Computer Vision Essentials with Python

Comprehensive introduction to Computer Vision, focusing on machine perception and interpretation of visual data. Covers image preprocessing, feature e…Show more

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5 modules

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29 chapters

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56 assignments

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What you will learn

Understanding image representation basics

Applying color models in image processing

Performing image filtering and transformations

Utilizing OpenCV for image manipulation

Implementing edge detection techniques

Building convolutional neural networks

Extracting features with CNN layers

Detecting objects with bounding boxes

Exploring advanced computer vision topics

Course content

Comprehensive introduction to Computer Vision, focusing on machine perception and interpretation of visual data. Covers image preprocessing, feature extraction, object detection, and deep lea…

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.

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Computer vision has significantly advanced over the years, shifting from basic image processing methods to complex deep learning techniques. This section delves into the latest innovations in computer vision, focusing on transfer learning, facial recognition, and image generation. We will explore the benefits of pre-trained models on performance, the principles of facial recognition technology, and the way AI creates images through deep learning.

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Computer Vision Essentials with Python

Course for Intermediate Level

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