The present study aimed to develop a theoretical framework toward e-learning adoption in universities through conducting a comprehensive review of literature, and empirical studies. Based on the technology acceptance model, the researchers developed a model designed to measure the impact of ease of use, perceived usefulness, and training on e-learning usage on the actual use of e-learning systems via the initial trend towards the use of e-learning systems and the intention towards the use of e-learning systems as mediating factors. The researchers also developed a survey questionnaire that will be distributed to a selected sample study on the students at the Jordanian public and private universities in order to answer the research questions and test its hypotheses by applying structural equation modeling.

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Scale-Invariant Feature Transform is an image matching algorithm used to match objects of two images by extracting the feature points of target objects in each image. Scale-Invariant Feature Transform suffers from long processing time due to embedded calculations which reduces the overall speed of the technique. This research aims to enhance SIFT processing time by imbedding Chamfer Distance Algorithm to find the distance between image descriptors instead of using Euclidian Distance Algorithm used in SIFT. Chamfer Distance Algorithm requires less computational time than Euclidian Distance Algorithm because it selects the shortest path between any two points when the distance is computed. To validate and evaluate the enhanced algorithm, A data set with (412) images including: (100) images with different degrees of rotation, (100) images with different intensity levels, (112) images with different measurement levels and (100) distorted images to different degrees were used; these images were applied according to four different criteria. The simulation results showed that the enhanced SIFT outperforms the ORB and the original Scale-Invariant Feature Transform in term of the processing time, and it reduces the overall processing time of the classical SIFT by (41%).

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