that Naïve Bayes achieved the highest performance in terms of accuracy (ACC = 91%) on the Osteoarthritis Initiative (OAI) dataset. The fusion of HOG and LBP features in KOA classification outperforms the use of either feature alone and the existing methods in the literature