Deep Learning

Introduction to Emotion Recognition In the realm of computer vision and deep learning, recognizing facial emotions is a fascinating and increasingly important task. Imagine a system that can tell whether you’re happy, sad, or just neutral – it sounds like something out of a sci-fi movie, but it’s very much a reality today. In this article, we’ll delve into the world of facial emotion recognition using OpenCV and the Deepface library in Python....

Введение в распознавание эмоций Распознавание эмоций по лицу — увлекательная и всё более важная задача в области компьютерного зрения и глубокого обучения. Представьте себе систему, которая может определить, счастливы вы, грустны или просто нейтральны — это похоже на что-то из научно-фантастического фильма, но сегодня это реальность. В этой статье мы погрузимся в мир распознавания эмоций с помощью OpenCV и библиотеки Deepface в Python. Зачем нужно распознавать эмоции? Прежде чем углубляться в технические детали, давайте поговорим о том, почему распознавание эмоций так ценно....

Introduction to Medical Image Segmentation Medical image segmentation is a crucial task in the field of medical imaging, enabling the precise identification and isolation of specific regions of interest within images. This process is vital for diagnosis, treatment planning, and surgical interventions. One of the most popular and effective architectures for this task is the U-Net, a type of convolutional neural network (CNN) specifically designed for biomedical image segmentation. What is U-Net?...

Introduction to YOLO and Object Detection Object detection is a fundamental task in computer vision that involves identifying and locating objects within images or video frames. One of the most advanced and efficient algorithms for this task is YOLO (You Only Look Once). YOLO is known for its speed and accuracy, making it suitable for real-time applications such as self-driving cars, surveillance systems, and robotics. How YOLO Works Preprocessing: The input image is resized to a fixed size and the pixel values are normalized....