Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University

Slides:



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

Content-Based Image Retrieval Selim Aksoy Department of Computer Engineering Bilkent University
Binary Image Analysis Selim Aksoy Department of Computer Engineering Bilkent University
Department of Computer Engineering
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Linear Filtering – Part I Selim Aksoy Department of Computer Engineering Bilkent University
Binary Image Analysis Selim Aksoy Department of Computer Engineering Bilkent University
Content-Based Image Retrieval Selim Aksoy Department of Computer Engineering Bilkent University
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Edge Detection Selim Aksoy Department of Computer Engineering Bilkent University
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University
Edge Detection Selim Aksoy Department of Computer Engineering Bilkent University
CS Spring 2009 CS 414 – Multimedia Systems Design Lecture 4 – Digital Image Representation Klara Nahrstedt Spring 2009.
Binary Image Analysis Selim Aksoy Department of Computer Engineering Bilkent University
Digital Image Processing
Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University
Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University
Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University
Image Enhancement Christoph Lampert / Chris Wojtan Some slides adapted from Selim Aksoy, Bilkent University.
Color Image Processing
DIGITAL IMAGE PROCESSING CMSC 150: Lecture 14. Conventional Cameras  Entirely chemical and mechanical processes  Film: records a chemical record of.
Capturing and optimising digital images for research Gilles Couzin.
Digital Image Processing: Digital Imaging Fundamentals.
Digtial Image Processing, Spring ECES 682 Digital Image Processing Oleh Tretiak ECE Department Drexel University.
Digital Images The nature and acquisition of a digital image.
ECE 472/572 – Digital Image Processing Lecture 2 – Elements of Visual Perception and Image Formation 08/25/11.
CS Spring 2011 CS 414 – Multimedia Systems Design Lecture 4 – Visual Perception and Digital Image Representation Klara Nahrstedt Spring 2011.
The Digital Image.
Image processing Second lecture. Image Image Representation We have seen that the human visual system (HVS) receives an input image as a collection of.
Image Formation. Input - Digital Images Intensity Images – encoding of light intensity Range Images – encoding of shape and distance They are both a 2-D.
Lab #5-6 Follow-Up: More Python; Images Images ● A signal (e.g. sound, temperature infrared sensor reading) is a single (one- dimensional) quantity that.
Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University
© 1999 Rochester Institute of Technology Introduction to Digital Imaging.
CS 376b Introduction to Computer Vision 02 / 08 / 2008 Instructor: Michael Eckmann.
Introduction to Computer Vision Sebastian van Delden USC Upstate
Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.
Chapter 1. Introduction. Goals of Image Processing “One picture is worth more than a thousand words” 1.Improvement of pictorial information for human.
CIS 601 Image Fundamentals Longin Jan Latecki Slides by Dr. Rolf Lakaemper.
Computer Images Can store color info about each pixel, but makes file BIG Compression for Web 15.
Image Representation. Digital Cameras Scanned Film & Photographs Digitized TV Signals Computer Graphics Radar & Sonar Medical Imaging Devices (X-Ray,
Digital Image Processing (DIP) Lecture # 5 Dr. Abdul Basit Siddiqui Assistant Professor-FURC 1FURC-BCSE7.
Chapter 2: Digital Image Fundamentals Spring 2006, 劉震昌.
DIGITAL IMAGE. Basic Image Concepts An image is a spatial representation of an object An image can be thought of as a function with resulting values of.
Digital Image Processing In The Name Of God Digital Image Processing Lecture2: Digital Image Fundamental M. Ghelich Oghli By: M. Ghelich Oghli
University of Kurdistan Digital Image Processing (DIP) Lecturer: Kaveh Mollazade, Ph.D. Department of Biosystems Engineering, Faculty of Agriculture,
Visual Computing Computer Vision 2 INFO410 & INFO350 S2 2015
CS Spring 2012 CS 414 – Multimedia Systems Design Lecture 4 – Visual Perception and Digital Image Representation Klara Nahrstedt Spring 2012.
Introduction to Images & Graphics JMA260. Objectives Images introduction Photoshop.
Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.
Image File Formats By Dr. Rajeev Srivastava 1. Image File Formats Header and Image data. A typical image file format contains two fields namely Dr. Rajeev.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
CS Spring 2010 CS 414 – Multimedia Systems Design Lecture 4 – Audio and Digital Image Representation Klara Nahrstedt Spring 2010.
Digital Image Processing Image Enhancement in Spatial Domain
An Introduction to Digital Image Processing Dr.Amnach Khawne Department of Computer Engineering, KMITL.
CS Spring 2014 CS 414 – Multimedia Systems Design Lecture 4 – Visual Perception and Digital Image Representation Klara Nahrstedt Spring 2014.
COMP 9517 Computer Vision Digital Images 1/28/2018 COMP 9517 S2, 2009.
Sampling, Quantization, Color Models & Indexed Color
Color Image Processing
Color Image Processing
Digital Image Processing (DIP)
Color Image Processing
IT – 472 Digital Image Processing
Digital Image Fundamentals
Color Image Processing
CIS 595 Image Fundamentals
Color Image Processing
Digital Image Fundamentals
Digital Image Processing
Presentation transcript:

Digital Image Fundamentals Selim Aksoy Department of Computer Engineering Bilkent University

CS 484, Spring 2010©2010, Selim Aksoy2 Imaging process Light reaches surfaces in 3D. Surfaces reflect. Sensor element receives light energy. Intensity is important. Angles are important. Material is important. Adapted from Rick Szeliski

CS 484, Spring 2010©2010, Selim Aksoy3 Image acquisition Adapted from Gonzales and Woods

CS 484, Spring 2010©2010, Selim Aksoy4 Image acquisition Adapted from Rick Szeliski

CS 484, Spring 2010©2010, Selim Aksoy5 CCD cameras Lens collects light arrays. Array of small fixed elements replace chemicals of film. Number of elements (pixels) less than with film (so far). Adapted from Shapiro and Stockman

CS 484, Spring 2010©2010, Selim Aksoy6 Sampling and quantization

CS 484, Spring 2010©2010, Selim Aksoy7 Sampling and quantization

CS 484, Spring 2010©2010, Selim Aksoy8 Problems with arrays Blooming: difficult to insulate adjacent sensing elements. Charge often leaks from hot cells to neighbors, making bright regions larger. Adapted from Shapiro and Stockman

CS 484, Spring 2010©2010, Selim Aksoy9 Problems with arrays Clipping: dark grid intersections at left were actually brightest of scene. In A/D conversion the bright values were clipped to lower values. Adapted from Shapiro and Stockman

CS 484, Spring 2010©2010, Selim Aksoy10 Problems with lenses Adapted from Rick Szeliski

CS 484, Spring 2010©2010, Selim Aksoy11 Image representation Images can be represented by 2D functions of the form f(x,y). The physical meaning of the value of f at spatial coordinates (x,y) is determined by the source of the image. Adapted from Shapiro and Stockman

CS 484, Spring 2010©2010, Selim Aksoy12 Image representation In a digital image, both the coordinates and the image value become discrete quantities. Images can now be represented as 2D arrays (matrices) of integer values: I[i,j] (or I[r,c]). The term gray level is used to describe monochromatic intensity.

CS 484, Spring 2010©2010, Selim Aksoy13 Spatial resolution Spatial resolution is the smallest discernible detail in an image. Sampling is the principal factor determining spatial resolution.

CS 484, Spring 2010©2010, Selim Aksoy14 Spatial resolution

CS 484, Spring 2010©2010, Selim Aksoy15 Spatial resolution

CS 484, Spring 2010©2010, Selim Aksoy16 Gray level resolution Gray level resolution refers to the smallest discernible change in gray level (often power of 2).

CS 484, Spring 2010©2010, Selim Aksoy17 Bit planes

CS 484, Spring 2010©2010, Selim Aksoy18 Electromagnetic (EM) spectrum

CS 484, Spring 2010©2010, Selim Aksoy19 Electromagnetic (EM) spectrum The wavelength of an EM wave required to “see” an object must be of the same size as or smaller than the object.

CS 484, Spring 2010©2010, Selim Aksoy20 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy21 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy22 Other types of sensors blue green red near ir middle ir thermal ir middle ir

CS 484, Spring 2010©2010, Selim Aksoy23 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy24 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy25 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy26 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy27 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy28 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy29 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy30 Other types of sensors

CS 484, Spring 2010©2010, Selim Aksoy31 Other types of sensors ©IEEE

CS 484, Spring 2010©2010, Selim Aksoy32 Image enhancement The principal objective of enhancement is to process an image so that the result is more suitable than the original for a specific application. Enhancement can be done in Spatial domain, Frequency domain. Common reasons for enhancement include Improving visual quality, Improving machine recognition accuracy.

CS 484, Spring 2010©2010, Selim Aksoy33 Image enhancement First, we will consider point processing where enhancement at any point depends only on the image value at that point. For gray level images, we will use a transformation function of the form s = T(r) where “r” is the original pixel value and “s” is the new value after enhancement.

CS 484, Spring 2010©2010, Selim Aksoy34 Image enhancement

CS 484, Spring 2010©2010, Selim Aksoy35 Image enhancement

CS 484, Spring 2010©2010, Selim Aksoy36 Image enhancement

CS 484, Spring 2010©2010, Selim Aksoy37 Image enhancement

CS 484, Spring 2010©2010, Selim Aksoy38 Image enhancement

CS 484, Spring 2010©2010, Selim Aksoy39 Image enhancement Contrast stretching:

CS 484, Spring 2010©2010, Selim Aksoy40 Image enhancement

CS 484, Spring 2010©2010, Selim Aksoy41 Histogram processing

CS 484, Spring 2010©2010, Selim Aksoy42 Histogram processing Intuitively, we expect that an image whose pixels tend to occupy the entire range of possible gray levels, tend to be distributed uniformly will have a high contrast and show a great deal of gray level detail. It is possible to develop a transformation function that can achieve this effect using histograms.

CS 484, Spring 2010©2010, Selim Aksoy43 Histogram equalization

CS 484, Spring 2010©2010, Selim Aksoy44 Histogram equalization

CS 484, Spring 2010©2010, Selim Aksoy45 Histogram equalization Adapted from Wikipedia

CS 484, Spring 2010©2010, Selim Aksoy46 Histogram equalization Original RGB imageHistogram equalization of each individual band/channel Histogram stretching by removing 2% percentile from each individual band/channel

CS 484, Spring 2010©2010, Selim Aksoy47 Enhancement using arithmetic operations

CS 484, Spring 2010©2010, Selim Aksoy48 Image formats Popular formats: BMPMicrosoft Windows bitmap image EPSAdobe Encapsulated PostScript GIFCompuServe graphics interchange format JPEGJoint Photographic Experts Group PBMPortable bitmap format (black and white) PGMPortable graymap format (gray scale) PPMPortable pixmap format (color) PNGPortable Network Graphics PSAdobe PostScript TIFFTagged Image File Format

CS 484, Spring 2010©2010, Selim Aksoy49 Image formats ASCII or binary Number of bits per pixel (color depth) Number of bands Support for compression (lossless, lossy) Support for metadata Support for transparency Format conversion …