It is quite alarming that the increase of glaucoma is due to the lack of awareness of the disease and the cost for glaucoma screening. The primary eye care centers need to include a comprehensive glaucoma screening and include machine learning models to elaborate it as decision support system. The proposed system considers the state of art of eye gaze features to understand cognitive processing, direction and restriction of visual field. There is no significant difference in global and local ratio and skewness value of fixation duration and saccade amplitude, which suggest that there is no difference in cognitive processing. The significance value of saccadic extent along vertical axis, Horizontal Vertical ratio (HV ratio), convex hull area and saccadic direction show that there is restriction in vertical visual field. The statistical measures (p < 0.05) and Spearman correlation coefficient with class label validate the results. The proposed system compared the performance of seven classifiers: Naïve Bayes classifier, linear and kernel Support Vector classifiers, decision tree classifier, Adaboost, random forest and eXtreme Gradient Boosting (XGBoost) classifier. The discrimination of eye gaze features of glaucoma and normal is efficiently done by XGBoost with accuracy 1.0. The decision support system is cost-effective and portable.
The objective is to remove rain from videos without blurring the object. The algorithm helps to devise the system which removes rain from videos to facilitate video surveillance, and to improve the various visionbased algorithms. Rain is a noise that impairs videos and images. Such weather conditions will affect stereo correspondence, feature detection, segmentation, and object tracking and recognition. In video surveillance if any problem is found due to weather conditions the object cannot be tracked well. In this paper we have considered only rain falling in static environment, i.e., the object is not moving.
Glaucoma is a type of visual impairment that is caused due to damage in the optic nerve. The vision loss increases from the peripheral vision towards the central vision, leading to blindness if untreated. The proposed approach is a Computer-Aided Detection (CADe) system using deep learning to screen visual field loss in glaucoma patients while performing different day-to-day activities such as searching objects, viewing photographs, etc. Incorporating an eye-tracking device helps to identify eye movements of glaucoma patients while performing different activities. Different day-to-day activities are depicted in the form of visual exploration tasks. CADe system fuses performance parameters and eye gaze parameters during visual exploration tasks onto images, to guide health care professionals of primary eye care centers in glaucoma screening. The pertinent eye gaze and performance parameters are visualized in the form of three fusion maps: Gaze Fusion Map (GFM), Gaze Fusion Reaction Time (GFRT) map, Gaze Convex Hull Map (GCHM), which are the outcomes of different visual exploration tasks. In addition, the explainability techniques applied in CADe generated Gaze Exploration -index (GE-i) that discriminates glaucoma and normal.
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