Advanced Computer Vision

This course is designed to provide an in-depth understanding of advanced computer vision techniques and their applications in the Finance & Insurance industries. Participants will learn cutting-edge methods for object detection, semantic segmentation, and 3D reconstruction, enabling them to develop and deploy sophisticated computer vision solutions for various tasks relevant to finance and insurance, such as document analysis, fraud detection, and risk assessment.

• Understand the principles and techniques behind advanced computer vision methods

• Implement and train state-of-the-art object detection models, such as Faster R-CNN, YOLO, and SSD, for financial document analysis

• Apply semantic segmentation algorithms, such as U-Net, DeepLab, and Mask R-CNN, for pixel-wise classification in financial and insurance documents

• Reconstruct 3D models from 2D images using structure from motion (SfM) and multi-view stereo (MVS) techniques for risk assessment and claims processing

• Develop and deploy advanced computer vision solutions for finance and insurance applications, such as fraud detection and automated underwriting

1. Object Detection in Financial Documents

• Overview of object detection and its applications in the Finance & Insurance industries

• Two-stage object detectors: R-CNN, Fast R-CNN, and Faster R-CNN

• Single-stage object detectors: YOLO, SSD, and RetinaNet

• Hands-on exercises: Implementing and training object detection models on financial document datasets

2. Semantic Segmentation for Document Analysis

• Introduction to semantic segmentation and its importance in finance and insurance

• Fully Convolutional Networks (FCNs) for semantic segmentation

• Encoder-decoder architectures: U-Net, SegNet, and DeepLab

• Instance segmentation with Mask R-CNN for extracting key information from financial documents

• Hands-on exercises: Applying semantic segmentation models to financial and insurance documents

3. 3D Reconstruction and Visualization

• Principles of 3D reconstruction from 2D images

• Structure from Motion (SfM) and feature matching techniques

• Multi-View Stereo (MVS) and dense reconstruction methods

• Point cloud processing and mesh generation for risk assessment and claims processing

• Hands-on exercises: Reconstructing 3D models of insured assets and accident scenes

4. Advanced Topics and Applications

• Attention mechanisms and transformer-based models for document understanding

• Domain adaptation and transfer learning for cross-domain image analysis

• Unsupervised and self-supervised learning for representation learning in finance and insurance data

• Case studies of advanced computer vision in finance and insurance (e.g., automated underwriting, fraud detection)

• Hands-on exercises: Developing an advanced computer vision solution for a specific finance or insurance use case

5. Deployment and Optimization

• Deploying computer vision models in production environments

• Optimizing models for real-time inference and edge deployment

• Monitoring and updating deployed models for continuous improvement

• Best practices for data management and version control in computer vision projects

• Hands-on exercises: Deploying an optimized computer vision model using a cloud platform (e.g., AWS, GCP)

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic computer vision concepts and techniques (e.g., image processing, feature extraction)

• Knowledge of convolutional neural networks (CNNs) and their applications