AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES

Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect

Clinical symptom profile of hospitalized COVID-19 Brazilian patients according to SARS-CoV-2 variants

OBJECTIVES The aim of this study was to investigate the prevalence of the main symptoms in Brazilian coronavirus disease 2019 (COVID-19) patients hospitalized during 4 distinct waves, based on their infection with different severe acute respiratory disease coronavirus 2 (SARS-CoV-2) variants. METHODS This study included hospitalized patients who tested positive for SARS-CoV-2 during 15 weeks around the peak of each of 4 waves: W1, ancestral strain/B.1 lineage (May 31 to September 12, 2020); W2, Gamma/P.1 variant (January 31 to May 15, 2021); W3, Omicron variant (December 5, 2021 to March 19, 2022); and W4, BA.4/BA.5 subvariants (May 22 to September 3, 2022). Symptom data were extracted from the Brazilian Severe Acute Respiratory Syndrome Database. Relative risks were calculated, and an analysis of symptom networks was performed. RESULTS Patients who were hospitalized during the prevalence of the Gamma/P.1 variant demonstrated a higher risk, primarily for symptoms such as fatigue, abdominal pain, low oxygen saturation, and sore throat, than patients hospitalized during the first wave. Conversely, patients who were hospitalized during the predominance of the Omicron variant exhibited a lower relative risk, particularly for symptoms such as loss of smell, loss of taste, diarrhea, fever, respiratory distress, and dyspnea. Similar results were observed in COVID-19 patients who were hospitalized during the wave of the Omicron subvariants BA.4/BA.5. A symptom network analysis, conducted to explore co-occurrence patterns among different variants, revealed significant differential profiles across the 4 waves, with the most notable difference observed between the W2 and W4 networks. CONCLUSIONS Overall, the relative risks and patterns of symptom co-occurrence associated with different SARS-CoV-2 variants may reflect disease severity

AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES

Abstract: Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect

AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES

<div><p>Abstract: Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.</p></div