Age-related macular degeneration (AMD) is a degenerative ocular disease that develops by the formation of drusen in the macula region leading to blindness. This condition can be detected automatically by automated image processing techniques applied in spectral domain optical coherence tomography (SD-OCT) volumes. The most common approach is the individualized analysis of each slice (B-Scan) of the SD-OCT volumes. However, it ends up losing the correlation between pixels of neighboring slices. The retina representation by topographic maps reveals the similarity of these structures with geographic relief maps, which can be represented by geostatistical descriptors. In this paper, we present a methodology based on geostatistical functions for the automatic diagnosis of AMD in SD-OCT. The proposed methodology is based on the construction of a topographic map of the macular region. Over the topographic map, we compute geostatistical features using semivariogram and semimadogram functions as texture descriptors. The extracted descriptors are then used as input for a Support Vector Machine classifier. For training of the classifier and tests, a database composed of 384 OCT exams (269 volumes of eyes exhibiting AMD and 115 control volumes) with layers segmented and validated by specialists were used. The best classification model, validated with cross-validation k-fold, achieved an accuracy of 95.2% and an AUROC of 0.989. The presented methodology exclusively uses geostatistical descriptors for the diagnosis of AMD in SD-OCT images of the macular region. The results are promising and the methodology is competitive considering previous results published in literature.

This paper presents a methodology for the development of augmented reality (AR) visualization applications in industrial scenarios. The proposal presents the use of georreferenced natural markers detected in real time, which enables the construction of AR systems. This use of augmented visualization allows the creation of tools that can aid on-site maintenance activities for operators. AR use makes possible including information about the equipment during a specific procedure. In this work, the detection of natural markers in the scene are based on Haar-like features associated with equipment geolocalization. This approach enable the detection of equipment in multiple user’s viewpoints in the industrial scenario and makes it possible the inclusion of real information about those equipment in real time as AR annotations. In this way, beyond a methodology approach, this paper presents a new way for Power System information visualization in the field that can be used in both for training and for control operations.