PPT-Linear filtering Motivation: Image

denoising How can we reduce noise in a photograph Lets replace each pixel with a weighted average of its neighborhood The weights are called the filter kernel What are the weights for the average of a . N is the process noise or disturbance at time are IID with 0 is independent of with 0 Linear Quadratic Stochastic Control 52 brPage 3br Control policies statefeedback control 0 N called the control policy at time roughly speaking we choo Deep Packet Inspection. Artyom. . Churilin. Tallinn University of Technology 2011. Web filtering & DPI. Web filtering (content control) . is a way control . what content is permitted to a . user. . Algorithms for Image Analysis. Image . Processing Basics. Lecture 3. Lena Gorelick, substituting for Yuri . Boykov. Acknowledgements: slides from Steven Seitz, . Aleosha. . Efros. , David Forsyth, and Gonzalez & Woods. 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. CS5670: Intro to Computer Vision. Noah Snavely. Hybrid Images, . Oliva. et al., . http://cvcl.mit.edu/hybridimage.htm. Lecture 1: Images and image filtering. Noah Snavely. Hybrid Images, . Oliva. et al., . Computational Photography. Derek Hoiem. 08/31/17. Graphic: . http://www.notcot.org/post/4068/. Administrative stuff. Any questions?. Tutorial. :. Looks like Sept . 6 . at . 5pm (lasting 1.5-2 . hrs. ), . Fouhey. .. Let’s Take An Image. Let’s Fix Things. Slide Credit: D. Lowe. We have noise in our image. Let’s replace each pixel with a . weighted. average of its neighborhood. Weights are . filter kernel. The relevant features for the examination task are enhanced. The irrelevant features for the examination task are removed/reduced. Here the input and output image are both digital image in color or gray scale.. Atif. . Iqbal. . Thesis Overview. 2. Introduction. Motivation. Previous Works. Cascaded Filtering for . Palmprints. Cascaded Filtering . for Fingerprints. Summary and Conclusion. What is Biometrics?. Motivation. . &. . Emotion. Stress. Sources. Measures. Theories. Effects. Coping. Motivation. Maslow’s Hierarchy of Needs. Drive Reduction Theory. Arousal Theory. Intrinsic/. Extrinsic Motivation. Outline. Recap. SVD . vs. PCA. Collaborative filtering. aka Social recommendation. k-NN CF methods. classification. CF via MF. MF . vs. SGD . vs. ….. Dimensionality Reduction. and Principle Components Analysis: Recap. 3. Filtering . Filtering image data. is a . standard process . used in almost all image processing systems. . Filters. are used to remove . noise. from digital image while keeping the details of image preserved. . Neighbourhood. Processing. Lecture 2(b). . Neighbourhood. Processing. We have seen . that . an image can be . modified . by applying a particular function to . each pixel value whereby this is known as point processing. . extrinsic motivation: a person performs an action because it leads to an outcome that is separate from or external to the person. intrinsic motivation: a person performs an action because the act is fun, challenging, or satisfying in an internal manner.