'Median filtering' diaporamas de présentation

Enhancing Images Ch 5:Shapiro, Ch 3:Gonzales

Enhancing Images Ch 5:Shapiro, Ch 3:Gonzales. Gray level Mapping. Brightness Transform: 1. Position Dependent f(i,j)= g(i,j). e(i,j) g:Clean image e:position dependent noise 2. Position independent. 2. Position Independent Gray Level Mapping s =T( r ). Negation.

1.26k views • 112 slides

Digital Image Processing

??????????. Digital Image Processing. Image Restoration (Chapter 5). H.R. Pourreza. Image Restoration. Image restoration vs. image enhancement Enhancement: largely a subjective process Priori knowledge about the degradation is not a must (sometimes no degradation is involved)

2.06k views • 76 slides

Lecture 2: Edge detection and resampling

CS6670: Computer Vision. Noah Snavely. Lecture 2: Edge detection and resampling. From Sandlot Science. Administrivia. New room starting Thursday: HLS B11. Administrivia. Assignment 1 (feature detection and matching) will be out Thursday Turning via CMS: https://cms.csuglab.cornell.edu/

665 views • 66 slides

Linear Filtering

Linear Filtering. CS 678 Spring 2018. Outline. Linear filtering Box filter vs. Gaussian filter Median filter Gaussian and Laplacian pyramids Sharpening images. Some slides from Lazebnik. Motivation: Noise reduction. Given a camera and a still scene, how can you reduce noise?.

644 views • 63 slides

Applications of Image Filters

02/04/10. Applications of Image Filters. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Review: Image filtering. 1. 1. 1. 1. 1. 1. 1. 1. 1. Credit: S. Seitz. Image filtering. 1. 1. 1. 1. 1. 1. 1. 1. 1. Credit: S. Seitz. Image filtering. 1. 1. 1.

787 views • 59 slides

Applications of Image Filters

02/04/10. Applications of Image Filters. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Review: Image filtering. 1. 1. 1. 1. 1. 1. 1. 1. 1. Credit: S. Seitz. Image filtering. 1. 1. 1. 1. 1. 1. 1. 1. 1. Credit: S. Seitz. Image filtering. 1. 1. 1.

611 views • 59 slides

EEM 561 Machine Vision Lecture 5 Linear Filtering

463 views • 46 slides

Noise in Imaging Technology

Noise in Imaging Technology. Two plagues in image acquisition Noise interference Blur (motion, out-of-focus, hazy weather) Difficult to obtain high-quality images as imaging goes Beyond visible spectrum Micro-scale (microscopic imaging) Macro-scale (astronomical imaging). What is Noise?.

804 views • 43 slides

Image Averaging Example

Image Averaging Example. Image Averaging Example. Spatial Masks. Examples of Spatial Low-pass Masks (Local Averaging). Response of 1-D and 2-D Local Averaging Masks. Other Low-pass Masks (Weighted Filters). Example: Local Averaging. Median Filtering. Smoothing-Median: Comparison.

355 views • 34 slides

Signature Recognition Using Neural Networks and Rule Based Decision Systems

Signature Recognition Using Neural Networks and Rule Based Decision Systems. CSC 8810 Computational Intelligence Instructor Dr. Yanqing Zhang Presented By Mateena Syeda Shilpa Panaganti. Outline. Introduction Signature Recognition System Design/Architecture Implementation

388 views • 34 slides

MSU CSE 803

Linear Operations Using Masks. Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute some result at that pixel. MSU CSE 803. Expressing linear operations on neighborhoods. MSU CSE 803. Images as functions. MSU CSE 803. Neighborhood operations.

485 views • 32 slides

240-373 Image Processing

240-373 Image Processing. Montri Karnjanadecha montri@coe.psu.ac.th http://fivedots.coe.psu.ac.th/~montri. Chapter 5. Image Filtering. Filter. Image Filtering. Filtering can be use to enhance some features and de-enhance others. Usually used as a pre-processing step.

312 views • 31 slides

Lecture 2: Convolution and edge detection

CS4670: Computer Vision. Noah Snavely. Lecture 2: Convolution and edge detection. From Sandlot Science. Gaussian Kernel. Source: C. Rasmussen. Mean vs. Gaussian filtering. Gaussian filters. = 1 pixel. = 10 pixels. = 30 pixels. = 5 pixels. Gaussian filter.

1.06k views • 29 slides

Rank filtering

953 views • 29 slides

Chapter 9

Chapter 9. Spatial Filters Blurring Median Filtering Hi-pass filtering SpatialFilterFrame and JAI. Blurring=Low-pass Filters. Why Blur an Image?. Remove Noise Other ideas?. Average=low-pass. Want Unity Gain. Many variations on LP filters. Gaussian Blur. Why use Gaussian Blur?.

395 views • 27 slides

Noise and Filtering

Basis beeldverwerking ( 8D040) d r. Andrea Fuster Prof.dr . Bart ter Haar Romeny dr. Anna Vilanova Prof.dr . Marcel Breeuwer. Noise and Filtering. Contents. Noise Mean Filters Order-statistic filters Median Alpha-trimmed. Gaussian Noise.

402 views • 25 slides

APECE-505 Intelligent System Engineering Low-level Processing Md. Atiqur Rahman Ahad

337 views • 23 slides

Median Filtering Detection Using Edge Based Prediction Matrix

Median Filtering Detection Using Edge Based Prediction Matrix. The 10th IWDW, Atlantic City, New Jersey, USA 23~26 October 2011. Chenglong Chen, Jiangqun Ni. School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, P.R. China. 1. Outline.

356 views • 22 slides

9. Spatial Filters

9. Spatial Filters. Spatial filter operating over a local neighborhood about a pixel convolution is a kind of spatial filters convolution: linear filter non-linear filter ? ???. 9.1 Blurring. Blurring neighborhood averaging low-pass filtering (smoothing filter)

317 views • 17 slides

IIR Median Hybrid Filter

IIR Median Hybrid Filter. Kristina Harrell. Purpose. Remove impulse and Gaussian noise while preserving signal edges. Utilize IIR, Low pass, and Standard Median (SM) filtering techniques. Median Filtering. LP IIR Filter. X(n). y 1 (n). x(n). Median.

429 views • 8 slides