PPT-Lecture 2: Image filtering

What is an image A grid matrix of intensity values common to use one byte per value 0 black 255 white 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255. 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 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?. 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 . 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. 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. Shahar . Kovalsky. Alon. . Faktor. 17/4/2011. IR. Indoor – low light. US. Can we (humans) . denoise. ?. IR. Indoor – low light. US. Sources of Noise. 01010101010101010101010101010101010101010101010101. 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). 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.. 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. Atif. . Iqbal. . Thesis Overview. 2. Introduction. Motivation. Previous Works. Cascaded Filtering for . Palmprints. Cascaded Filtering . for Fingerprints. Summary and Conclusion. What is Biometrics?. Henning Lange, Mario . Bergés. , Zico Kolter. Variational Filtering. Statistical Inference. (Expectation Maximization, Variational Inference). Deep Learning. Dynamical Systems. Variational Filtering. An introduction. CS578-Digital speech signal processing. Invited lecture. On the (Glottal) Inverse Filtering of Speech Signals. Introduction. Inverse Filtering Techniques. Conclusions. Introduction. On the (Glottal) Inverse Filtering of Speech Signals. Matthew Heintzelman. EECS 800 SAR Study Project . ‹#›. . Background:. Typical SAR image formation . algorithms. produce relatively high sidelobes (fast-time and slow-time) that . contribute. to image speckle and can mask scatterers with a low RCS..