A Fast Back-Projection SAR Imaging Algorithm Based on Wavenumber Spectrum Fusion for High Maneuvering Platforms

In this paper, a fast back-projection (BP) synthetic aperture radar (SAR) imaging algorithm based on wavenumber spectrum fusion is proposed for high maneuvering platforms with high squint angles and high dive angles. When the platforms have nonlinear trajectory errors, the trajectory errors will introduce significant spectrum displacement, which varies with the target position. If the influence of trajectory error is not considered, the trajectory’s deviation from the ideal SAR imaging geometry will degrade the focusing performance. The dive angle will further enlarge the influence on SAR focusing. In this paper, the center function of the spatially variable sub-aperture wavenumber spectrum is derived, firstly, which is the basis for reasonably setting the oversampling rate. Then, the accurate spectrum fusion method is proposed, which is efficiently implemented by fast Fourier transform (FFT) and circular shifting. The proposed algorithm is called high-squint and high-dive accelerated factorized back-projection (HSHD-AFBP). Compared to the commonly used spectrum fusion method, HSHD-AFBP not only improves the imaging quality, but also expands the image width. Finally, the effectiveness of the proposed algorithm is verified by simulation data.

Penentuan Jenis Tanaman Berdasarkan Kemiringan Lahan Pertanian Menggunakan Adopsi Linier Programming Berbasis Pengolahan Citra

The slope is a measure of the slope of the land relative to the flat plane which is generally expressed in percent or degree. Agricultural land that has a slope of more than 15 ° can be more easily damaged. In this study, a value of 2.5 ° was used in determining the normal slope. The process of implementing a decision support system used linear programming adoption in determining the slope angle of the land, rotation of the slope of the land and determining whether or not the normalization of the input image was necessary or not. The purpose of this research is to determine the time needed to determine the type of plant based on rotation or the angle of setting determined by the user. The results of the study are the greater the height and width of an object the longer the time needed to determine the type of plant, land price or slope of the land. The results of the study were obtained in five tests. Tests 1 and 2 images with height and image width ranged from 150-480 obtained time for normalization of 15-30, testing 3.4.5 images with height and image widths ranging from 322 and 788 requiring 54- 108. Keywords: Slope, Adopting Linear Programming, Decision Support Systems

Retinal Image Vessel Width Analysis in Retinopathy of Prematurity

This paper aims at development of Oscillating Pendulum Based Algorithm (OPBA) for retinal image width computation and analysis for the investigation of Retinopathy of prematurity (ROP). The algorithms have been explained in detail with theoretical investigations and simulations, images from local hospital data base were considered in the investigation. Further this paper also presents an overview of direct retinal vessel width computation method along with the comparative analysis. The results obtained are found to be encouraging for analysis of plus disease under ROP.

Boundary Extraction and Vessel Width Calculation in Retinal Fundus Images

A retinal vessel width measurement algorithm is presented towards ROP (Retinopathy of Prematurity) plus diagnostic automation. The algorithm involving geometrical feature extraction with the image processing is used to compute the effective width of the major vessels in a retinal image. Width measurement is shown to be a statistical parameter estimation related to the statistics of the retinal information. The algorithm is applied to the generic data bases available and the results are found to be satisfactory with ophthalmologist opinion. The effectiveness of the algorithm depends on the fundus image capturing settings.

Proposed algorithm for image classification using regression-based pre-processing and recognition models

<span>Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.</span>

A Sampling-Agnostic Software Framework for Converting Between Texture Map Representations of Virtual Environments

We have developed a utility to both stitch cube maps into other types of texture maps (equirectangular,dual paraboloid, and octahedral), and stitch those other types back into cube maps. The utility allows for flexibility in the image size of the conversion - the user can specify the desired image width, and the height is computed (cube, paraboloid, and octahedral mappings are square, and spherical maps are generated to have 16:9 aspect ratio). Moreover, the utility is sampling-agnostic, so the user can select whether to use uniform or jittered sampling over the pixels, as well as the number of samples to use per pixel. The rest of this paper discusses the mathematical framework for projecting from cube maps to equirectangular, dual paraboloid, and octahedral environment maps, as well as the mathematical framework for the inverse projections. We also describe two sampling techniques: uniform sampling and correlated multi-jittered sampling. We perform an evaluation of the sampling techniques and a comparative analysis of the different projections using objective image quality assessment metrics.

Monitoring of End-Mill Behavior with Its Projection Image

End mills with small diameter have found their wide application with the development of high-speed cutting. It becomes more and more important to develop effective methods to monitor and control the milling process with small end mills. In this paper, a measuring system of projection image for small end mills is introduced, and the application of the projection image to monitor the behavior of the end mill is discussed. It is found that for static state of the end mill, the measurement accuracy can be easily controlled within 1 μm. When the end mill rotates, it is not so difficult to control the accuracy within 3 μm. By using of the change in image width, the radial wear of end mill can be predicted. On the other hand, if the centre shift of the image is pre-measured, the deflection of the end mill during cutting can be predicted.