Computer Vision for Image Analysis Training in San Francisco, USA

This training on Computer Vision for Image Analysis focuses on teaching participants how to process, analyze, and interpret images using modern computer vision techniques. The course will cover foundational concepts of computer vision, key algorithms for image processing, and the use of machine learning models to solve real-world image analysis tasks. Participants will learn to work with popular tools and libraries such as OpenCV, TensorFlow, and Keras to implement image analysis workflows, including object detection, image classification, and segmentation.

Introduction to Computer Vision & Image Processing

• Session 1: Introduction to Computer Vision

  • What is computer vision?
  • Applications of computer vision in various industries (e.g., healthcare, automotive, security).
  • Overview of key tasks in image analysis: Image classification, object detection, segmentation.

• Session 2: Image Preprocessing and Basic Image Processing Techniques

  • Image fundamentals: Pixel, resolution, color spaces (RGB, Grayscale).
  • Basic image manipulations: Cropping, rotating, resizing, and flipping images.
  • Image filters: Gaussian blur, edge detection (Canny, Sobel).

• Session 3: Image Classification with Convolutional Neural Networks (CNNs)

  • Introduction to CNNs: Convolution, pooling, fully connected layers.
  • Image classification techniques using CNNs.

• Session 4: Object Detection Techniques

  • Overview of object detection and its real-world applications.
  • Haar Cascade classifiers for face detection.
  • Introduction to YOLO (You Only Look Once) for real-time object detection.

Advanced Image Processing, Segmentation, and Data Augmentation

• Session 5: Advanced Image Processing Techniques

  • Morphological transformations: Dilation, erosion, opening, and closing.
  • Histogram equalization and contrast adjustments.
  • Edge detection and contour detection in images.

• Session 6: Image Segmentation

  • What is image segmentation?
  • Types of segmentation: Semantic segmentation vs. instance segmentation.
  • U-Net architecture for semantic segmentation.

• Session 7: Image Augmentation for Improved Model Performance

  • What is data augmentation and why is it important?
  • Techniques for augmenting images: Rotation, flipping, cropping, brightness adjustment, and noise injection.

• Session 9: Deep Learning and Transfer Learning in Image Analysis

  • Introduction to transfer learning and using pre-trained models.
  • Fine-tuning pre-trained models like VGG16, ResNet, and InceptionV3.

• Session 10: Implementing Object Detection and Advanced Applications

  • Advanced object detection techniques: YOLOv4, Faster R-CNN.
  • Real-time object detection and tracking.

• Session 11: Real-World Applications and Use Cases

  • Applying computer vision techniques to real-world datasets.
  • Overview of project work: Image analysis for various domains (e.g., medical imaging, security surveillance, autonomous vehicles).
  • Hands-on

• Session 12: Final Wrap-up and Q&A

  • Hands-on
  • Q&A session to discuss advanced techniques and explore further resources.

This training provides participants with an in-depth understanding of computer vision and image analysis techniques. By combining theoretical insights with hands-on practice, participants will gain practical experience in working with advanced computer vision tools and algorithms, empowering them to tackle real-world image analysis challenges.