Assessment of the risk for development of age-related macular degeneration (ARMD) requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs for the latter are the so-called drusen that appear as abnormal white-yellow deposits on the retina. Colour retinal images are used presently to visually identify the presence of drusens. Segmentation of these features using conventional image analysis methods is quite complicated mainly due to the non-uniform illumination and the variability of the pigmentation of the background tissue. Automated detection and analysis can provide vital information about the quantity and quality of the drusens. In this paper, we report on two methods that we have developed to reliably detect and count drusens. The methods exploit the morphological characteristics of the drusens such as texture and their 3D profiles. We compare the results of using these two methods and make recommendations for automated drusen analysis.
A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.
The effectiveness of an e-learning system for distance education to a large extent depends on the relevancy and presentation of learning content to the learner. The ability to gather documents on a particular topic from the web and adapt the contents of the document to suit the learner is an important task from the content creation perspective of e-learning. For the developer of e-learning material the provision to automatically extract, organize, and present content material would improve its effectiveness. This paper proposes to extract information from documents using language processing techniques and organizing the content into appropriate presentation slides for learning purposes using domain ontology and learning oriented pedagogy ontology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.