Digital Filters. What are they?  Local operation (neighborhood operation in GIS terminology) by mask, window, or kernel (different words for the same.

Slides:

Advertisements

Similar presentations

Environmental Remote Sensing GEOG 2021

Advertisements

3-D Computer Vision CSc83020 / Ioannis Stamos  Revisit filtering (Gaussian and Median)  Introduction to edge detection 3-D Computater Vision CSc

Linear Filtering – Part I Selim Aksoy Department of Computer Engineering Bilkent University

Spatial Filtering (Chapter 3)

Topic 6 - Image Filtering - I DIGITAL IMAGE PROCESSING Course 3624 Department of Physics and Astronomy Professor Bob Warwick.

Spatial Filtering.

Image enhancement and sharpening

Image Filtering. Outline Outline Concept of image filter  Focus on spatial image filter Various types of image filter  Smoothing, noise reductions 

Image processing (spatial &frequency domain) Image processing (spatial &frequency domain) College of Science Computer Science Department

Digital Image Processing

Image Enhancement in the Spatial Domain II Jen-Chang Liu, 2006.

Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli

6/9/2015Digital Image Processing1. 2 Example Histogram.

Low Pass Filtering Spatial frequency is a measure of how rapidly brightness or colour varies as we traverse an image. Figure 7.11a shows that an image.

Digital Image Processing

1 Image Filtering Readings: Ch 5: 5.4, 5.5, 5.6,5.7.3, 5.8 (This lecture does not follow the book.) Images by Pawan SinhaPawan Sinha formal terminology.

CS443: Digital Imaging and Multimedia Filters Spring 2008 Ahmed Elgammal Dept. of Computer Science Rutgers University Spring 2008 Ahmed Elgammal Dept.

Image Enhancement.

Image Analysis Preprocessing Arithmetic and Logic Operations Spatial Filters Image Quantization.

Gholamreza Anbarjafari, PhD Video Lecturers on Digital Image Processing Digital Image Processing Spatial Domain Filtering: Part II.

ECE 472/572 - Digital Image Processing Lecture 4 - Image Enhancement - Spatial Filter 09/06/11.

Chapter 3 (cont).  In this section several basic concepts are introduced underlying the use of spatial filters for image processing.  Mainly spatial.

Radiometric Correction and Image Enhancement

Presentation Image Filters

Neighborhood Operations

Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5: ;

Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.

Spatial Filtering: Basics

Linear Filtering – Part I Selim Aksoy Department of Computer Engineering Bilkent University

Digital Image Processing Lecture 5: Neighborhood Processing: Spatial Filtering Prof. Charlene Tsai.

Lecture 03 Area Based Image Processing Lecture 03 Area Based Image Processing Mata kuliah: T Computer Vision Tahun: 2010.

Image Processing is replacing Original Pixels by new Pixels using a Transform rst uvw xyz Origin x y Image f (x, y) e processed = v *e + r *a + s *b +

Digital Image Processing CSC331 Image Enhancement 1.

Image processing Fourth lecture Image Restoration Image Restoration: Image restoration methods are used to improve the appearance of an image.

School of Computer Science Queen’s University Belfast Practical TULIP lecture next Tues 12th Feb. Wed 13th Feb 11-1 am. Thurs 14th Feb am. Practical.

Convolution and Filtering

Chapter 5: Neighborhood Processing

Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.

Spatial Filtering.

Image Subtraction Mask mode radiography h(x,y) is the mask.

COMP322/S2000/L171 Robot Vision System Major Phases in Robot Vision Systems: A. Data (image) acquisition –Illumination, i.e. lighting consideration –Lenses,

Sharpening Filters To highlight fine detail or to enhance blurred detail. –smoothing ~ integration –sharpening ~ differentiation Categories of sharpening.

Intelligent Vision Systems ENT 496 Image Filtering and Enhancement Hema C.R. Lecture 4.

Digital Image Processing Lecture 5: Neighborhood Processing: Spatial Filtering March 9, 2004 Prof. Charlene Tsai.

Digital Image Processing Part 3 Spatial Domain Processing.

Gholamreza Anbarjafari, PhD Video Lecturers on Digital Image Processing Digital Image Processing Spatial Domain Filtering: Part I.

Image Enhancement by Spatial Domain Filtering

Image Filtering with GLSL DI1.03 蔡依儒. Outline Convolution Convolution Convolution implementation using GLSL Convolution implementation using GLSL Commonly.

Sharpening Spatial Filters ( high pass)  Previously we have looked at smoothing filters which remove fine detail  Sharpening spatial filters seek to.

Digital Image Processing Week V Thurdsak LEAUHATONG.

Filters– Chapter 6. Filter Difference between a Filter and a Point Operation is that a Filter utilizes a neighborhood of pixels from the input image to.

Image Enhancement Band Ratio Linear Contrast Enhancement

Spatial Filtering (Chapter 3) CS474/674 - Prof. Bebis.

Environmental Remote Sensing GEOG 2021

Spatial Image Enhancement

Image Subtraction Mask mode radiography h(x,y) is the mask.

Fundamentals of Spatial Filtering:

ECE 692 – Advanced Topics in Computer Vision

Practical TULIP lecture next Tues 12th Feb. Wed 13th Feb 11-1 am.

Filtering – Part I Gokberk Cinbis Department of Computer Engineering

Spatial Filtering - Enhancement

Digital Image Processing

Fundamentals of Spatial Filtering

9th Lecture - Image Filters

Spatial operations and transformations

Digital Image Processing Week IV

Department of Computer Engineering

Digital Filters.

Spatial operations and transformations

DIGITAL IMAGE PROCESSING Elective 3 (5th Sem.)

Presentation transcript:

Digital Filters

What are they?  Local operation (neighborhood operation in GIS terminology) by mask, window, or kernel (different words for the same thing).  Also called “convolution”!

Purposes (1)  Image improvement or restoration  Elimination of problems with lines and/or points (ie. filling in gaps)  Noise suppression  Image enhancement (sharpening)  Edge detection of linear structures.

Purposes (2)  Preprocessing before classification  Averaging of field units  Elimination of local disturbances.  Discover spatial patterns (enhancement)  Better distinguish area, line, and point objects.

Overall….  Trying to enhance low frequency detail (averaging), high frequency detail (sharpening), and edges (areas of rapid change).  All about, as always, reflectance values.

 The size of the neighborhood convolution mask or kernel (n) is usually 3 x 3, 5 x 5, 7 x 7, or 9 x 9.  We will constrain our discussion to 3 x 3 convolution masks with nine coefficients, c i, defined at the following locations:  c 1 c 2 c 3  Mask template = c 4 c 5 c 6  c 7 c 8 c 9

 The coefficients, c 1, in the mask are multiplied by the following individual brightness values (BV i ) in the input image – and then divided by 9 (thereby creating some sort of an average):  c 1 x BV 1 c 2 x BV 2 c 3 x BV 3  Mask template = c 4 x BV 4 c 5 x BV 5 c 6 x BV 6 c 7 x BV 7 c 8 x BV 8 c 9 x BV 9

 Low frequency (pass) filter: block the high spatial frequency detail, left the low-frequency  A kernel with small positive values with the same or a little large central value  High frequency (pass) filter: remove the slowly varying components and enhance the high- frequency local variations.  a kernel with a high central value, typically surrounded by negative weights abc d e

 Linear edge enhancement  Directional  Laplacian  Highlight points, lines, edges, suppress uniform and smooth regions Vertical edgesHorizontal edgeNW-SENE-SW directional Laplacian

Can also do, say, a median or modal (median below, 3x3 and 5x5) filter

Another median filter example.

Review: Properties of filters  Low pass: averaging small fluctuations in image values. Random noise suppression. Smoothing. However, image blurs.  High pass: enhancing details (and noise). Emphasizes edges.