A. Image enhancement Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features. Image enhancement mainly includes some process: A.1 Convert a Colored Image into Gray Image Every digital image mainly comprises of three components or it is made up of three colors that is RGB (Red Green and Blue) and each image is represented in a 3-dimensional which is very difficult to analysis and process. Therefore to overcome some difficulty initially each image is first converted into a gray image which is made up of white and black components of varying intensity. …show more content…
So on, it moves until the end of a column and calculate the total sum of differences between adjacent pixels. At the end, an array containing the column-wise sum is created. The same process is carried out to find the vertical array. In this case, rows are processed instead of columns. A.4 Filtering out Unwanted Regions in an Image Once the arrays are passed through a low-pass digital filter, a filter is applied so that to remove undesirable areas from an image which includes the horizontal and vertical components of array with low values. Generally low value indicates the plain areas with characters it is due to the fact that intensity of each neighboring pixel of plain areas contains a similar intensity or pixel values such that during horizontal and vertical processing the difference between them is negligible but in case of edges and specially numeric characters the difference between adjacent pixel is high due to variation of intensity, which results into a high array value for such part of an image. Therefore, the required area of region of interest with probable number has a high values while areas with small value are undesirable and such regions are filtered out by applying a certain threshold. In this algorithm, the threshold is equal to the average value of an array. Both row and column vectors are passed through a filter with this threshold, so that the final output
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
Digital Filter. An on-chip digital filter is used to minimize the signals and noise that are outside the band of interest.
To introduce color composites, histograms, and scatterplots as tools for exploring image data stored in database channels
These data items for recovering a tile image T are integrated as a five-component bit stream of the form in which the bit segments represent the values of the index of B, the rotation angle of T , the means of T and B, the standard deviation quotients, and total number of tiles, respectively. In more detail, the numbers of required bits for the five data items in M are
Answer: - In the raster data model, a grid is used to cover the space and the value of each cell called as pixel in the grid and it corresponds to the characteristic of the geographic feature at the cell location. The cell is the smallest unit in the grid which is a matrix of
where $\Psi(P)$(resp. $\Psi(Q)$) is a square patch centered in $P$ (resp. in $Q$.) Then, the pixel $P$ is replaced the pixel $Q$ being the centre of the most similar patch $\Psi(Q)$ to the $\Psi(P)$. In this way, the image gap is filled-in recursively pixel by
This coding method helps to remove the redundancy between row vectors and column vectors of a block. For each 8 × 8 block RCRC generates r, which represents the row reference vector (RRV), and c, which represents column reference vector (CRV as cited in [1]. Vectors r and c may be viewed as 8-tuples which can acquire values 0 ≤ ri ≥ 1, 0 ≤ ci ≥ 1 and 1 ≤ i ≥ 8. A comparison between pairs of rows and columns from the block b is done in an iterative manner. The result of comparison gives the reduced vector. If the rows or columns of a given pair are identical, then the first pair of vector is kept for future use and the second pair of vector is eliminated, the second pair of vector corresponds to the duplicate pair. The final vector contains the reduced block. If the vectors in the given pair are not identical, then both of the vectors are preserved. The preserved vector pairs of rows and columns are stored in RRV and CRV,
The goal of the feature extraction and selection is to reduce the dimension of the data. In this experiment the dimension of the AVIRIS and HYDICE images reduced to 20 from 220 and 191 respectively using PCA. From the PCA analysis we can see that image of principal component 1 is brightest and sharpest than other PCA image which is illustrated in figure-2.
Pixel count - A pixel is a physical point in an image represented on the screen. The intensity of each pixel is variable. The more pixels, the better the quality. Resolution
The basic principle of this algorithm is to recognize the input paper currency. First of all acquired the image from a particular source. As in this thesis we use for reference images. System read the particular image. Then resize the image. After that the color separator convert the image into RGB to Gray scale and then in binary image. After that the system use color noise median filter. The currency length detector detects the length of the currency. Using the feature extraction techniques the system detect the particular feature of that currency and then the system use pattern matching algorithm to math that particular feature. The input image match with particular database image and according to that we find the currency. In this way this thesis design a automatic system in which we can recognized the paper currency.
Digital photography was the reason for the graphics editing software such as Adobe Photoshop were developed, and has changed the way people take pictures. The digital camera has been in development since George Smith and Willard Boyle invented the charged-coupled device (CCD) which is an image sensor. The original intended function of the CCD was to be the new semi-conductor memory for computers. By 1975 image quality improved greatly and was able to be broadcasted over television. Since then cameras have improved eminently through the years, progressively advancing and functioning more dynamically. A digital image is a long string of 1s and 0s that represent all the pixels in an image. Just as with film cameras, the image that is recorded is visible light or flash. When the light bounces off the subject and is captured in the camera, it breaks into light patterns and is converted into a series of pixel values. The light is then converted into electric charges which are then used by the CCD or CMOS (Complementary Metal Oxide Semiconductor) to record images.
Describe an algorithm in pseudocode, prose, graphical, or any other representation, to collect and reconstruct the original datagram's data field based on this concept.
In image processing, Edge Detection is a fundamental tool based on mathematical methods to detect points in a digital image at which there is a huge variation in the brightness between each other. These points are organized in a line of segments which is called edges. The purpose of detecting those variations is to help analyze an image in the following aspects:
Image Segmentation is concerned about segmenting the image into various segments using various techniques. In early days a semi-automatic approach was being used to detect the exact boundaries of the brain tumor. However the semiautomatic methods were not very successful as they had human induced errors and were time consuming. A better application of tumor detection was made by introducing fully automated tumor detection systems. Various methods have been proposed like Markov random fields method, Fuzzy c-means (FCM) clustering, Otsu’s thresholding, K-Mean’s, neural network. In this project, four different algorithms namely Otsu’s method, Thresholding, K-means method and Fuzzy c-means and PSO have been used for designing the brain tumor extraction system. Various segmentation techniques which will be used in this project to segregate the different regions on the basis of interest are described as follows:
As for photo editing basics, one should show an effectively illustrate the theme and elicit a reaction from the viewer, will the image move the viewer emotionally or intellectually or both? Impossible to say, no two people are alike. One always looks at the question of framing, placing images in a meaningful sequence. This is just a few of the editing questions one must ask them; there is a list for this.