Computer Vision Certification Training, Port Harcourt, Rivers State and Uyo, Akwa Ibom State
Computer Vision: A Beginner to Mastery Course
Overall Course Objective:
Module-Specific Learning Objectives:
Module 1: Introduction to Computer Vision
- Define computer vision and describe its scope and applications.
- Trace the history and evolution of computer vision.
- Identify key challenges and limitations in computer vision systems.
- Analyze real-world case studies of successful computer vision applications.
- Research and report on emerging trends in computer vision.
Module 2: Image Formation and Representation
- Explain the image acquisition process and its impact on image quality.
- Understand digital image fundamentals, including pixels, bit depth, and color models.
- Describe image sampling and quantization and their effects on image resolution.
- Analyze and interpret image histograms.
- Implement basic image manipulation techniques using OpenCV.
- Compare and contrast different image compression algorithms.
Module 3: Image Processing Techniques
- Apply image enhancement techniques to improve image quality.
- Implement noise reduction algorithms to remove noise from images.
- Detect edges and boundaries within images using various operators.
- Perform morphological operations to extract features from images.
- Develop an image processing pipeline for a given image dataset.
- Implement a simple image segmentation algorithm.
Module 4: Feature Extraction and Description
- Detect corners and interest points within images using various algorithms.
- Extract and describe features using techniques like SIFT, SURF, HOG, and LBP.
- Match features between two images.
- Implement a simple object recognition system based on feature-based matching.
Module 5: Introduction to Machine Learning for Computer Vision
- Understand the concepts of supervised, unsupervised, and reinforcement learning.
- Learn about common machine learning evaluation metrics.
- Apply cross-validation and hyperparameter tuning techniques.
- Implement a simple image classification model using Support Vector Machines (SVM).
- Analyze the impact of different hyperparameters on model performance.
Module 6: Machine Learning for Image Classification
- Apply the Histogram of Oriented Gradients (HOG) for object detection.
- Understand the Bag-of-Words (BoW) model for image representation.
- Implement and evaluate image classification models using SVM and K-Nearest Neighbors (KNN).
- Compare the performance of different classifiers on a given image dataset.
Module 7: Introduction to Deep Learning
- Understand the fundamental concepts of artificial neural networks.
- Explain the backpropagation algorithm and gradient descent optimization.
- Describe the architecture and operation of Convolutional Neural Networks (CNNs).
- Implement a simple feedforward neural network from scratch.
- Research and understand the historical development of deep learning.
Module 8: Convolutional Neural Networks (CNNs)
- Explore various CNN architectures (LeNet, AlexNet, VGG, ResNet, Inception).
- Apply transfer learning and fine-tuning techniques to pre-trained CNN models.
- Implement data augmentation techniques to improve model performance.
- Utilize different optimizers (Adam, SGD, RMSprop) for training CNNs.
- Train a CNN model to classify images from a standard dataset (e.g., CIFAR-10).
- Compare the performance of different CNN architectures on a given image classification task.
Module 9: Object Detection with Deep Learning
- Understand the concepts of region-based and single-shot object detectors.
- Explore popular object detection architectures (R-CNN, Faster R-CNN, Mask R-CNN, SSD, YOLO).
- Train an object detection model to detect objects in images or videos.
- Evaluate the performance of different object detection models on a challenging dataset.
Module 10: Image Segmentation with Deep Learning
- Differentiate between semantic and instance segmentation.
- Explore popular image segmentation architectures (U-Net, FCN, DeepLabv3).
- Train a U-Net model to segment objects in medical images.
- Implement a semantic segmentation model for a specific application.
Module 11: Generative Adversarial Networks (GANs)
- Understand the architecture and training process of GANs.
- Explore applications of GANs in computer vision (image generation, style transfer, super-resolution).
- Train a simple GAN to generate synthetic images.
- Experiment with different GAN architectures and evaluate their image generation quality.
Module 12: 3D Computer Vision
- Understand different depth estimation techniques (stereo vision, structured light, time-of-flight).
- Learn about Structure from Motion (SfM) and 3D object reconstruction.
- Process and analyze point cloud data.
- Implement a simple depth estimation algorithm using stereo images.
- Research and understand 3D object reconstruction techniques used in robotics.
Module 13: Video Analysis
- Understand video processing basics (frame extraction, video stabilization).
- Implement object tracking algorithms (Kalman filter, particle filter, deep learning-based trackers).
- Analyze temporal data and implement action recognition systems.
- Develop an action recognition system for a specific activity.
Module 14: Computer Vision with AWS
- Utilize AWS services for computer vision (Amazon Rekognition, Amazon SageMaker).
- Deploy computer vision models on the AWS platform.
- Optimize performance and cost of computer vision applications on AWS.
Module 15: Computer Vision with AWS (Continued)
- Build and deploy custom computer vision models on Amazon SageMaker.
- Integrate computer vision with other AWS services (e.g., S3, Lambda).
- Design and implement cost-effective solutions for processing large volumes of images on AWS.
Module 16: Deploying Computer Vision Systems
- Apply software development best practices for computer vision projects.
- Utilize containerization techniques (Docker) for model deployment.
- Explore model optimization and deployment strategies.
- Understand the concepts of edge computing and embedded systems.
- Containerize a computer vision model and deploy it to a local or cloud-based environment.
- Research and compare different deployment options for computer vision applications.
Module 17: Advanced Deep Learning Topics
- Understand attention mechanisms in computer vision.
- Explore Transformer models for computer vision (e.g., Vision Transformer).
- Learn about self-supervised learning in computer vision.
- Investigate meta-learning techniques for computer vision.
- Experiment with a Transformer-based model for image classification.
- Research and report on the latest advancements in self-supervised learning for computer vision.
Module 18: 3D Computer Vision (Continued)
- Utilize the Point Cloud Library (PCL) for point cloud processing.
- Implement 3D object detection and recognition algorithms.
- Understand and explore Simultaneous Localization and Mapping (SLAM) techniques.
- Process and visualize point cloud data using the PCL library.
- Research and implement a simple SLAM algorithm for robot navigation.
Module 19: Medical Imaging
- Understand medical image analysis techniques.
- Apply deep learning for medical image segmentation and classification.
- Explore applications of computer vision in disease diagnosis and treatment planning.
- Analyze and classify medical images using pre-trained models.
- Research and understand the ethical considerations of using AI in medical imaging.
Module 20: Autonomous Vehicles
- Understand the role of computer vision in self-driving cars.
- Implement object detection and tracking algorithms for autonomous driving.
- Explore path planning and decision-making algorithms.
- Simulate a simple autonomous vehicle navigation task using a computer vision-based approach.
- Research and understand the challenges and future directions of autonomous driving technology.
Module 21: Augmented Reality (AR) and Virtual Reality (VR)
- Understand computer vision techniques used in AR/VR applications.
- Implement object recognition and tracking in AR/VR environments.
- Explore 3D rendering and visualization techniques for AR/VR.
- Develop a simple AR application using computer vision libraries (e.g., ARKit, ARCore).
- Research and understand the potential of AR/VR technologies in various industries.
Module 22: Ethical Considerations in Computer Vision
- Identify and analyze bias and fairness issues in computer vision algorithms.
- Understand privacy and security concerns related to computer vision applications.
- Explore responsible AI development and deployment practices.
- Analyze legal and societal implications of computer vision applications.
- Analyze case studies of ethical challenges in specific computer vision applications.
- Develop a set of ethical guidelines for a computer vision project.
Module 23: Career Development
- Build a strong professional portfolio showcasing computer vision projects and skills.
- Prepare for job interviews in the computer vision field.
- Develop networking and professional development strategies.
- Research and identify job opportunities in the computer vision field.
- Write a cover letter and resume for a computer vision-related job.
Module 24: Certification Preparation
- Review key concepts and techniques covered throughout the course.
- Practice exam questions and participate in mock tests.
- Develop effective exam strategies and tips.
- Practice solving exam-style questions and analyze performance.
- Develop a comprehensive study plan for the Certified Computer Vision Expert and AWS Certified Machine Learning – Specialty certifications.
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