Using Image Acquisition Is The Input Text Document

Shi Et al [13] used a local projection profile at each pixel of the image, and transform the original image into an adaptive local connectivity map (ALCM). For this process, first the gray scale image is reversed so the foreground pixels (text) have idensity values of up to 255. Then the image is downsample to ¼ of each size, ½ in each direction. Next a sliding window of size 2c scans the image from left to right, and right to left in order to compute the cumulative idensity of every neighborhood. This technique is identical to computing the projection profile of every sliding window, (i.e. counting the foreground pixels), but instead of outpiting a projection profile histogram, the entire sliding window sum is saved on the ALCM image. Finaly

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

By examining a range of recently published journal articles, magazine articles and internet sites on the topic of scanners this report describes the main types of scanners in common use today and examines their performance in relation to four criteria: resolution, bit-depth, dynamic range and software. The report then considers the effect of further technological advances in these four areas, as well as the deployment of new sensor technology on the future development of scanners.

The system can read not only JPEG (JPG), but others. But this will make the image large

The forth method is the Video Spectral Comparator 2000. This equipment is used to see concealed or hidden writing on any paper. It also determines the quality of paper and the origin as well as what is called the “lift” indented writing. “This is one of the most valuable forensic technologies available anywhere” because even if the paper is damaged by water or fire, this equipment can still complete the analyses.

2. Learn the best method for scanning different size photographs and which settings will give the best result for digitalizing.

The Binary Plane Techniques are applied for color images as well as for gray scale images. In all of the techniques the compressed file is maintained in two parts. The first part is bit plane and second is data table. The bit plane is collection of l’s and 0‘s to represent whether a pixel is repeated or not. The second part is data table, which holds only the necessary pixel values. The bit plane and data table are merged into one file. To further compress, another loss less technique Huffman coding is applied and final form of compressed file is generated [14][15][16].

Detecting a gray scale image is hard if there is an edge or color information could be helpful if we need known hue identify objects. If we don’t require any color we can considered as noise.

1. In previous technique the data was hided in image due to which only less amount of data was made secure if the large amount of data is hided in the image then the image gets blur.

One component is the LF f the authentic photo, the pinnacle right consists of horizontal info of the picture, the one bottom left consists of vertical information of the unique image, the bottom right contains high frequency (HF) of the original image. The LF coefficients are greater sturdy to embed watermark because it incorporates more statistics of the original image [68]. The reconstruct of the original image from the decomposed image is performed by IDWT [69]. The DWT are scalable in nature. DWT more regularly used in DIW due to its tremendous spatial localization and multi resolution techniques. The remarkable spatial localization assets are very handy to apprehend the place inside the cowl picture in which the watermark is embedded correctly.

In region-based segmentation method, a seed point is selected as our starting point. The pixel intensity of the seed point is used for the initial condition, pixels with similar features are found to be in the same region [11]. As the size of the region increases with the addition of nearby pixels, a region growth take place and this process is referred as region growth process. Thus region-based segmentation methods are more suitable for situations where the background environment and the shape of the foreground may change continuously.

In image processing, noise reduction and restoration of image is expected to enhance the qualitative inspection of an image and the performance criteria of quantitative image analysis methods Digital image is inclined to a variety of noise which attribute the quality of image. The main purpose of de-noising the image is to reinstate the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is contaminated .In the field of reducing the image noise variously type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image betterment have been considered, each of which has their own

In Fig.1.11, the left image is the base picture and the right one is the spliced picture as if so some cropped picture is pasted over the base picture and a new picture is produced. Image splicing is a not unusual form of IF. Such changes may leave no visual clues of tampering. Image splicing is to create a brand new image from two or more pictures, and it is a long way

For facsimile production, Computer image adapting software can practically be divided into two types. The first type (which includes programs such as Adobe Photoshop and the open-source Gimp) involves editing the images themselves, e.g., brightness and contrast manipulations, colour corrections and noise removal. These programs support experienced tools for foreground/background dissolution. For example, in the case of an inscription’s image, ink traces can be isolated from the background element in order to create a facsimile. In Figs. 1a–b, a foreground/background selection of a character can be observed developing a facsimile of the character in Fig. 1c. The second type of image adapting software (such as Adobe Illustrator

2) k-means Algorithm k-means is an unsupervised clustering algorithm which is used to classify input image to k clusters based on the nearest mean. The modified algorithm for k-means derived from [11] is explained as follows