harshit ppt image processing

Image Processing

Harshit SrivastavaDepartment of Electrical and Electronics

EngineeringFall Semester 2010

Roll No. 0705621028

Introduction and Special Techniques

19 March 2010 1HARSHIT SRIVASTAVA 2009-2010

Introduction

19 March 2010 HARSHIT SRIVASTAVA 2009-2010 2

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


TopicsTopics

Tom and Bolt


Tom

Bolt

Tom and Bolt will be subjects of some of the imagery in this introduction.


Image FormationImage Formation

light

sour

ce

image plane

lens

objec

t



projection through lens

projection through lens

image of objectimage of object



projection onto discrete sensor array.

projection onto discrete sensor array. digital cameradigital camera



sensors register average colour.

sensors register average colour.

sampled imagesampled image



continuous colours, discrete locations.

continuous colours, discrete locations. discrete real-

valued image

discrete real-valued image

Digital Image Formation: Quantization


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


pixel grid

sampledreal image quantized sampled & quantized

Digital ImageDigital Image


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

HARSHIT SRIVASTAVA 2009-2010

Colour Sensing / Colour Perception


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.


Colour Images

On a CRT

Colour Images

On a CRT


Point ProcessingPoint Processing


original + gamma- gamma + brightness- brightness

original + contrast- contrast histogram EQhistogram mod

Colour Balance and Saturation


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


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


Motion Blur

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verticalregional

zoom rotational

original


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.


Noise Reduction

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colour noiseblurred image colour-only blur

blurred image colour noise 5x5 Wiener filter

Next level of image


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


Nonlinear Processing: Grayscale Reconstruction

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reconstructed openingoriginal


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


Image Compression: JPEG

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JPE

G q

ualit

y le

vel

File size in

bytes


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.


Image Compositing Example

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This man in his home office. Needs a better shirt.



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This shirt demands a monogram.




He needs some more color.


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Nice. Now for the way he’d wear his hair if he had any.



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He can’t stay in the office like this.



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Where’s a Daddy-O like this belong?

Now the background has changed


THANK YOU

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I WOULD LIKE TO THANK PROF. RICHARD ALLEN PETER II


harshit ppt image processing

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