Computer Vision and Image Processing Overview

This content provides a comprehensive overview of topics covered in a Computer Vision and Image Processing course. It includes details on linear algebra foundations, photometry and color, linear filters, image features and textures, stereopsis, motion estimation, clustering, and segmentation. The material also covers midterm logistics and review for the upcoming exam. Each section offers insights into key concepts and methodologies essential for understanding computer vision and image processing techniques.

• Computer Vision
• Image Processing
• Linear Algebra
• Stereopsis
• Motion Estimation

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Presentation Transcript

1. CSE 473/573 Computer Vision and Image Processing (CVIP) Ifeoma Nwogu Lecture 35 Review for midterm

2. Schedule Last class Overview of convolution neural networks Today Midterm review Readings for today: None 2 11/17/2014

3. Midterm logistics In class; 45 minutes for 20-25 questions Similar in difficulty level to the quizzes Will cover topics we did in class; programming assignments are also fair game Close book exam 1-- sided cheat sheet notes allowed on a standard 8.5x11 paper 3 11/17/2014

4. Linear algebra foundations Review quiz 0 to get a sense of the LA questions Basic linear algebra definitions Vector and matrix operations Principal component analysis (PCA), eigenvalues and eigenvectors RANSAC 4 11/17/2014

5. Photometry and color Reflection at surfaces Lambertian + specular model Shape from shading Color representation (linear and nonlinear color spaces) No questions on Human color perception Physics of color 5 11/17/2014

6. Linear filters Fundamentals of filtering Convolution and correlation Gradients Edge detectors Pyramids 6 11/17/2014

7. Image features and textures Harris corner detector Blob detector Descriptors Histogram of gradients SIFT Texture extraction 7 11/17/2014

8. Stereopsis Correspondence problem (disparity) Epipolar geometry Image rectification Depth estimation Homography Fundamental and essential matrices Know when to use which 8 11/17/2014

9. Motion estimation Estimating optical flow Lucas-Kanade flow equations Motion-based feature tracking 9 11/17/2014

10. Clustering and Segmentation K-means clustering Mean-shift algorithm Features for segmentation 10 11/17/2014

11. Object detection and recognition Detection via classification Person detection Bag-of-words representation Object detection evaluation 11 11/17/2014

12. Probability concepts and classifiers Basic definitions Bayes rule Linear versus nonlinear classifiers 12 11/17/2014

13. Deep architectures Artificial Neural networks Perceptron Multi-layer networks and backpropagation Motivation for deep architectures Uses of CNN 13 11/17/2014

14. Questions