Перегляд за автором "Daradkeh Yousef Ibrahim"

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  • Зображення мініатюри
    Публікація
    Classification of Images Based on a System of Hierarchical Features
    (2022) Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Al-Dhaifallah Mujahed
    The results of the development of the new fast-speed method of classification images using a structural approach are presented. The method is based on the system of hierarchical features, based on the bitwise data distribution for the set of descriptors of image description. The article also proposes the use of the spatial data processing apparatus, which simplifies and accelerates the classification process. Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure, for which the sets of descriptors are compared. The introduction of the system of hierarchical features allows to further reduce the calculation time by 2–3 times while ensuring high efficiency of classification. The noise immunity of the method to additive noise has been experimentally studied. According to the results of the research, the marginal degree of the hierarchy of features for reliable classification with the standard deviation of noise less than 30 is the 8-bit distribution. Computing costs increase proportionally with decreasing bit distribution. The method can be used for application tasks where object identification time is critical.
  • Зображення мініатюри
    Публікація
    Cluster representation of the structural description of images for effective classification
    (Computers, Materials & Continua, 2022) Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Zeghid, M.
    The problem of image recognition in the computer vision systems is being studied. The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided. We propose three versions of the classifier according to the following principles: “object–etalon”, “object descriptor–etalon” and “vector description of the object–etalon”, which are not similar in level of integration of researched data analysis. The options for constructing clusters over the whole set of descriptions of the etalon database, separately for each of the etalons, as well as the optimal method to compare sets of segment centers for the etalons and object, are implemented. An experimental rating of the efficiency of the created classifiers in terms of productivity, processing time, and classification quality has been realized of the applied. The proposed methods classify the set of etalons without error. We have formed the inference about the efficiency of classification approaches based on segment centers. The time of image processing according to the developed methods is hundreds of times less than according to the traditional one, without reducing the accuracy.
  • Зображення мініатюри
    Публікація
    Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic
    (2021) Daradkeh Yousef Ibrahim; Tvoroshenko, I. S.; Gorokhovatskyi, V. O.; Latiff, L. A.; Ahmad N.
    ABSTRACT The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problemof an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the in uence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classi cation and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The veri cation of theoretical foundations and evaluation of the effectiveness of the proposed data processingmethods for real image bases is performed using theOpenCV library. The applied signi cance of thework is substantiated according to the criterion of data processing timewithout reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.
  • Зображення мініатюри
    Публікація
    Development of QoS Methods in the Information Networks with Fractal Traffic
    (International Journal of Electronics and Telecommunications, 2018) Кіріченко, Л. О.; Радівілова, Т. А.; Daradkeh Yousef Ibrahim
    The paper discusses actual task of ensuring the quality of services in information networks with fractal traffic. The generalized approach to traffic management and quality of service based on the account of multifractal properties of the network traffic is proposed. To describe the multifractal traffic properties, it is proposed to use the Hurst exponent, the range of generalized Hurst exponent and coefficient of variation. Methods of preventing of network overload in communication node, routing cost calculation and load balancing, which based on fractal properties of traffic are presented. The results of simulation have shown that the joint use of the proposed methods can significantly improve the quality of service network.
  • Зображення мініатюри
    Публікація
    Statistical data analysis models for determining the relevance of structural image descriptions
    (IEEE Access, 2023) Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Gadetska, S.; Al-Dhaifallah Mujahed
    The aim of the research is to improve the effectiveness of image recognition methods according to the description in the form of a set of keypoint descriptors. The focus is on increasing the speed of analysis and processing of description data while maintaining the required level of classification efficiency. The class of the image is provided as a description of the etalon. It is proposed to transform the description by implementing a statistical system of features for non-intersecting data fragments. The developed method is based on the aggregation of data distribution values within the description, the basis of which is the bit representation of the descriptors. Statistical features are calculated as the frequency of occurrence of the fixed value of a fragment on a set of description data and thus reflect the individual properties of images. Three main classifier models are analyzed: calculating the measure of data relevance in the form of distributions; assigning each of the descriptors to defined classes (voting); using the apparatus of statistical data analysis to decide on the significance of the difference between the distributions of the object and etalons. The results of software modeling of methods and calculations of statistical significance of differences based on distributions for training sets of images are represented. Using distributions instead of a set of descriptors increases the processing speed by hundreds of times, while the classification accuracy is maintained at a sufficient level and does not deteriorate compared to traditional voting.
  • Зображення мініатюри
    Публікація
    Tools for Fast Metric Data Search in Structural Methods for Image Classification
    (IEEE Access, 2022-01-02) Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Zeghid Medien
    The article proposes a new classification method based on implementing the high-speed search tools for the indexed data structure created on the etalon set of features, which has significant advantages in processing speed compared to the traditional approaches. The classifier is represented as two-stage processing, where at the first stage the class for the separate object descriptor is determined, and at the second stage, the resulting class of the object is determined based on the obtained set of local solutions. The developed method is based on the preliminary construction of the indexed hash structures for the set of descriptors of the base of the etalon images. Implementing the hash representation allows for increasing the speed of identification or classification of visual objects. A comparative experiment with the traditional method of voting has been conducted, where the linear search for the nearest descriptor has been implemented for the identification without the use of prior creation of the indexed hash representation of the etalons. In the experiment, we have gained in processing speed for the developed method compared to the traditional over 10 times. The gain in processing speed increases proportionally with the number of the etalons and the number of the descriptors in the descriptions. The experiment has shown that the efficiency of the method can be enhanced by varying the values of its parameters and adapting to the properties of the data.