Detection and thinning of edges in previously known directions

This work presents a study on the generation of digital masks aiming at edge detection with previously known directions. This solution is important when edge direction is available either from a direction histogram or from a prediction based on camera and object models. A modification in the non-maximum suppression method of thinning is also presented enabling the comparison of local maxima for any edge directions. Results with a synthetic image and with crops of a CBERS satellite images are presented showing an example with its application in road detection, provided that directions are previously known.

Detecção de rodovias em imagens digitais de alta resolução com o uso da teoria de morfologia matemática

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Extração de segmentos de rodovias em imagens de resoluções variadas usando o princípio de bordas paralelas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Molecular detection of Paracoccidioides brasiliensis in road-killed wild animals

Paracoccidioides brasiliensis infections have been little studied in wild and/or domestic animals, which may represent an important indicator of the presence of the pathogen in nature. Road-killed wild animals have been used for surveillance of vectors of zoonotic pathogens and may offer new opportunities for eco-epidemiological studies of paracoccidiodomycosis (PCM). The presence of P. brasiliensis infection was evaluated by Nested-PCR in tissue samples collected from 19 road-killed animals; 3 Cavia aperea (guinea pig), 5 Cerdocyon thous (crab-eating-fox), 1 Dasypus novemcinctus (nine-banded armadillo), 1 Dasypus septemcinctus (seven-banded armadillo), 2 Didelphis albiventris (white-eared opossum), 1 Eira barbara (tayra), 2 Gallictis vittata (grison), 2 Procyon cancrivorus (raccoon) and 2 Sphiggurus spinosus (porcupine). Specific P. brasiliensis amplicons were detected in (a) several organs of the two armadillos and one guinea pig, (b) the lung and liver of the porcupine, and (c) the lungs of raccoons and grisons. P. brasiliensis infection in wild animals from endemic areas might be more common than initially postulated. Molecular techniques can be used for detecting new hosts and mapping 'hot spot' areas of PCM.

Molecular detection of Leishmania spp. in road-killed wild mammals in the Central Western area of the State of São Paulo, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Georeferenced road extraction and formulation of hypotheses for new road segments

The acquisition and update of Geographic Information System (GIS) data are typically carried out using aerial or satellite imagery. Since new roads are usually linked to georeferenced pre-existing road network, the extraction of pre-existing road segments may provide good hypotheses for the updating process. This paper addresses the problem of extracting georeferenced roads from images and formulating hypotheses for the presence of new road segments. Our approach proceeds in three steps. First, salient points are identified and measured along roads from a map or GIS database by an operator or an automatic tool. These salient points are then projected onto the image-space and errors inherent in this process are calculated. In the second step, the georeferenced roads are extracted from the image using a dynamic programming (DP) algorithm. The projected salient points and corresponding error estimates are used as input for this extraction process. Finally, the road center axes extracted in the previous step are analyzed to identify potential new segments attached to the extracted, pre-existing one. This analysis is performed using a combination of edge-based and correlation-based algorithms. In this paper we present our approach and early implementation results.

Dynamic programming approach for road centerline extraction from digital images

This paper presents a dynamic programming approach for semi-automated road extraction from medium-and high-resolution images. This method is a modified version of a pre-existing dynamic programming method for road extraction from low-resolution images. The basic assumption of this pre-existing method is that roads manifest as lines in low-resolution images (pixel footprint> 2 m) and as such can be modeled and extracted as linear features. On the other hand, roads manifest as ribbon features in medium- and high-resolution images (pixel footprint ≤ 2 m) and, as a result, the focus of road extraction becomes the road centerlines. The original method can not accurately extract road centerlines from medium- and high- resolution images. In view of this, we propose a modification of the merit function of the original approach, which is carried out by a constraint function embedding road edge properties. Experimental results demonstrated the modified algorithm's potential in extracting road centerlines from medium- and high-resolution images.