The paper "Possible Ways of Improving Imaging" is a great example of a research paper on logic and programming. Imaging processing has been characterized by significant technological advancement thus leading to the development of more sophisticated and efficient imaging modalities. These technologies provide detailed useful information of an image. The most common imaging processing is edge detection which is essential in data compression, image reconstruction, and image segmentation as well as in matching. Among the image edge detection that has become the most favorable and outstanding are Sobel, canny, and filtering.
Filtering is regarded as invaluable in imaging technology because of its ability to produce images of outstanding quality non-invasively with no adverse side effects. There are several factors and conditions that must be met in order to obtain a high-quality image. When these conditions and factors are not observed, edges are detected. Edges in images are simply pixels that do not represent the real state of the image being examined. In such a case, the underlying image is visualized, but other signals that do not relate to real image at that location are also present on the image.
Although most edges have a tendency of degrading the image quality or rendering the image unusable, sometimes these edges may not be very visible and may be ignored. Thus, it is important to gain a proper understanding of edge detection, their characteristics, and how they can be either be prevented or compensated. Methodology In filtering, the image is placed in the K-space such that the data associated with low frequencies are put at the center while those with high frequencies are placed around the center.
Low-frequency signal has more information about the signal (noise) and contrast. The k-space path followed in the imaging sequence must cover all the data points in order to create a complete image. Sobel and canny imaging techniques are easier since their codes are simple to writer and they are inbuilt thus can not cause a huge edge that can distort the image quality. When carrying out edge imaging, it is important to accurately establish the Matlab code effects in order to achieve reliable image reconstruction. The coding effects of the receiver sensitivities can be determined by either calibration before starting any examination or by obtaining the data during each image acquisition.
When creating a 2-D image, significantly reduces the sampling time by minimizing the phase encoding steps. To achieve this, the distance between successive phase-encoding lines is increased, but the maximum k-space value is maintained. The reduction in a number of K-space samples relative to the full sample is described using the reduction factor R. When filter imaging techniques are used, any reduction in the k-space sampling density results in a reduction in edge hence leading to detection. Results and discussion The image that was used is that of a car number plate was acquired to edge detection. Image detection > > I = imread('D: \Documents and Settings\Administrator\My Documents\house. JPG'); this will help in importing the image to workspace figure Figure 1: Image adapted from The Home Sitter show(I); - displaying the images