Turn your Social media in to Paychecks with right Social Media Optimization Strategy

Social media optimization is the process of increasing the awareness of product or service or brand or event by using social media .

Social media includes all the common Twitter,  Facebook , Linkedin , Pinterest , Blogger etc which have large content contributions . To generate the viral publicity we need a unique user generated content base

mostly all business focused on listed in Google's first page results using SEO and keyword researchs to drive traffic in to their web portals. Social media have a faster impact over people than Google . Last month traffic to Blogger was more than 220 million . So its by attracting so of that traffic surely business can achieve more

When even before starting a business people and marketers do create a Facebook Fanpage , twitter handle and Youtube channel , not all business need all these .you can do better making your own online presence which is social media friendly to linking with your products and services

Social Meida is the online version of word of mouth advertising , so by engaging with your social media can make a positive change in the customer base

Being simple the users can communicate with you , being open and honest make you as favourite to your  customers . Merge the current marketing with social media . May be some them are familiar with your existing marketing campaigns link it with social media ,

 Mostly all business have an SEO strategy to make Google's Search Engine ranking priority list . Blogging is the perfect way to optimize the content to Google . Concentrate on making contents for your blog and don't stop there . Cut short the contents or make the headlines for Facebook and twitter updates .use the same for print and email marketing

You can make announcements  via your social media , and other great way of using social media is by offers .. give massive offer announcements which will surely increase number of the customers

If need a perfect SMO strategy contact  


Edge Detection in Image Processing - MATLAB program

Edge detection methods for finding object boundaries in images

Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision.

Prewitt Edge Detection
sobel Edge Detection
canny Edge Detection
log Edge Detection
robert Edge Detection

here is the program for the above edge detections 

Digital Steganography: Hiding Data within Data - MATLAB Program

Uses of Steganography

Steganography is a means of storing information in a way that hides that information’s existence. Paired with existing communication methods, steganography can be used to carry out hidden exchanges. Governments are interested in two types of hidden communications: those that support national security and those that do not. Digital steganography provides vast potential for both types. Businesses may have similar concerns regarding trade secrets or new product information. Avoiding communication in well-known forms greatly reduces the risk of information being leaked in transit.

Images as Carriers
Images are a good medium for hiding data (for details, see Pan, Chen, and Tseng 3 ). The more detailed an image, the fewer constraints there are on how much data it can hide before it becomes suspect. The JPHide/JPSeek package ( uses the coefficients in a JPEG to hide information. A newer method ( embeds data in visually insignificant parts of an image. Both of these methods alter the image; however, you can explore image degradation using different images and messages of varying length. An alternative, specific to GIF images, is to manipulate an image’s palette in order to hide data. Gifshuffle ( does not alter the image itself in any visible way; rather, it permutes a GIF image’s color map, leaving the original image completely intact

Here is the matlab code for stegnography . both encode and decode 

download it from github


Efficient Dilation, Erosion, Opening, and Closing Algorithms in MATLAB

We propose an efficient and deterministic algorithm for computing the one-dimensional dilation and erosion (max and min) sliding window filters. For a p-element sliding window, our algorithm computes the 1D filter using 1:5 þ oð1Þ comparisons per sample point. Our algorithm constitutes a deterministic improvement over the best previously known such algorithm, independently developed by van Herk [25] and by Gil and Werman [12] (the HGW algorithm). Also, the results presented in this paper constitute an improvement over the Gevorkian et al. [9] (GAA) variant of the HGW algorithm. The improvement over the GAA variant is also in the computation model. The GAA algorithm makes the assumption that the input is independently and identically distributed (the i.i.d. assumption), whereas our main result is deterministic. We also deal with the problem of computing the dilation and erosion filters simultaneously, as required, e.g., for computing the unbiased morphological edge. In the case of i.i.d. inputs, we show that this simultaneous computation can be done more efficiently then separately computing each. We then turn to the opening filter, defined as the application of the min filter to the max filter and give an efficient algorithm for its computation. Specifically, this algorithm is only slightly slower than the computation of just the max filter. The improved algorithms are readily generalized to two dimensions (for a rectangular window), as well as to any higher finite dimension (for a hyperbox window), with the number of comparisons per window remaining constant. For the sake of concreteness, we also make a few comments on implementation considerations in a contemporary programming language.
efficient Dilation, Erosion, Opening, and Closing Algorithms
Figure : The effect of the opening (top) and closing (bottom) filters. (Original image is shown on left frame, followed by the filtered image using rectangular windows sized 2x2, 4x4, 8x8, and 16x16.)

Paper link in IEEE

VOL. 24, NO. 12, DECEMBER 2002

Here is the MATLAB code for the same paper is availabe on git hub 

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