### Simple Histogram Equalization ###
Histogram equalization **improves image contrast** by spreading out intensity values, making dark regions brighter and light regions darker.


**Adaptive Histogram Equalization (CLAHE)**

Unlike traditional histogram equalization, **CLAHE (Contrast Limited Adaptive Histogram Equalization)** improves local contrast while preventing over-brightening in specific areas.

This course provides a comprehensive introduction to Computer Vision, covering both fundamental and advanced topics. You'll learn how machines process, analyze, and interpret visual data through various techniques such as image processing, feature extraction, object detection, and deep learning models.

Computer Vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer Vision integrates with AI and machine learning. 

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Computer Vision Course Outline

Histogram Equalization

Simple Histogram Equalization

Рішення

Histogram Equalization

Simple Histogram Equalization

Рішення