segmentation

This paper presents an approach to improving mobile robot localization by filtering dynamic objects using camera image segmentation. The proposed algorithm integrates a Particle Filter with state-of-the-art computer vision techniques, specifically employing the YOLO model for segmentation, which effectively differentiates static elements of the environment from moving objects. This approach reduces the impact of noisy data and enhances localization accuracy in dynamic conditions, which is crucial for the reliable autonomous operation of mobile robots.

The paper investigates computer diagnostic systems, their architectures, methods, and algorithms used in their work to diagnose cancer, including breast, lung, brain, and other tumors.

Traditional and neural network methods for image segmentation and classification are analyzed and compared, and diagnostic tools in medicine are analyzed.

The key approaches to medical image processing are investigated, in particular, the analysis of segmentation methods based on U-Net networks and classification using convolutional neural networks.

Запропоновано метод підвищення ефективності алгоритму DyXY шляхом сегментації мережі на основі статистичних даних та відносної пропускної характеристики елементів. За цим методом, за наявності кількох вільних альтернативних маршрутів пакет відправляється до сегмента, в якого вища пропускна характеристика. Так зменшується ризик простою пакета через зайнятість транзитного отримувача.

The analyzed market of board games is quite widespread in the world. This market is only gaining momentum and becomes interesting in the future in Ukraine, as it is potentially large-scale and not yet saturated with goods. Indeed, the market is gradually changing and there was a particular jump during the lockdown when people got into board games. Therefore, the article reviews the board games market, which, due to the impact of the Covid-19 pandemic, had a different impact on the enterprise’s activities in this field of activity.

Among the main requirements of modern surveillance systems are stability in the face of negative influences and intellectualization. The purpose of intellectualization is that the surveillance system should perform not only the main functions such as monitoring and stream recording but also have to provide effective stream processing. The requirement for this processing is that the system operation has to be automated, and the operator's influence should be minimal. Modern intelligent surveillance systems require the development of grouping methods.

Purpose. The goal of this work was to develop and study the methods of texture classification of SEM images of micro surfaces of objects based on the statistical and spectral characteristics of texture fragments, as well as a comparative analysis of segmentation methods of SEM images. Methods. The determination of the texture characteristics was based on statistical moments computed by the brightness histogram of a SEM- image or its region. The spectral measures of texture of SEM image were based on properties of the Fourier spectrum.

Object of study. Planned distribution establishment of erosive “spots” of the agricultural lands is based on the processing of binary images of aerial photographic materials using morphological and planimetric methods of analysis. Methodology. Offered methodology is based on the non-linear operators’ application.

The authors emphasize the special significance of customer service on the primary market of residential real estate. There is carried out segmentation of the primary real estate market with the account of such a criterion as a residential housing class. There are singled out the following segments of this market: premium-segment, business-segment, comfort- segment, and economy-segment. The features of each market segment are described.