'Grey level' presentation slideshows

The Course

1.01k views • 77 slides

Histograms

Histograms. Histogram Equalization. Properties of histograms. Integrated optical density Mean grey level. Image statistics. IOD. MEAN ? = VARIANCE ? 2 = STANDARD DEVIATION ? =. Mean Grey Level = MGL. Definition: What is a histogram?.

1.09k views • 76 slides

HISTOGRAM TRANSFORMATION IN IMAGE PROCESSING

HISTOGRAM TRANSFORMATION IN IMAGE PROCESSING. SHINTA P TEKNIK INFORMATIKA STMIK MDP 2013. Contents. Histogram Histogram transformation Histogram equalization Contrast streching Applications. Histogram. The (intensity or brightness) histogram shows how many

1.02k views • 49 slides

HISTOGRAM TRANSFORMATION IN IMAGE PROCESSING

HISTOGRAM TRANSFORMATION IN IMAGE PROCESSING. SHINTA P TEKNIK INFORMATIKA STMIK MDP 2008. Contents. Histogram Histogram transformation Histogram equalization Contrast streching Applications. Histogram. The (intensity or brightness) histogram shows how many

1.08k views • 49 slides

Programming for Artists

ART 315 Dr. J. R. Parker Art/Digital Media Lab Lec 11 Fall 2010. Programming for Artists. Processing - Loops. We know about variables and some kinds of expressions. We know about conditions/flow of control (IF-THEN-ELSE)

512 views • 44 slides

Digital Elevation Models (DEMs)

Analysis of Surface Textures of Physiographic Features Extracted from Digital Elevation Models via Grey Level Co-occurrence Matrix: A Multiscale Approach. Digital Elevation Models (DEMs). The Global Digital Elevation Model (GTOPO30) of Great Basin.

547 views • 35 slides

Pattern Recognition with N-Tuple Systems

Pattern Recognition with N-Tuple Systems. Simon Lucas Computer Science Dept Essex University. Overview. Standard Binary n-tuple Dealing with grey-levels Continuous n-tuple Bit-plane decomposition Dealing with sequences Scanning N-Tuple Future Directions. N-Tuple Systems.

504 views • 34 slides

Something about boxes...

436 views • 31 slides

Digital image processing Chapter 6. Image enhancement

Digital image processing Chapter 6. Image enhancement. IMAGE ENHANCEMENT Introduction Image enhancement algorithms & techniques Point-wise operations Contrast enhancement; contrast stretching Grey scale clipping; image binarization (thresholding)

820 views • 29 slides

Hongtao Du

410 views • 27 slides

Multimedia Communications EG 371 and EG 348

Multimedia Communications EG 371 and EG 348. Dr Matthew Roach dr.matt.roach@iee.org Lecture 4 Fundamentals of compression. Speech, Image & Multimedia communications. Dr Matt Roach Lecture 4 History of video formats. Chromatic images. Colour Represented by vector not scalar

463 views • 27 slides

Chapter 4: Image Statistics and Histograms

Chapter 4: Image Statistics and Histograms. Univariate Statistics - used to describe and assess image quality of one band of imagery Multivariate Statistics - used to describe and assess the relationship between two or more bands of data. Image Statistics Brightness Contrast

289 views • 27 slides

Computer Vision

Computer Vision. Hough Transform, PDM, ASM and AAM David Pycock D.Pycock@bham.ac.uk www.bham.ac.uk/pycockd. Image Edge detected image. Hough Transform. To detect lines, circles, ellipses and other forms of curve

272 views • 26 slides

Estimating Pedestrian Density in Crowded Conditions

328 views • 23 slides

Grey Level Enhancement

Grey Level Enhancement. Contrast stretching Linear mapping Non-linear mapping Efficient implementation of mapping algorithms Design of classes to support gray level mapping. Image Histogram. Brightness histogram provides the frequency of the brightness value in the image.

232 views • 23 slides

Image Processing Fundamentals

550 views • 20 slides

Texture Detection & Texture related clustering

306 views • 17 slides