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.

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 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

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 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).

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

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.

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

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.

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

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.

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.

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

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

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)

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 processes Training 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

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

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.

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

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