Computer Vision Basics

This course is designed to provide a solid foundation in computer vision techniques and their applications in the Finance & Insurance industries. Participants will learn the fundamental concepts and algorithms used in image processing, object detection, and image segmentation. The course covers various computer vision libraries and tools, enabling participants to develop practical skills in analyzing and interpreting visual data relevant to finance and insurance, such as financial documents, insurance claims, and satellite imagery for risk assessment.

• Understand the fundamental concepts and techniques in computer vision

• Implement basic image processing operations, such as filtering, edge detection, and morphological transformations

• Apply object detection and image segmentation algorithms to identify and localize objects of interest in financial and insurance documents

• Utilize computer vision libraries, such as OpenCV and scikit-image, for efficient image processing and analysis

• Develop computer vision applications for various Finance & Insurance use cases, such as document processing, fraud detection, and risk assessment

1. Introduction to Computer Vision

• Overview of computer vision and its applications in the Finance & Insurance industries

• Digital image fundamentals (pixels, color spaces, image formats)

• Image processing basics (reading, writing, and displaying images)

• Hands-on exercises: Basic image manipulation using Python and OpenCV

Week 2: Image Processing Techniques

• Image filtering (smoothing, sharpening, and noise reduction)

• Edge detection (Sobel, Canny, and Laplacian operators)

• Morphological transformations (erosion, dilation, opening, and closing)

• Hands-on exercises: Implementing image processing techniques on financial and insurance documents

2. Object Detection

• Template matching and its limitations

• Feature-based object detection (SIFT, SURF, and ORB)

• Cascade classifiers and Haar-like features for detecting objects in financial documents

• Deep learning-based object detection (YOLO, SSD, and Faster R-CNN) for identifying and localizing objects in satellite imagery

• Hands-on exercises: Detecting objects of interest in financial documents and satellite imagery for risk assessment

3. Image Segmentation

• Thresholding techniques (global, adaptive, and Otsu's method)

• Region-based segmentation (region growing and split-and-merge)

• Edge-based segmentation (watershed and graph-based methods)

• Semantic segmentation using deep learning (FCN, U-Net, and DeepLab) for segmenting financial documents and satellite imagery

• Hands-on exercises: Segmenting regions of interest in financial documents and satellite imagery

4. Applications and Advanced Topics

• Case studies of computer vision applications in the Finance & Insurance industries (e.g., document processing, fraud detection, risk assessment)

• Introduction to 3D computer vision and point cloud processing for insurance claim assessment

• Challenges and future directions in computer vision for finance and insurance

• Hands-on exercises: Developing a computer vision application for a specific Finance or Insurance use case

• Strong understanding of linear algebra and calculus

• Proficiency in programming with Python and libraries such as NumPy and Matplotlib

• Familiarity with basic machine learning concepts and techniques