Certified Artificial Intelligence Professional

Overview of Computer Vision: Introduction to the field and real-world applications (e.g., autonomous vehicles, facial recognition).

Basic Image Representation: Understanding pixels, image channels (RGB, grayscale), and resolution.

Image Acquisition and File Formats: Introduction to various image and video file formats and acquisition techniques.

Image Preprocessing Techniques: Techniques like resizing, cropping, flipping, and rotating for data augmentation.

Image Enhancement: Techniques like contrast adjustment, histogram equalization, and filtering.

Introduction to OpenCV: Installation and basic functions in Python for image manipulation and processing.

Edge Detection and Gradient Filters: Sobel, Prewitt, and Canny edge detection.

Smoothing and Blurring Techniques: Gaussian blur, median filtering, and bilateral filters.

Thresholding and Contour Detection: Adaptive thresholding and detecting contours in images.

Morphological Transformations: Erosion, dilation, opening, and closing for image structure refinement.

Feature Extraction: Harris corner detection, Shi-Tomasi, and Scale-Invariant Feature Transform (SIFT).

Region of Interest (ROI) and Masking: Identifying regions within an image for further analysis.

Introduction to Machine Learning for Vision: Overview of supervised, unsupervised, and deep learning approaches.

Feature Representation and Descriptors: Keypoint detection and descriptor techniques (SIFT, ORB).

Image Classification: Basic approaches using Support Vector Machines (SVM) and k-NN.

Introduction to Neural Networks: Fundamentals of artificial neural networks and how they apply to vision tasks.

Convolutional Neural Networks (CNNs) Basics: Understanding how CNNs are designed and function for image classification.

Image Augmentation in Machine Learning: Techniques to enhance datasets for training robust models.

Advanced CNN Architectures: VGG, ResNet, and Inception architecture deep dives.

Transfer Learning in Vision Tasks: How to leverage pre-trained models for specialized tasks.

Object Detection: Techniques like YOLO (You Only Look Once), SSD (Single Shot Detection), and Fast R-CNN.

Image Segmentation: Semantic segmentation using Fully Convolutional Networks (FCNs) and U-Net architecture.

Autoencoders and GANs in Vision: Image generation and reconstruction using autoencoders and Generative Adversarial Networks.

Model Evaluation and Metrics: Precision, recall, F1 score, IoU (Intersection over Union) in vision tasks.

Facial Recognition Systems: Techniques for face detection, recognition, and emotion analysis.

Optical Flow and Motion Analysis: Tracking movement in videos using algorithms like Lucas-Kanade.

Video Processing and Action Recognition: Techniques for handling video streams and recognizing actions.

3D Vision and Depth Perception: Depth estimation using stereo vision and applications in augmented reality.

Text Detection and Recognition (OCR): Optical Character Recognition (OCR) using deep learning techniques.

Pose Estimation: Human pose estimation with techniques like OpenPose.

Project 1: Image Classification with CNN: Building a CNN model for classifying images in a custom dataset.

Project 2: Object Detection Model with YOLO: Developing and training an object detection model using YOLO architecture.

Project 3: Image Segmentation with U-Net: Implementing U-Net for semantic segmentation of medical or satellite images.

Model Deployment: Techniques for deploying models in real-world applications (e.g., Flask, TensorFlow Serving).

Optimization and Model Compression: Techniques to optimize models for deployment, including pruning and quantization.

Capstone Project Presentation: Final project involving a real-world computer vision challenge, presentation, and feedback.