Name
Abstract | ||
|---|---|---|
Remote sensing images with large spatial dimensions are usual. Besides, they also include a diversity of spectral channels, increasing the volume of information. To obtain valuable information from remote sensing data, computers need higher amounts of memory and more efficient processing techniques. The first process in image analysis is segmentation, which identifies regions in images. Therefore, segmentation algorithms must deal with large amounts of data. Even with current computational power, certain image sizes may exceed the memory limits, which ask for different solutions. An alternative to overcome such limits is to employ the well-known divide and conquer strategy, by splitting the image into chunks, and segmenting each one individually. However, it arises the problem of merging neighboring chunks and keeping the homogeneity in such regions. In this work, we propose an alternative to divide the image into chunks by defining noncrisp borders between them. The noncrisp borders are computed based on Dijkstra algorithm, which is employed to find the shortest path between detected edges in the images. By applying our method, we avoid the postprocessing of neighboring regions, and therefore speed up the final segmentation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-02895-8_45 | ACIVS |
DocType | Volume | ISSN |
Conference | 8192 | 0302-9743 |
Citations | PageRank | References |
3 | 0.73 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thales Sehn Körting | 1 | 3 | 2.42 |
| Emiliano Ferreira Castejon | 2 | 8 | 3.08 |
| Leila Maria Garcia Fonseca | 3 | 47 | 17.89 |