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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