Dr. Saida Safibullayevna S. Beknazarova Profile

Dr. Saida Safibullayevna S. Beknazarova

at Tashkent Univ of Information Technology

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Publications (4)

Proceedings Article | 20 February 2024 Paper

Methods of increasing the contrast of images in video information systems

S. Beknazarova, S. Abdullayev, O. Abdullayeva, Z. Abdullayev

Proceedings Volume 13065, 130650L (2024) https://doi.org/10.1117/12.3025016

KEYWORDS: Image processing, Image filtering, Tunable filters, Video, Matrices, Video compression, Digital filtering, Video processing, Televisions, Digital image processing

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Proceedings Article | 20 February 2024 Paper

Machine learning algorithms are used to detect and track objects on video images

S. Beknazarova, N. Latipova, M. Maxmudova, V. Alekseeva, A. Turakulova

Proceedings Volume 13065, 130650K (2024) https://doi.org/10.1117/12.3025015

KEYWORDS: Detection and tracking algorithms, Object detection, Machine learning, Video, Video surveillance, Education and training, Quantization, Neural networks, Computer vision technology, Image processing

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In recent years, the integration of machine learning algorithms has significantly advanced the field of computer vision, particularly in the domain of object detection and tracking in video images. This article explores the application of machine learning techniques to enhance the accuracy and efficiency of object detection and tracking systems. The article begins by providing an overview of the challenges associated with traditional methods of object detection and tracking, highlighting the limitations in handling complex scenarios and diverse object types. Subsequently, it delves into the methodology of employing machine learning algorithms, emphasizing their adaptability and capability to discern patterns and features crucial for accurate object recognition. Various machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are discussed in detail, elucidating their roles in extracting meaningful representations from video frames. The training process is explored, encompassing the use of labeled datasets to enable the algorithms to generalize and make informed predictions in real-world scenarios. Furthermore, the article investigates the integration of deep learning techniques, exploring the advantages of transfer learning and fine-tuning pre-trained models to optimize performance. The role of neural networks in handling object occlusion, scale variations, and pose changes is emphasized, showcasing the adaptability of machine learning algorithms to dynamic and unpredictable environments. The practical implementation of these algorithms in object detection and tracking systems is presented, highlighting real-world applications across industries such as surveillance, autonomous vehicles, and industrial automation. The article concludes by discussing ongoing research and potential future developments, addressing the evolving landscape of machine learning in computer vision and its implications for advancing object detection and tracking capabilities in video images.

Proceedings Article | 20 February 2024 Paper

Algorithm for finding reference brightness correction coefficients

Ozoda Abdullayeva, Saida Beknazarova

Proceedings Volume 13065, 1306517 (2024) https://doi.org/10.1117/12.3025212

KEYWORDS: Matrices, Image processing, Color and brightness control algorithms, Image quality, Image filtering, Digital image processing, Image segmentation, Tunable filters, Visualization, Televisions

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Proceedings Article | 18 May 2022 Paper

Digital images processing: filtering by pulse characteristic

S. Beknazarova, O. Talipova, K. Ibragimova

Proceedings Volume 12251, 122510A (2022) https://doi.org/10.1117/12.2631511

KEYWORDS: Image filtering, Digital filtering, Image processing, Image segmentation, Optical filters, Digital image processing, Televisions, Control systems, Fourier transforms, Pulse filters

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