Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2

Background: e cerebellum is divided into two hemispheres and contains a narrow midline zone called thevermis. A set of large folds are conventionally used to divide the overall structure into ten smaller "lobules". evermis receives fibres from the trunk and proximal portions of limbs, But the question is that does the cerebellum have the same measurementvalues in males and females of the same age?Material and method: e present study used 80 sectional brain MRI images (40: males, 40: females); 35-50 years old as indices of size for thevermian structures of the Cerebellum. is middle age group was taken because as known generally it could be neither an age of growth as inthe young nor of atrophy as in old individuals. e aim rega

This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

Aspect categorisation and its utmost importance in the eld of Aspectbased Sentiment Analysis (ABSA) has encouraged researchers to improve topic model performance for modelling the aspects into categories. In general, a majority of its current methods implement parametric models requiring a pre-determined number of topics beforehand. However, this is not e ciently undertaken with unannotated text data as they lack any class label. Therefore, the current work presented a novel non-parametric model drawing a number of topics based on the semantic association present between opinion-targets (i.e., aspects) and their respective expressed sentiments. The model incorporated the Semantic Association Rules (SAR) into the Hierarchical Dirichlet Proce

The aim of this study was to identify the rate of return of the stock through the financial information disclosed by the financial statements of companies both services and insurance included in Iraqi market for securities . The study used a descriptive statistical methods and the correlation matrix for the independent factors , in addition to a regression model for data  analysis and hypothesis . Model included a number of independent variables , which was measured in the size of company (sales or revenue) , and the leverage , in addition to the structure of assets and the book value of owners'  equity in the company , as well as the general price index .Based on the data of (11)companies and for three years, showed the result

 Abstract 

The vegetative filter strips (VFS) are a useful tool used for reducing the movement of sediment and pesticide in therivers. The filter strip’s soil can help in reducing the runoff volume by infiltration. However, the characteristics of VFS (i.e., length) are not recently identified depending on the estimation of VFS modeling performance. The aim of this research is to study these characteristics and determine acorrelation between filter strip length and percent reduction (trapping efficiency) for sediment, water, and pesticide. Two proposed pesticides(one has organic carbon sorption coefficient, K_oc, of 147 L/kg which is more moveable than XXXX, and another one