Digital image consist of very small elements called pixels

Pixel value describes how bright that pixel is, and/or what color it should be

• Binary image

• Gray scale image

• Color image

Type of image

8-bit color image 24-bit color image

b = M * N * k

Number of bits to store

image

Number of rows

Number of columns

Number of bits for each

pixel

Color Quantization

Color quantization is applied when the color information of an image is to be reduced. The most common case is when a 24-bit color image is transformed into an 8-bit color image

Distance Metrics

distance between two pixels in an image

Euclidean Distance

City Block Distance

Chessboard Distance

APPLICATIONS OF IMAGE PROCESSING

1. Automatic Visual Inspection System

• Automatic inspection of incandescent lamp filaments

• Faulty component identification

• Automatic surface inspection systems

3 .Biomedical Imaging Techniques• localizing the objects of interest, i.e. different organs

• taking the measurements of the extracted objects, e.g. tumors in the image

2. Remotely Sensed Scene Interpretation

5 .Content-Based Image Retrieval

7 .Image and Video Compression 7 .Image and Video Compression

4 .Defense surveillance

• interpreting the objects for diagnosis

Lung disease identification

Heart disease identification

Digital mammograms

Classification of textures

Based on the attributes, textures are of two types:1. Microtextures2. Macrotextures

This classification is based on the size of the primitives that constitutethe textures

Two important attributes of such textures are coarseness and directionality

The biometric identification systems are useful in several applications such as commercial and law enforcement applications, especially in criminal identification, security system, videophone, credit card verification, photGIDs for personal identification, etc. Recognition of human faces, fingerprints, signatures

Biometric

Image Negatives

s = L - 1 - r.

Log Transformations

s = c log (1 + r)

Power-Law Transformations

s = cr

With gamma 3, 4, and 5

Real world signals usually contain departures from the ideal signal that would be produced by our model of the signal production process. Such departures are referred to as noise

Noise Generation

the noise is caused by errors in the data transmission. The corrupted pixels are either set to the maximum value (which looks like snow in the image) or have single bits flipped over

single pixels are set alternatively to zero or to the maximum value, giving the image a `salt and pepper' like appearance

The impact of the noise on the image is often described by the signal to noise ratio

It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels grow in size while holes within those regions become smaller.

Dilation

it can be used to fill in small spurious holes (`pepper noise') in images

shows an image containing pepper noise

shows the result of dilating this image with a 3×3 square structuring element

uses for dilation

Dilation can also be used for edge detection by taking the dilation of an image and then subtracting away the original image

uses for erosion

One of the more common is to separate touching objects in a binary image so that they can be counted using a labeling algorithm

It is typically applied to binary images, but there are versions that work on grayscale images. The basic effect of the operator on a binary image is to erode away the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels shrink in size, and holes within those areas become larger.

Erosion

Erosion can also be used to remove small spurious bright spots (`salt noise') in images

shows an image with salt noiseshows the result of erosion with a 3×3 square structuring element

We can also use erosion for edge detection by taking the erosion of an image and then subtracting it away from the original image

erosion is also used as the basis for many other mathematical morphology operators

Pixel Addition

In its most straightforward implementation, this operator takes as input two identically sized images and produces as output a third image of the same size as the first two, in which each pixel value is the sum of the values of the corresponding pixel from each of the two input images. More sophisticated versions allow more than two images to be combined with a single operation.

C = 50

Image Operations

Blending

This operator forms a blend of two input images of the same size

and are the two input images. In some applications can also be a constant, thus allowing a constant offset value to be added to a single image. X is the blending ratio which determines the influence of each input image in the output

The resulting image is calculated using the formula

X= 0.5

+

Pixel Multiplication and ScalingLike other image arithmetic operators, multiplication comes in two main forms. The first form takes two input images and produces an output image in which the pixel values are just those of the first image, multiplied by the values of the corresponding values in the second image. The second form takes a single input image and produces output in which each pixel value is multiplied by a specified constant. This latter form is probably the more widely used and is generally called scaling

X 3 X 5

Pixel Subtraction

The pixel subtraction operator takes two images as input and produces as output a third image whose pixel values are simply those of the first image minus the corresponding pixel values from the second image. It is also often possible to just use a single image as input and subtract a constant value from all the pixels

Pixel Division

The exponential and `raise to power' operators are two operators which can be applied to grayscale images. Like the logarithmic transform, they are used to change the dynamic range of an image. However, in contrast to the logarithmic operator, they enhance high intensity pixel values.

where P and Q are the input and output images respectively, b is the basis and c is the scaling factor

enhancing contrast of brighter regions

Exponential and “raise to power”

If r > 1, the `raise to power' operator is similar to the exponential operator in the sense that it increases the bandwidth of the high intensity values at the cost of the low pixel values. However, if r < 1, the process enhances the low intensity value while decreasing the bandwidth of the high intensity values

Invert/Logical NOT

Logical NOT or invert is an operator which takes a binary or graylevel image as input and produces its photographic negative, i.e. dark areas

in the input image become light and light areas become dark .

The logical NOT can also be used for a graylevel image being stored in byte pixel format by applying it in a bitwise fashion. The resulting value for each

pixel is the input value subtracted from 255 :

Bitshift Operators

Logical Operators

AND

OR

Logical OR/NOR

the output values of NOR are simply the inverses of the corresponding output values of OR

the histogram of an image normally refers to a histogram of the pixel intensity values. This histogram is a graph showing the number of pixels in an image at each different intensity value found in that image

Histogram

Contrast stretching (often called normalization) is a simple image enhancement technique that attempts to improve the contrast in an image by `stretching' the range of intensity values it contains to span a desired range of values, e.g. the full range of pixel values that the image type concerned allows

Contrast Stretching

The simplest sort of normalization then scans the image to find the lowest and highest pixel values currently present in the image. Call these c and d. Then each pixel P is scaled using the following function:

Values below 0 are set to 0 and values above 255 are set to 255.

c = 79 and d = 136

In many vision applications, it is useful to be able to separate out the regions of the image corresponding to objects in which we are interested, from the regions of the image that correspond to background. Thresholding often provides an easy and convenient way to perform this segmentation on the basis of the different intensities or colors in the foreground and background regions of an image

Thresholding

•a band of intensity values can be set to white while everything else is set to black

•may be possible to set different thresholds for each color channel, and so select just those pixels within a specified cuboid in RGB space

•Another common variant is to set to black all those pixels corresponding to background, but leave foreground pixels at their original color/intensity (as opposed to forcing them to white), so that that information is not lost

adaptive thresholding changes the threshold dynamically over the image. This more sophisticated version of thresholding can accommodate changing lighting conditions in the image, e.g. those occurring as a result of a strong illumination gradient or shadows.

Adaptive Thresholding:

There are two main approaches to finding the threshold:) i (the Chow and Kaneko approach and

) ii (local thresholding

Global threshold

image mean of a 7×7 neighborhood

7×7 neighborhood and C=7

75×75 neighborhood

and C=10

like Thresholding except choose values locally

The reflection operator geometrically transforms an image such that image elements, i.e. pixel values, located at position in an original image are reflected about a user-specified image axis or image point into a new position in a corresponding output image

Reflect

Reflection about a horizontal axis of ordinate :

Geometric Scaling

Scaling is used to change the visual appearance of an image, to alter the quantity of information stored in a scene representation, or as a low-level preprocessor in multi-stage image processing chain which operates on features of a particular scale

The rotation operator performs a geometric transform which maps the position of a picture element in an input image onto a position in an output image by rotating it through a user-specified angle about an origin

Rotate

The rotation operator performs a transformation of the form :

Translate

The translate operator performs a geometric transformation which maps the position of each picture element in an input image into a new position in an output image, where the dimensionality of the two images often is, but need not necessarily be, the same

The translation operator performs a transformation of the form :

ConvolutionConvolution is a simple mathematical operation which is fundamental to many common image processing operators

Small image

Kernel

Line Detection

The line detection operator consists of a convolution kernel tuned to detect the presence of lines of a particular width n, at a particular orientation

Four line detection kernels which respond maximally to horizontal, vertical and oblique (+45 and - 45 degree) single pixel wide lines .

Conservative Smoothing

Conservative smoothing is a noise reduction technique

if the central pixel intensity is greater than the maximum value, it is set equal to the maximum value; if the central pixel intensity is less than the minimum value, it is set equal to the minimum value

Mean Filter

Mean filtering is a simple, intuitive and easy to implement method of smoothing images, i.e. reducing the amount of intensity variation between one pixel and the next. It is often used to reduce noise in images.