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In this paper we propose a new method for the detection and evaluation of exudates in retinal images. In the learning phase we focus on selecting efficient features that can uniquely identify the exudates. In this process we develop a neural network for image processing. We then further extend and train the neural network to detect and evaluate the exudates. The efficiency of the proposed method is...
The paper presents a new method for detecting and localizing regions of interest (ROI) in retinal images. In the learning phase, the focus is on an efficient feature selection based on K-means clustering of feature values and on artificial neural networks (ANNs) for image processing and textural features computation. Finally, a voting scheme identifies the regions of interest. The experiments were...
The paper presents a new method for the detection of exudates and hemorrhages in eye-fundus images using a voting scheme for the selection of efficient features. Also it was used a new algorithm for feature selection based on a statistical indicator of performance. The used features include statistical, textural and fractal characteristics of the corresponding areas in the eye-fundus images. A combination...
In this paper we developed an intelligent method for the selection of statistical, textural and fractal features that characterize different regions of interest in eye-fundus images. Because the regions like optic disc, macula, exudates and hemorrhages are difficult to detect, an intelligent scheme for feature detection and classification is necessary. The method is based on a voting scheme that takes...
In this paper we propose a methodology for detection, localization, segmentation and size evaluation of flood areas from aerial images which are taken with drones. The approach is based on sliding box method and texture features analyses. The process of feature selection takes into account a performance degree obtained from false positive and false negative cases. We combined different properties...
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