In this Master's project increasing the performance of object detection inforestry environment, based on the extracted features is studied. There areseveral object detection projects for robots which are based on feature cal-culation and extraction. An example of these kinds of projects is the sugarbeet project [3] that has inspired the feature selection and calculations partspresented in this report. Extracted feature sets are given to several classiersand their results are merged and fused such that the overall performance ofthe forestry object detection increases. Furthermore dierent supervised andunsupervised methods of dimensionality reduction are applied on the featureset as an approach to improve classication accuracy. Comparison betweenthe output classication performance of dimensionality reduction methodsshow that applying supervised methods result in improving the classicationperformance by about 12 percent.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-93528 |
| Date | January 2011 |
| Creators | Ostovar, Ahmad |
| Publisher | Umeå universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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