Image Processing

Image Processing

by

S.Steena Vaiz

Introduction

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.

Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

Types

Image processing usually refers to digital image processing, but optical and analog image processing are also possible.

Image Processing Operations

Geometric transformations such as enlargement, reduction, and rotation

Color corrections such as brightness and contrast adjustments, quantization, or conversion to a different color space

Digital compositing or optical compositing (combination of two or more images). Used in filmmaking to make a "matte"

Interpolation, demosaicing, and recovery of a full image from a raw image format using a Bayer filter pattern

Image Processing Operations(Contd.) Image editing (e.g., to increase the quality of a digital

image)

Image differencing

Image registration (alignment of two or more images)

Image stabilization

Extending dynamic range by combining differently exposed images

Image Segmentation

Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images.

The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image.

Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).

Image Processing Applications Computer vision Face recognition Feature detection Non-photorealistic rendering Medical image processing Microscope image processing Morphological image processing Remote sensing

Face Recognition

A facial recognition system is an image processing

application for automatically identifying or verifying a

person from a digital image or a video frame from a

video source. One of the ways to do this is by comparing

selected facial features from the image and a facial

database.

Face Recognition Used in :

Human and computer interface

Biometric identification

Principal Component Analysis (PCA) :

Widely adopted

Most promising face recognition algorithm

Solution : Applying PCA on wavelet subband

Subbands obtained using wavelet decomposition.

PCA applied on the generated subband face

Objective of Face Recognition : To determine the identity of a person from a given

face image. Complications occur due to variations in :

Illumination Pose facial expression Aging occlusions such as spectacles, hair, etc.

In the proposed method we proceed as follows :

Decompose face image into subbands using Discrete Wavelet

Transform (DWT)

Select mid-frequency subband (Diagonal) from third level.

Compute representational bases (apply PCA) for reference

images

Store as training image representations

Translate probe image into probe image representation using

representational bases

Use classifier to match with reference images to identify face

image

Discrete Wavelet Transform A face image of a person contains common (approximation) as well

as discriminatory (detail) information.

Discriminatory information is due to structural variations of the face.

The similarity information and discriminatory information are

segregated in different subbands at different levels of decomposition

of the face image.

Wavelet decomposition splits the facial features into :

Approximations, containing common (smooth) parts of the face

Details, containing the discriminatory (variations) information.

DWT (Contd.) The original image is decomposed into four

subbands - Approximation (A), Horizontal (H), Vertical (V ) and Diagonal (D) details.

where D = {H, V,D} such that A1= A2+D2 = A3+D3+D2.

Subband Creation & Selection

Principal Component Analysis (PCA) To recognize a face we need to measure the

difference between the new image and the original images

But the face contains an awful lot of data PCA is used to find a low dimensional

representation of data By means of PCA, one can transform each original

image of the training set into a corresponding eigenface

Eigenface

Eigenface is the eigenvector obtained from PCA Each eigenface represents only certain features of

the face In essence, eigenfaces are nothing but the

characteristic features of a face Similar faces (images) possess similar features

(eigenfaces) So, all images having similar eigenfaces are likely to

be similar faces

Face & their eigenfaces

Classification

An important part of image analysis is identifying

groups of pixels having similar spectral

characteristics and to determine the various features

This form of analysis is known as classification

Classification employs two phases of processing: Training – Create unique description based on

characteristic properties of image (face)

Testing – Match the description and classify the image

(face)

Face Recognition Process

The training & recognition processesTraining Process

Recognition Process

Training Stage Steps involved :

Apply 3-level Daubechies Wavelet Transform on

reference images

Choose subband 4 from level 3

Apply PCA on subband 4 & get eigenvectors and

eigenvalues

By arranging eigenvalues in a descending order,

eigenvectors with largest eigenvalues are used as

representational bases

Recognition Stage Steps Involved:

Apply 3-level Daubechies Wavelet Transform on

the test images

Apply PCA on subband 4 & get the eigenvectors

and eigenvalues

Use k-NN classifier to classify the test images into

appropriate classes based on the training set

Conclusion

Hence the nearest and similar neighbour is matched and the input face image is recognised using the Image Processing technique.

THANK YOU !!!