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    Wyświetlanie 1-5 z 5
    Tytuł:
    A flexible, high performance hardware implementation of the simplified histogram of oriented gradients descriptor
    Autorzy:
    Kraft, M.
    Olejniczak, M.
    Fularz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/114399.pdf
    Data publikacji:
    2017
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    HoG
    FPGA
    image processing
    Opis:
    In this paper, a high performance, configurable, compact hardware architecture for computing the histogram of oriented gradients (HoG) descriptors is presented. The descriptor computation algorithm is simplified w.r.t. to the original solution, enabling hardware resource cost reduction with only a small accuracy penalty. The proposed architecture can be accommodated to different block sizes and different block grid configurations, enabling its use in a wide range of object detection and recognition tasks with varying region of interest sizes. The resulting architecture is systolic and massively parallel, enabling high throughput processing.
    Źródło:
    Measurement Automation Monitoring; 2017, 63, 5; 177-179
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Human Detection in Thermal Images Using Low-level Features
    Autorzy:
    Budzan, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/114333.pdf
    Data publikacji:
    2015
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    human detection
    infrared
    feature extraction
    HOG
    Opis:
    In this work the human detection method in infrared has been presented. The proposed solution focuses on the use low-level features and detecting parts of the human body. Low–level processing is based on modified HOG (Histogram of Oriented Gradients) algorithm. First, the only squared cells have been used, also calculation of the gradient has been improved. Next, the model of the head from the dataset IR (Infra Red) images has been created, also the model of the human body. Finally, the probability matrix has been examined using minimal distance classifier. The novelty of the proposed solution focuses on the combination of the pixel-gradient and body parts processing, also three stage classification process (head modelling, human modelling and classifier), which has been proposed to reduce the false detection. The experiments were performed on self-created IR images database, which contains images with most of the possible difficult situations such as overlapped people, different pose, small and high resolution of the people. The performance of the proposed algorithm was evaluated using Precision and Recall quality measure.
    Źródło:
    Measurement Automation Monitoring; 2015, 61, 6; 191-194
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Overcoming Overfitting Challenges with HOG Feature Extraction and XGBoost-Based Classification for Concrete Crack Monitoring
    Autorzy:
    Barkiah, Ida
    Sari, Yuslena
    Powiązania:
    https://bibliotekanauki.pl/articles/27311909.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    HOG
    XGBoost
    classification
    feature extraction
    concrete crack monitoring
    Opis:
    This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 3; 571--577
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models
    Autorzy:
    Xu, Jun
    Wei, Yumeng
    Wang, Aichun
    Zhao, Heng
    Lefloch, Damien
    Powiązania:
    https://bibliotekanauki.pl/articles/2200761.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
    Tematy:
    e-commerce
    clothing image classification
    traditional machine learning
    CNN
    HOG
    SVM
    small VGG network
    Opis:
    Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups: pure clothing images and dressed clothing images. Targeting small and medium-sized clothing companies or merchants, it compares traditional machine learning and deep learning models to determine suitable models for each group. For pure clothing images, the HOG+SVM algorithm with the Gaussian kernel function obtains the highest classification accuracy of 91.32% as compared to the Small VGG network. For dressed clothing images, the CNN model obtains a higher accuracy than the HOG+SVM algorithm, with the highest accuracy rate of 69.78% for the Small VGG network. Therefore, for end-users with only ordinary computing processors, it is recommended to apply the traditional machine learning algorithm HOG+SVM to classify pure clothing images. The classification of dressed clothing images is performed using a more efficient and less computationally intensive lightweight model, such as the Small VGG network.
    Źródło:
    Fibres & Textiles in Eastern Europe; 2022, 5 (151); 66--78
    1230-3666
    2300-7354
    Pojawia się w:
    Fibres & Textiles in Eastern Europe
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Age-Type Identification and Classification of Historical Kannada Handwritten Scripts using Line Segmentation with HOG feature Descriptors
    Autorzy:
    Bannigidad, Parashuram
    Gudada, Chandrashekar
    Powiązania:
    https://bibliotekanauki.pl/articles/1075427.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    HOG
    K-NN
    Kannada
    LDA
    Line segmentation
    Recognition
    Restoration
    SVM
    Seam carving
    document image analysis
    handwritten script
    historical documents
    Opis:
    The offline handwritten text recognition is one of the most challenging tasks in document image analysis; our aim is to recreate the cultural importance of the Kannada Language writing tradition through the historical degraded manuscripts. In the present digital era, we need to protect and digitize the resources of our Indian culture and heritage by digitizing those manuscripts which are losing its status; the degraded manuscripts are influenced by weather condition. In this paper, we have attempted to identify and recognise the historical Kannada handwritten scripts of various dynasties; namely, Vijayanagara dynasty (1460 AD), Mysore Wadiyar dynasty (1936 AD), Vijayanagara dynasty (1400 AD) and Hoysala dynasty (1340 AD) by using the improved seam carving text line segmentation method with HOG feature descriptors. The average classification accuracy for different dynasties are computed. The LDA classifier is yielded 93.4%, K-NN classifier has yielded 92% and SVM classifier has 95.5%. Based on the experimentation, the SVM classifier has recorded good classification performance comparatively LDA and K-NN classifiers for historical Kannada handwritten scripts.
    Źródło:
    World Scientific News; 2019, 126; 23-35
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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