PPT-CS654: Digital Image Analysis

Lecture 21 Image Restoration Recap of Phase 1 Image acquisition digitization Geometric Transformations Interpolation techniques Image Transforms spatial to frequency domain Image Enhancement spatial and frequency domain. Lecture 30: Clustering based Segmentation. Slides are . adapted from: http://www.wisdom.weizmann.ac.il/~vision/. Recap of Lecture 26. Thresholding. Otsu’s method. Region based segmentation. Region growing, split-merge, quad-tree. Lecture 20: Image Enhancement in Frequency Domain. Recap of Lecture 19. Spatial filtering. Mean Filter. Non-Local Mean Filter. Median Filter. Unsharp. Masking. Adaptive . Unsharp. Masking. Outline of Lecture 20. Lecture 7. : Camera Model and Imaging . Geometry. Recap of lecture 6. Basic mathematical transformation. Translation, Rotation, Scaling. Transformation about origin and arbitrary point. Transformation in 2D and 3D. Lecture 6. : Basic Transformations. Recap of Lecture 5. Different distance measures. D4, D8,Dm, Euclidean. Application of distance transform. Shape matching. Arithmetic and logical operations on images. Lecture 28: Advanced topics in Image Segmentation. Image courtesy: IEEE, IJCV. Recap of Lecture 27. Clustering based Image segmentation. Mean Shift. Kernel density estimation. Application of Mean shift: Filtering, Clustering, Segmentation. Lecture 32: Image Morphology: Open, Closing and Transforms. Recap of Lecture 31. Image morphology. . Set operation on images. . Dilation – translation, union. . Erosion – translation, intersection. Lecture 8: . Stereo . Imaging. Recap of Lecture 7. Inverse perspective transformation and its issues. Many to one mapping. Generalized perspective transformation. Fundamentals of camera calibration. Outline of Lecture 8. Lecture 12: Separable Transforms. Recap of Lecture 11. Image Transforms. Source and target domain. Unitary transform, 1-D. Unitary transform, . 2-D. High computational complexity. Outline of Lecture 12. Processing & Machine Vision. By:. Dr. Rajeev Srivastava. Associate Professor, CSE, IIT(BHU), Varanasi. Fundamentals of . Digital Image Processing. • Applications of image processing. • What's an image?. Lecture 5. : Pixels Relationships. Recap of Lecture 4. Different pixel relationships. Neighbourhood. Connectivity. Adjacency. Path. Connected component labelling. Outline. Different distance measures. Lecture 11: . Image . Transforms. Recap of Lecture 10. B-Spline curve. Constant, Linear, Quadratic, Cubic interpolation. Nearest neighbour, Bi-Linear, Bi-Cubic interpolation. Outline of Lecture 11. Image transforms. Monitor Bridge Behavior and Condition . Prepared by the University of New Hampshire . Department . of Civil Engineering for the . New . Hampshire Department of Transportation . How to you measure the structural health of a bridge?. , Marshall lab. Introduction to . Digital . Image . Analysis. Part I: Digital Images. Kurt Thorn. NIC. UCSF. What is a digital Image?. Many measurements of . light intensity. 0. 0. 0. 0. 0. 0. 0. 0. 0. Image Types. All images can be thought of as “color” images since even black and white are colors. More commonly we distinguish between three types of images:. Color: an image containing any color.