Building Change Detection in Myanmar using Image Processing

Building change detection makes it is easy to locate buildings from a distance in the sky. They can also observe the development of rural, or urban areas between 10 decade and present. So, higher resolution satellite and aerial pictures are needed to detect buildings. Building shape varies from one to another over the world. Rural areas are sparsely populated, but densely and complexly populated in urban areas. And it is difficult to detect separate buildings from them. To solve obstacles, non-linear filter, line extracting and region thresholding method is used in this research. The test images from the last decade and images of current year are acquired by using google earth pro, and have different spatial resolutions. Detection area is Hlaingthaya Township, Yangon, Myanmar. This system is simulated with MATLAB programming language

Stability of non-linear filter for deterministic dynamics

<p style='text-indent:20px;'>This papers shows that nonlinear filter in the case of deterministic dynamics is stable with respect to the initial conditions under the conditions that observations are sufficiently rich, both in the context of continuous and discrete time filters. Earlier works on the stability of the nonlinear filters are in the context of stochastic dynamics and assume conditions like compact state space or time independent observation model, whereas we prove filter stability for deterministic dynamics with more general assumptions on the state space and observation process. We give several examples of systems that satisfy these assumptions. We also show that the asymptotic structure of the filtering distribution is related to the dynamical properties of the signal.</p>

Framework for comprehensive enhancement of brain tumor images with single-window operation

Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.

Technology of enhancing image detalization with nonlinear correction of highly gradient fragments

The article is devoted to the problem of improving the quality of images recorded using low-resolution optical instruments, including diffraction-based cameras. A two-stage image correction technology is proposed. At the first stage, the correction is carried out using a linear FIR filter with a centrally symmetric frequency response in the form of quadratic and exponential functions. The resulting image is then processed with a non-linear filter that performs computer retouching of image areas characterized by a noticeable brightness difference. This procedure is only performed on those pixels in which the absolute value of gradients in different directions is sufficiently high, that is, they are located on the borders of areas with different intensity levels. This allows us to avoid noise amplification in the background, which is typical of traditional filters. The examples of the implementation are provided, showing the possibility of achieving high sharpness and illustrating how the filter can be adjusted by visual perception.