| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | van Ginneken, B. | |
| dc.contributor.author | Kaspers, A. | |
| dc.date.accessioned | 2011-05-05T17:00:36Z | |
| dc.date.available | 2011-05-05 | |
| dc.date.available | 2011-05-05T17:00:36Z | |
| dc.date.issued | 2011 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/6944 | |
| dc.description.abstract | For many high vision purposes, detecting low-level objects in an image is of great importance. These objects, which can be 2D or 3D, are called blobs. Blobs appear in different ways depending on their scale and can be detected using local operations in a multi-scale representation of the image. This paper describes several blob detection methods and applications and tries to make a fair comparison without performing experiments. It shows that blobs can be defined and localized in different ways and that each method has its own strength and shortcomings. | |
| dc.description.sponsorship | Utrecht University | |
| dc.format.extent | 1439748 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | |
| dc.title | Blob Detection | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | blob, detection, scale-space, interesting point | |
| dc.subject.courseuu | Biomedical Image Sciences | |
