Forms Of Image Analysis Research Articles

Meningkatkan Aktivitas dan Hasil Belajar PKn melalui Value Clarification Technique Siswa Kelas V SDN 12 Gedong Tataan, Pesawaran Tahun Pelajaran 2022/2023

This classroom action research started from a background of low activity and learning outcomes for Civics students in grade V SDN 12 Gedong Tataan, Gedong Tataan District. This study uses a learning model approach to act with value clarification technique (VCT) Image Analysis. The research conducted at SDN 12 Gedong Tataan, Gedong Tataan District, aims to increase the activities and learning outcomes of Civics students in grade V and to increase the creativity and performance of teachers in learning activities. This study used a reflective and collaborative classroom action research design carried out in 3 cycles. The data were obtained through observing student activities and learning outcomes in each cycle which were analyzed descriptively quantitatively. The results of the study can be concluded that the use of the learning approach to act in this type of value clarifying technique or Value Clarification Technique of Value Analysis in the form of image analysis can increase the activities and learning outcomes of Civics class V students at SD Negeri 12 Gedong Tataan, Gedong Tataan District in 2022

On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling

Steel bar manufacturing process is a multistage process in which melted steel passes through multiple steps to form into a bar or rod of varying diameters. The most common cause of rejection of such products is the surface defects such as seams, scales, cracks etc. Often automated inspection in the form of image analysis is done to predict the formation of such defects. These inspections suffer from the fact that they do not include the process information and hence are not very reliable. This paper presents a novel methodology of prediction of surface defects in bar manufacturing. In this method, the surface images obtained by inspection methodology are decomposed into individual defects based on process knowledge. These are then decomposed into the physical quantities affecting them which are decomposed into design parameters on rolling mill. To achieve this decomposition, Bayesian hierarchical modeling is used. Data collected from sensors installed on rolling mill are used for model building and design. After the model is built, it is converted into an automated system called Diagnostics on Rolling Mill (DORM). DORM is installed on rolling mill and its performance is evaluated for several days. It is found that the model performs very closely with manual inspection and predicts surface defects with good accuracy. The Bayesian model also gives an insight into process parameters and physical quantities which affect the individual defects.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

Fast automated mosaic synthesis method for 2-D/3-D image analysis of specimens much wider than the field of view

We present an efficient, robust, and widely-applicable technique for computational synthesis of wide-area images from a series of overlapping partial views. The synthesized image is the set union of the areas covered by the partial views, and is called the “mosaic”. One application is the laser-scanning confocal microscopy of specimens that are much wider than the field of view of the microscope. Another is imaging of the retinal periphery using a standard fundus imager. This technique can also be used to combine the results of various forms of image analysis, such as cell counting and neuron tracing, to generate large representations that are equivalent to processing the total mosaic, rather than the individual partial views.The synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the application. For instance, in the retinal imaging application, the vascular branching and crossover points are a natural choice. Likewise, the locations of cells in Figs. 1 and 2 provide a natural set of landmarks for joining these images.

Digitizing outlines of fossil specimens using video frame capture

Recently developed techniques for the analysis of biological shapes require rapid digitization of specimen outlines. A technique is described for automated capture of coordinates of fossil specimen outlines using an inexpensive video frame-capture digitizer. The method is applicable to specimens that can be silhouetted against a background, and can easily be adapted to other forms of image analysis.