Tuesday, March 22, 2011

spatial filtering

"Spatial filtering" is a term used to describe the methods that are used to compute spatial density estimates for events that have been observed at individual locations.
The term describes a set of tools for displaying functions estimated from these data points that are distributed in two-dimensional space.
Spatial filtering is a form of data smoothing designed to give us a clearer view of the general, unencumbered by details which do not answer our questions of interest.
Spatial filtering is a non-parametric analysis method which belongs within the field of exploratory spatial analysis which relies, to a large degree, on graphical methods of analysis.

Image enhancement methods

SPATIAL DOMAIN METHODS:

The term spatial domain refers to the aggregate of pixels composing an image, and spatial domain methods are procedure that operates directly in this pixel

Image processing function in the spatial domain may be expressed as

g(x,y) =T[f(x,y)]

Where f(x,y) is the input image and g(x,y) is the processed image, and T is an operator on f, defined over some neighborhood about(x,y)

FREQUENCY DOMAIN METHODS:

The foundation of frequency domain technique is the convolution theorem .Let g(,y) be an image formed by the convolution of an image f(x,y)and a linear position, position invariant operator h(x,y) that is, g(x,y) = h(x,y) * f(x,y)

Then from the convolution theorem, the following frequency domain relation holds:

G( u,v) = H (u , v)F(u , v)

Where G,F &H are the Fourier transforms of g,h &h respectively.

spatial domain method of image registration

Spatial domain methods

The value of a pixel with coordinates (x,y) in the enhanced image $\hat{F}$ is the result of performing some operation on the pixels in the neighbourhood of (x,y) in the input image, F.
Neighbourhoods can be any shape, but usually they are rectangular.

Grey scale manipulation

The simplest form of operation is when the operator T only acts on a $1 \times 1$ pixel neighbourhood in the input image, that is $\hat{F}(x,y)$ only depends on the value of F at (x,y). This is a grey scale transformation or mapping.
The simplest case is thresholding where the intensity profile is replaced by a step function, active at a chosen threshold value. In this case any pixel with a grey level below the threshold in the input image gets mapped to 0 in the output image. Other pixels are mapped to 255.
Other grey scale transformations are outlined in figure 1 below.

**Figure 1:** Tone-scale adjustments.
$\begin{figure} \par \centerline{ \psfig {figure=figure51.ps,width=12cm} } \par\end{figure}$

Histogram Equalization

Histogram equalization is a common technique for enhancing the appearance of images. Suppose we have an image which is predominantly dark. Then its histogram would be skewed towards the lower end of the grey scale and all the image detail is compressed into the dark end of the histogram. If we could `stretch out' the grey levels at the dark end to produce a more uniformly distributed histogram then the image would become much clearer.

**Figure 2:** The original image and its histogram, and the equalized versions. Both images are quantized to 64 grey levels.
$\begin{figure} \par \centerline{ \psfig {figure=figure52.ps,width=12cm} } \par\end{figure}$

Histogram equalization involves finding a grey scale transformation function that creates an output image with a uniform histogram (or nearly so). How do we determine this grey scale transformation function? Assume our grey levels are continuous and have been normalized to lie between 0 and 1.
We must find a transformation T that maps grey values r in the input image F to grey values s = T(r) in the transformed image $\hat{F}$ .
It is assumed that

T is single valued and monotonically increasing, and
$0 \leq T(r) \leq 1$ for $0 \leq r \leq 1$ .

The inverse transformation from s to r is given by

r = T^-1(s).

If one takes the histogram for the input image and normalizes it so that the area under the histogram is 1, we have a probability distribution for grey levels in the input image P_r(r).
If we transform the input image to get s = T(r) what is the probability distribution P_s(s) ?
From probability theory it turns out that

$\begin{displaymath} P_{s}(s) = P_{r}(r)\frac{dr}{ds}, \end{displaymath}$

where r = T^-1(s). Consider the transformation

$\begin{displaymath} s = T(r) = \int_{0}^{r}P_{r}(w)dw. \end{displaymath}$

This is the cumulative distribution function of r. Using this definition of T we see that the derivative of s with respect to r is

$\begin{displaymath} \frac{ds}{dr} = P_{r}(r). \end{displaymath}$

Substituting this back into the expression for P_s, we get

$\begin{displaymath} P_{s}(s) = P_{r}(r) \frac{1}{P_{r}(r)} = 1 \end{displaymath}$

for all $s, where 0 \leq s \leq 1$ .Thus, P_s(s) is now a uniform distribution function, which is what we want.

Discrete Formulation

We first need to determine the probability distribution of grey levels in the input image. Now

$\begin{displaymath} P_{r}(r) = \frac{n_{k}}{N} \end{displaymath}$

where n_k is the number of pixels having grey level k, and N is the total number of pixels in the image. The transformation now becomes

Note that $0 \leq r_{k} \leq 1$ , the index $k = 0,1,2,\ldots ,255$ ,and $0 \leq s_{k} \leq 1$ .
The values of s_k will have to be scaled up by 255 and rounded to the nearest integer so that the output values of this transformation will range from 0 to 255. Thus the discretization and rounding of s_k to the nearest integer will mean that the transformed image will not have a perfectly uniform histogram.

Image Smoothing

The aim of image smoothing is to diminish the effects of camera noise, spurious pixel values, missing pixel values etc. There are many different techniques for image smoothing; we will consider neighbourhood averaging and edge-preserving smoothing.

Neighbourhood Averaging

Each point in the smoothed image, $\hat{F}(x,y)$ is obtained from the average pixel value in a neighbourhood of (x,y) in the input image.
For example, if we use a $3 \times 3$ neighbourhood around each pixel we would use the mask

1/9	1/9	1/9
1/9	1/9	1/9
1/9	1/9	1/9

Each pixel value is multiplied by $\frac{1}{9}$ , summed, and then the result placed in the output image. This mask is successively moved across the image until every pixel has been covered. That is, the image is convolved with this smoothing mask (also known as a spatial filter or kernel).
However, one usually expects the value of a pixel to be more closely related to the values of pixels close to it than to those further away. This is because most points in an image are spatially coherent with their neighbours; indeed it is generally only at edge or feature points where this hypothesis is not valid. Accordingly it is usual to weight the pixels near the centre of the mask more strongly than those at the edge.
Some common weighting functions include the rectangular weighting function above (which just takes the average over the window), a triangular weighting function, or a Gaussian.
In practice one doesn't notice much difference between different weighting functions, although Gaussian smoothing is the most commonly used. Gaussian smoothing has the attribute that the frequency components of the image are modified in a smooth manner.
Smoothing reduces or attenuates the higher frequencies in the image. Mask shapes other than the Gaussian can do odd things to the frequency spectrum, but as far as the appearance of the image is concerned we usually don't notice much.

Edge preserving smoothing

Neighbourhood averaging or Gaussian smoothing will tend to blur edges because the high frequencies in the image are attenuated. An alternative approach is to use median filtering. Here we set the grey level to be the median of the pixel values in the neighbourhood of that pixel.
The median m of a set of values is such that half the values in the set are less than m and half are greater. For example, suppose the pixel values in a $3 \times 3$ neighbourhood are (10, 20, 20, 15, 20, 20, 20, 25, 100). If we sort the values we get (10, 15, 20, 20, |20|, 20, 20, 25, 100) and the median here is 20.
The outcome of median filtering is that pixels with outlying values are forced to become more like their neighbours, but at the same time edges are preserved. Of course, median filters are non-linear.
Median filtering is in fact a morphological operation. When we erode an image, pixel values are replaced with the smallest value in the neighbourhood. Dilating an image corresponds to replacing pixel values with the largest value in the neighbourhood. Median filtering replaces pixels with the median value in the neighbourhood. It is the rank of the value of the pixel used in the neighbourhood that determines the type of morphological operation.

**Figure 3:** Image of Genevieve; with salt and pepper noise; the result of averaging; and the result of median filtering.
$\begin{figure} \par \centerline{ \psfig {figure=figure53.ps,width=5in} } \par\end{figure}$

Image sharpening

The main aim in image sharpening is to highlight fine detail in the image, or to enhance detail that has been blurred (perhaps due to noise or other effects, such as motion). With image sharpening, we want to enhance the high-frequency components; this implies a spatial filter shape that has a high positive component at the centre (see figure 4 below).

**Figure 4:** Frequency domain filters (top) and their corresponding spatial domain counterparts (bottom).
$\begin{figure} \par \centerline{ \psfig {figure=figure53a.ps,width=5in} } \par\end{figure}$

A simple spatial filter that achieves image sharpening is given by

-1/9	-1/9	-1/9
-1/9	8/9	-1/9
-1/9	-1/9	-1/9

Since the sum of all the weights is zero, the resulting signal will have a zero DC value (that is, the average signal value, or the coefficient of the zero frequency term in the Fourier expansion). For display purposes, we might want to add an offset to keep the result in the $0 \ldots 255$ range.

High boost filtering

We can think of high pass filtering in terms of subtracting a low pass image from the original image, that is,

High pass = Original - Low pass.

However, in many cases where a high pass image is required, we also want to retain some of the low frequency components to aid in the interpretation of the image. Thus, if we multiply the original image by an amplification factor A before subtracting the low pass image, we will get a high boost or high frequency emphasis filter. Thus,

Now, if A = 1 we have a simple high pass filter. When A > 1 part of the original image is retained in the output.
A simple filter for high boost filtering is given by

-1/9	-1/9	-1/9
-1/9	$\omega$ /9	-1/9
-1/9	-1/9	-1/9

where $\omega = 9A - 1$ .

Monday, March 21, 2011

2nd Internal portion to come on 22.03.11

Marketing management:-

Enviroment scanning tools and techniques.,
module-ii

Market research and Information System,
Consumer behaviour

E-commerce

Convergence,

Collaborative computing,

Content Management,

Call center,

Suply chain management

2nd Internal schedule

date	9:30am-10:30am	11:30 am-12:30pm
22.03.2011	Marketing management	E-commerce
23.03.2011	Image Processing	Mobile computing
24.03.2011	Computer security

if anybody have the portion to come in exam please post here.....

Wednesday, March 9, 2011

Agile Programming

What Is Agile Programming?

Agile programming is an approach to project management, typically used in software development. It helps teams react to the instability of building software through incremental, iterative work cycles, known as sprints. But before turning our discussion to the details of agile programming, it’s best to start at the beginning with the project management paradigm that preceded it: waterfall, or traditional sequential development.

What are the Origins of Agile Programming?

The roots of agile programming can be traced back to 1970, when Dr. Winston Royce delivered a presentation called “Managing the Development of Large Software Systems.” This paper introduced his thoughts on waterfall. Basically, Royce asserted that building software was akin to assembling an automobile. Put another way, each piece can be added in isolated, sequential phases. This process demands that every phase of the project must be completed before the next phase can begin. Thus, developers collect requirements first, work on architecture and design second, and so on. Very little communication occurs during the hand-offs between the specialized groups responsible for each phase of development.
You can probably begin to think of ways in which this approach to software development is flawed. For example, it assumes the customer can identify every single requirement of the project prior to any coding taking place. In other words, waterfall imagines that a customer knows exactly what he or she wants at the outset and that he or she can deliver an airtight and inclusive plan for achieving that vision. If you’re a developer, then you know that it’s infinitely easier for a customer to describe his or her vision when there is functional software to interact with and respond to. It’s a lesson that many software developers have discovered through failing. Moreover, many times, when a waterfall project wraps, a team that built software based on customer specifications finds out that it’s not actually what the customer wanted or another project has rendered all that heads-down programming irrelevant. In the latter scenario, an organization has spent time and money to create a product that no one wants.

Why Agile Programming?

Agile programming gives teams repeated opportunities to assess the direction of a project throughout the entire development lifecycle. These chances for evaluation are built into the natural workflow of agile programming through regular cadences of work, known as sprints or iterations. At the end of every sprint, the team presents a functioning piece of software to the Product Owner for review. This emphasis on a shippable product ensures that the team doesn’t get bogged down with gathering requirements. Because sprints repeat and the product continually adds increments of functionality, agile programming is described as “iterative” and “incremental.” In waterfall, development teams have a single shot at getting each part of a project right. Not so in agile programming, in which every aspect of development is revisited throughout the lifecycle. There’s virtually no chance that a project will follow the wrong direction for very long. Because the team reassesses the direction of a project regularly, there’s always time to change course.
Perhaps the effects of this “inspect-and-adapt” strategy are obvious: Namely, they significantly reduce both development costs and time to market. Because teams can gather requirements while coding, exhaustive requirements gathering can’t prevent a team from making progress. And because agile teams develop within the short, repeatable work cycles, stakeholders have ongoing opportunities to ensure that the product being created matches the customers’ vision. In essence, it could be said that agile programming helps companies build products their customers want. Instead of committing to market a piece of unwritten, sub-optimized software, agile programming empowers teams to build the best software possible. In the end, agile programming protects a product’s market relevance and prevents a team’s work from winding up on a shelf, unreleased, which is an option that makes both stakeholders and developers happy.

Saturday, March 5, 2011

facebook mail

Facebook is used by millions of people around the globe. Here is a sneak preview of the mail service which will be introduced by them. Email by Facebook would in our view, a great feature and would help the users get more connected to their social network.

The picture below shows how you may check your Facebook emails in your inbox. However, this might just be a test run but still its something to get excited about.

Digital Signature

A digital signature is a mathematical scheme for demonstrating the authenticity of a digital message or document. A valid digital signature gives a recipient reason to believe that the message was created by a known sender, and that it was not altered in transit. Digital signatures are commonly used for software distribution, financial transactions, and in other cases where it is important to detect forgery or tampering.

Digital signatures are often used to implement electronic signatures, a broader term that refers to any electronic data that carries the intent of a signature,^[1] but not all electronic signatures use digital signatures.^[2]^[3]^[4] In some countries, including the United States, India, and members of the European Union, electronic signatures have legal significance. However, laws concerning electronic signatures do not always make clear whether they are digital cryptographic signatures in the sense used here, leaving the legal definition, and so their importance, somewhat confused.

Digital signatures employ a type of asymmetric cryptography. For messages sent through a nonsecure channel, a properly implemented digital signature gives the receiver reason to believe the message was sent by the claimed sender. Digital signatures are equivalent to traditional handwritten signatures in many respects; properly implemented digital signatures are more difficult to forge than the handwritten type. Digital signature schemes in the sense used here are cryptographically based, and must be implemented properly to be effective. Digital signatures can also provide non-repudiation, meaning that the signer cannot successfully claim they did not sign a message, while also claiming their private key remains secret; further, some non-repudiation schemes offer a time stamp for the digital signature, so that even if the private key is exposed, the signature is valid nonetheless. Digitally signed messages may be anything representable as a bitstring: examples include electronic mail, contracts, or a message sent via some other cryptographic protocol.