The article presents the development of an interactive system for recognizing and classifying human hand gestures based on machine learning technologies. A new approach to gesture representation is proposed, combining spatial and temporal characteristics of the location of key points of the hand, which ensures high accuracy, noise resistance, and adaptability of the system to various conditions of use.
This paper presents a study on organizing requirements for automated meat quality control systems. It identifies key quality indicators–color, texture, marbling, and gloss–and analyzes the technical and functional parameters essential for practical assessment. The research highlights integrating computer vision, image processing, and machine learning algorithms to enhance objectivity, accuracy, and evaluation speed. The proposed approach aims to reduce human influence, enable real-time monitoring, and offer scalable solutions suitable for large-scale producers and small enterprises.
A brief description of the basic stages of image processing is given to pay attention to the segmentation stage as a
possible way to improve efficiency in decision-making. The main characteristics of the presented model are visual signs, such as
color, shape, the presence of a stem, and others. Due to the different approaches in image processing, a high level of truthfulness is
achieved, which is expressed in the percentage ratio of the accuracy of decision-making and varies in the range from 90 to 96%.