PPT-Filtering Techniques and spatial filter types

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 . F01943024. Reference. Yang, . Qingxiong. . "Recursive bilateral filtering." . ECCV . 2012. .. Deriche. , . Rachid. . "Recursively . implementating. the Gaussian and its derivatives." . ICIP 1993.. 2. Overview of Filtering. Convolution. Gaussian filtering. Median filtering. Overview of Filtering. Convolution. Gaussian filtering. Median filtering. Motivation: Noise reduction. Given a camera and a still scene, how can you reduce noise?. Clutter Filtering Challenges. Conventional ground clutter filters discriminate between clutter and weather based on radial velocity.. When clutter and weather have sufficiently different velocities, they work very well.. A . Deep Dive into Spatial Indexing. Michael Rys. Principal Program Manager. Microsoft Corporation. @. SQLServerMike. DBI405. Q: Why is my Query . so. Slow?. A: . Usually . because the index isn’t being used.. 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. Subvert Your Spam Filter. Blaine Nelson / Marco . Barreno. / . fuching. . Jack Chi / Anthony D. Joseph. Benjamin I. P. Rubinstein / . Udam. . Saini. / Charles Sutton / J.D. . Tygar. / Kai Xia. University of California, Berkeley. Motivation: Image . denoising. How can we reduce noise in a photograph?. Let’s 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 . 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. Winter 2019, University of Michigan. http://web.eecs.umich.edu/~fouhey/teaching/EECS442_W19/. Note: I’ll ask the front row on the right to participate in a demo. All you have to do is say a number that I’ll give to you. If you don’t want to, it’s fine, but don’t sit in the front. . http://users.cecs.anu.edu.au/~. yili/CVinNutshell.htm. Outline. Paper discussion. Image Processing (overview). Diffusion Process. Image Processing (filtering). Filter Banks. Image Processing (filtering). 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. Images. x. y. f(x, y). row. column. Sample. Quantize. 2. Monochromatic Digital Image. x. y. Gray. Level. f(x,y). 3. Color Images. R. B. G. W. M. C. Y. Mixtures of Light: Additive Primaries. 4. R+G+B. 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. . @. sqlgene. www.sqlgene.com/powerbi/. emeidinger@all-lines-tech.com. About me. Business Intelligence developer. Worked for All-Lines for 5 years. Spoken at Pittsburgh SQL User Group and various SQL Saturdays.