introduction to image processing
DESCRIPTION
This presentation gives basic insight to image processing techniques to morphology and histogram . The presentation is made according to a novice and can be understood by anyone as it starts with basic concepts of images.TRANSCRIPT
Image Processing
Harshit SrivastavaDepartment of Electrical and Electronics
EngineeringFall Semester 2010
Roll No. 0705621028
Introduction and Special Techniques
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Introduction
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This presentation is an overview of some of the ideas and techniques of image processing.
Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image.
Image processing usually refers to digital image processing.
Digital image processing is the use of computer algorithms to perform image processing in digital images.
1. Image formation 2. Point processing and equalization 3. Colour correction 4. Image sampling and warping 5. Noise reduction 6. Mathematical morphology 7. Image compression 8. Image compositing
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TopicsTopics
Tom and Bolt
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Tom
Bolt
Tom and Bolt will be subjects of some of the imagery in this introduction.
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Image FormationImage Formation
light
sour
ce
image plane
lens
objec
t
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Image FormationImage Formation
projection through lensprojection through lens
image of objectimage of object
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Image FormationImage Formation
projection onto discrete sensor array.
projection onto discrete sensor array. digital cameradigital camera
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Image FormationImage Formation
sensors register average colour.sensors register average colour.
sampled imagesampled image
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Image FormationImage Formation
continuous colours, discrete locations.continuous colours, discrete locations.
discrete real-valued imagediscrete real-valued image
Digital Image Formation: Quantization
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continuous colour input
disc
rete
col
our
outp
ut
continuous colours mapped to a finite, discrete set of colours.
continuous colours mapped to a finite, discrete set of colours.
Sampling and Quantization
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pixel grid
sampledreal image quantized sampled & quantized
Digital ImageDigital Image
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a grid of squares, each of which contains a single colour
a grid of squares, each of which contains a single colour
each square is called a pixel (for picture element)
each square is called a pixel (for picture element)
Colour images have 3 values per pixel; monochrome images have 1 value per pixel.
Colour Processing
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requires some knowledge of how we see colors
requires some knowledge of how we see colors
Eye’s Light Sensors
#(blue) << #(red) < #(green)
cone density near fovea
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Colour Sensing / Colour Perception
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These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye. The eye has 3 types of photoreceptors: sensitive to red, green, or blue light,
The brain transforms RGB into separate brightness and color channels (e.g., LHS).
These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye. The eye has 3 types of photoreceptors: sensitive to red, green, or blue light,
The brain transforms RGB into separate brightness and color channels (e.g., LHS).
The simultaneous red + blue response causes us to perceive a continuous range of hues on a circle. No hue is greater than or less than any other hue.
The simultaneous red + blue response causes us to perceive a continuous range of hues on a circle. No hue is greater than or less than any other hue.
Colour Images
• Are constructed from three intensity maps.
• Each intensity map is pro-jected through a colour filter (e.g., red, green, or blue, or cyan, magenta, or yellow) to create a monochrome image.
• The intensity maps are overlaid to create a colour image.
• Each pixel in a colour image is a three element vector.
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Colour Images
On a CRT
Colour Images
On a CRT
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Point ProcessingPoint Processing
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original + gamma- gamma + brightness- brightness
original + contrast- contrast histogram EQhistogram mod
Colour Balance and Saturation
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Uniform changes in colour components result in change of tint.
E.g., if all G pixel values are multiplied by > 1 then the image takes a green cast.
Resampling
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8× 16×nearest neighbornearest neighbor nearest neighbornearest neighbor
bicubic interpolationbicubic interpolation bicubic interpolationbicubic interpolation
(resizing)
ROTATION
MOTION BLURMotion blur happens when an camera cannot distinguish these values
1. Egomotion2. Tracking3. Optical flow
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and motion blur
In geometry and linear algebra, a rotation is a transformation in a plane or in space that describes the motion of a rigid body around a fixed point
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Motion Blur
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verticalregional
zoom rotational
original
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Image Warping Image warping is an special type of affect which changes the function of an image…to next level..
In image warping the dimension of every side is changed to get effect..
It is an special type of affect which changes the orignality of image.
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Noise Reduction
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colour noiseblurred image colour-only blur
blurred image colour noise 5x5 Wiener filter
Next level of image
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MorphologyNonlinear Processing: Binary Reconstruction
• Used after opening to grow back pieces of the original image that are connected to the opening.
• Permits the removal of small regions that are disjoint from larger objects without distorting the small features of the large objects.
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original opened reconstructed
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Nonlinear Processing: Grayscale Reconstruction
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reconstructed openingoriginal
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Image Compression
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Yoyogi Park, Tokyo, October 1999.
Original image is 5244w x 4716h @ 1200 ppi: 127MBytes
Original image is 5244w x 4716h @ 1200 ppi: 127MBytes
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Image Compression: JPEG
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JPE
G q
ualit
y le
vel F
ile size in bytes
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Image Compositing• Combine parts from separate images to form a new image.• It’s difficult to do well.• Requires relative positions, orientations, and scales to be
correct.• Lighting of objects must be consistent within the separate
images.• Brightness, contrast, colour balance, and saturation must
match.• Noise colour, amplitude, and patterns must be seamless.
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Image Compositing Example
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This man in his home office. Needs a better shirt.
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Image Compositing Example
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This shirt demands a monogram.
NOW
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Image Compositing Example
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He needs some more color.
And again some more
changes
Image Compositing Example
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Nice. Now for the way he’d wear his hair if he had any.
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Image Compositing Example
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He can’t stay in the office like this.
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Image Compositing Example
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Where’s a Daddy-O like this belong?
Now the background has changed
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THANK YOU
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I WOULD LIKE TO THANK PROF. RICHARD ALAN PETER II
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