PPT-Lecture 1: Images and image filtering

CS5670 Intro to Computer Vision Noah Snavely Hybrid Images Oliva et al httpcvclmiteduhybridimagehtm Lecture 1 Images and image filtering Noah Snavely Hybrid Images Oliva et al . Convolution is a general purpos e filter effect for images Is a matrix applied to an image and a mathematical operation comprised of integers It works by determining the value of a central pixel by adding the weighted values of all its neighbors tog 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?. Stacy Morgan. LIS 600. UNC Greensboro. 23 October 2013. The Setting. How is internet used in the . school library?. How is internet used in the school library?. Today’s students are “digital natives”, born into a culture and lifestyle where technology immersion is the norm (. 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. Time . series: . smoothing, filtering, rejecting . outliers, . interpolation. moving average, splines, penalized splines, wavelets. autocorrelation in time series. variance increase, pattern generation;. Processing The PARIS File. Deuces Wild. FILTERING OPTIONS FOR YOUR PARIS FILE. Stephen Bach, New York State Office of Temporary and Disability Services, Bureau of Program Integrity. Mark Zaleha, Ohio Department of Job and Family Services, Bureau of Program Integrity. Positioning and the Box Model. When working on layouts for your HTML web pages, it is convenient to think about your content in the terms of being in boxes. . If you took a simple web page with 4 elements, and put a border around each element, this becomes easy to visualize.. 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 . 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. Minjie. Chen*, . Mantao. . Xu. and . Pasi. . Fränti. Speech and Image Processing Unit (SIPU). School of Computing. University of . Eastern Finland. , . FINLAND. Raster Map Images. Topographic or road maps. 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. 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.