Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Microscopic Image Analysis of Robot Generated Use Wear Data
Use-wear analysis in archaeology tries to infer tool function from microscopic wear traces found on historic tools. Stone tool replicas worn by a robot arm serve as a reference for the real findings. In this project image analysis protocols for wear quantification are developed.
The RoboCut project is a collaboration between the Agile & Dexterous Robotics Lab (ADRL) and researchers from MONREPOS Archaeological Research Centre and Museum for Human Behavioural Evolution in Germany and New York University. We study prehistoric human tasks through use-wear traces generated on stone tool replicas with a robot arm. Typical tasks are scraping of hides and wood, or sawing. The traces generated serve as a reference for real tools from archaeological sites.
Experimental stone samples are imaged with a focus variation microscope yielding topographic and RGB images of areas as small as 100um x 100um. Topographic images are very well suited for quantitative, 3D analysis of surfaces. However, due to intrinsic limitations of the microscope some wear features of our stone samples cannot be resolved on those images. Therefore, we want to incorporate information from the RGB image in the analysis process.
The RoboCut project is a collaboration between the Agile & Dexterous Robotics Lab (ADRL) and researchers from MONREPOS Archaeological Research Centre and Museum for Human Behavioural Evolution in Germany and New York University. We study prehistoric human tasks through use-wear traces generated on stone tool replicas with a robot arm. Typical tasks are scraping of hides and wood, or sawing. The traces generated serve as a reference for real tools from archaeological sites. Experimental stone samples are imaged with a focus variation microscope yielding topographic and RGB images of areas as small as 100um x 100um. Topographic images are very well suited for quantitative, 3D analysis of surfaces. However, due to intrinsic limitations of the microscope some wear features of our stone samples cannot be resolved on those images. Therefore, we want to incorporate information from the RGB image in the analysis process.
The student's task would be first, to conduct a literature study on adequate image analysis algorithms that can be helpful for the quantification of use-wear features. In a second stage implementation and testing of the algorithms on our test data set will be done. Finally, the student has the possibility to work on advanced, e.g. learning based methods and test algorithms on new image data.
The student's task would be first, to conduct a literature study on adequate image analysis algorithms that can be helpful for the quantification of use-wear features. In a second stage implementation and testing of the algorithms on our test data set will be done. Finally, the student has the possibility to work on advanced, e.g. learning based methods and test algorithms on new image data.
- Supervision of SA project
- Testing of image processing algorithms on a unique image database
- Work on an interdisciplinary project in the interface of robotics, microscopy and anthropology
- Possibility for a publication in an anthropological journal (subject to quality of results)
- Supervision of SA project - Testing of image processing algorithms on a unique image database - Work on an interdisciplinary project in the interface of robotics, microscopy and anthropology - Possibility for a publication in an anthropological journal (subject to quality of results)
- Master student in mechanical, electrical engineering, robotics, computer science or similar.
- Good knowledge of image processing methods
- Hands on experience with image processing tools (e.g. MATLAB, ImageJ, openCV)
- Basic knowledge in microscopy a plus
- Master student in mechanical, electrical engineering, robotics, computer science or similar. - Good knowledge of image processing methods - Hands on experience with image processing tools (e.g. MATLAB, ImageJ, openCV) - Basic knowledge in microscopy a plus