Digital Processing Lecture 1 (Introduction) Bu-Ali Sina University Computer Engineering Dep. Fall 2011
Introduction One picture is worth more than ten thousand p words
Outline Syllabus References Course Plane Grading and policies Introduction to image processing
Introduction
Syllabus
MSRT References
References Digital Processing, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002
Digital Processing Using matlab muya
Recommended journals and conferences IEEE tran. On processing Journal of Graphics, Vision and Processing (GVIP) and Vision Computing Computer Vision and Understanding Journal of Visual Communication and Representation International Journal of Computer Vision Machine Vision and Applications Journal of Mathematical Imaging and Vision Graphical Models and Processing
Course plan fundamental enhancement (Spatial domain) transform (Fourier, DCT) enhancement (Frequency domain) restoration Color image processing compression Morphological image processing segmentation
Grading and Policies Exams 50% Midterm 50% (25% of total) about 15/8/90 Final 50% (25% of total) Final Project (25%) One project (deadline is about 31/1/90) Seminar (15%) Every body presents a seminar (select a subject until 15/8/90) Home works (10%) 5 home works
Hours: 8-10 Sun. and Tue. (every two week) Site: http://profs.basu.ac.ir/khotanlou Email:Hassan.khotanlou@gmail.com hkh@basu.ac.ir Contact: 8257410, 11 (324 )
Contents This lecture will cover: What is a digital image? What is digital image processing? History of digital image processing State of the art examples of digital image State of the art examples of digital image processing
What is a Digital? A digital image is a representation of a twodimensional image as a finite set of digital values, called picture elements or pixels s taken from Gonzalez & Woods, Digital Processing (2002)
What is a Digital? (cont ) Processing (2002) s taken from Gonzalez & Woods, Digital Pixel values typically represent gray levels, colours, heights, etc Remember digitization implies that a digital image is an approximation of a real scene 1 pixel
What is digital image? An image: 2-d function I=f(x,y) I: intensity(or color) (x,y): coordinate When (x,y) and I are finite and discrete quantities -> digital image pixels, picture elements, image elements, pels
Representing digital images
Pixels
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Representing digital images Matrix form f(0,0) f(0,1) f(0,n-1) f(1,0) f(0,1) f(1,n-1) f(m-1,0) f(m-1,1) f(m-1,n-1) MxN bits to store the image = M x N x k gray level = 2 k
Sources of digital images Electromagnetic(EM) energy Acoustic imaging Synthetic (computer-generated) imaging
EM images (cont.) The same objects in different EM spectrum
Ultrasound images
Synthetic images
What is a Digital? (cont ) Common image formats include: 1 sample per point (B&W or Grayscale) 3 samples per point (Red, Green, and Blue) 4 samples per point (Red, Green, Blue, and Alpha, Opacity) For most of this course we will focus on greyscale images
What is Digital Processing? Digital image processing focuses on two major tasks Improvement of pictorial information for human interpretation Processing of image data for storage, transmission and representation for autonomous machine perception Some argument about where image processing ends and fields such as image analysis and computer vision start
What is DIP? (cont ) The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes Low Level Process Mid Level Process High Level Process Input: Output: Examples: Noise removal, image sharpening Input: Output: Attributes Examples: Object recognition, segmentation Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation In this course we will stop here
Research fields Low-level processing Mid-level processing processing Early vision High-level processing Computer vision Brain processing
Related fields processing Inputs and outputs are images Extract attributes from images analysis Computer vision Use computers to emulate human vision Related to artificial intelligence (AI) Pattern Recognition
History of Digital Processing Processing (2002) s taken from Gonzalez & Woods, Digital Early 1920s: One of the first applications of digital imaging was in the newspaper industry The Bartlane cable picture transmission service s were transferred by submarine cable between London and New York Pictures were coded for cable transfer and reconstructed at the receiving end on a telegraph printer Early digital image
History of DIP (cont ) Processing (2002) s taken from Gonzalez & Woods, Digital Mid to late 1920s: Improvements to the Bartlane system resulted in higher quality images New reproduction processes based on photographic techniques Increased number of tones in reproduced images Improved digital image Early 15 tone digital image
History of DIP (cont ) Processing (2002) s taken from Gonzalez & Woods, Digital 1960s: Improvements in computing technology and the onset of the space race led to a surge of work in digital image processing 1964: Computers used to improve the quality of images of the moon taken by the Ranger 7 probe Such techniques were used in other space missions including the Apollo landings A picture of the moon taken by the Ranger 7 probe minutes before landing
History of DIP (cont ) Processing (2002) s taken from Gonzalez & Woods, Digital 1970s: Digital image processing begins to be used in medical applications 1979: Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack share the Nobel Prize in medicine for the invention of tomography, the technology behind Computerised Axial Tomography (CAT) scans Typical head slice CAT image
History of DIP (cont ) 1980s - Today: The use of digital image processing techniques has exploded and they are now used for all kinds of tasks in all kinds of areas enhancement/restoration Artistic effects Medical visualisation Industrial inspection Law enforcement Human computer interfaces
Examples: Enhancement One of the most common uses of DIP techniques: improve quality, remove noise etc s taken from Gonzalez & Woods, Digital Processing (2002)
Examples: The Hubble Telescope Launched in 1990 the Hubble telescope can take images of very distant objects However, an incorrect mirror made many of Hubble s images useless processing techniques were used to fix this
Examples: Artistic Effects Artistic effects are used to make images more visually appealing, to add special p effects and to make composite images
Examples: Medicine Processing (2002) Take slice from MRI scan of canine heart, and find boundaries between types of tissue with gray levels representing tissue density Use a suitable filter to highlight g edges s taken from Gonzalez & Woods, Digital Original MRI of a Dog Heart Edge Detection
Examples: GIS Processing (2002) s taken from Gonzalez & Woods, Digital Geographic Information Systems Digital image processing techniques are used extensively to manipulate satellite imagery Terrain classification Meteorology
Examples: GIS (cont ) Processing (2002) s taken from Gonzalez & Woods, Digital Night-Time Lights of the World data set Global inventory of human settlement Not hard to imagine the kind of analysis that might be done using this data
Examples: Industrial Inspection Processing (2002) s taken from Gonzalez & Woods, Digital Human operators are expensive, slow and unreliable Make machines do the job instead Industrial vision systems are used in all kinds of industries Can we trust them?
Examples: PCB Inspection Printed Circuit Board (PCB) inspection Machine inspection is used to determine that all components are present and that all solder joints are acceptable Both conventional imaging and x-ray imaging are used
Examples: Law Enforcement Processing (2002) s taken from Gonzalez & Woods, Digital processing techniques are used extensively by law enforcers Number plate recognition for speed cameras/automated toll systems Fingerprint recognition Enhancement of CCTV images
Examples: HCI Try to make human computer interfaces more natural Face recognition Gesture recognition Does anyone remember the user interface from Minority Report? These tasks can be extremely difficult
Key Stages in Digital Processing Restoration Morphological Processing Enhancement Segmentation Acquisition Object Recognition Problem Domain Colour Processing Compression Representation & Description
Key Stages in Digital Processing: Aquisition Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Enhancement Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Restoration Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Morphological Processing Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Segmentation Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Object Recognition Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Representation & Description Processing (2002) s taken from Gonzalez & Woods, Digital Enhancement Acquisition Problem Domain Restoration Colour Processing Morphological Processing Compression Segmentation Object Recognition Representation & Description
Key Stages in Digital Processing: Compression Restoration Morphological Processing Enhancement Segmentation Acquisition Object Recognition Problem Domain Colour Processing Compression Representation & Description
Key Stages in Digital Processing: Colour Processing Restoration Morphological Processing Enhancement Segmentation Acquisition Object Recognition Problem Domain Colour Processing Compression Representation & Description
Fundamental steps in DIP
Summary We have looked at: What is a digital image? What is digital image processing? History of digital image processing State of the art examples of digital image processing Key stages in digital image processing