The automated analysis of digital underwater images requires complex approaches of machine learning. Central problems are the detection of objects as well as the subsequent semantic classification of plants, animals or mineral resources.
An interest in data mining and/or image analysis is required and skills in efficient coding are beneficial but not mandatory. We target high-throughput solutions and the hundreds of thousands of images to be analysed require utilization of state of the art machine learning libraries. Computational speedup can be achieved through GPU enabled algorithms or parallelization.
Big data challenges the ways we think about data, analyze data and handle data. We in the Biodata Mining Group follow a collaborative strategy and develop web-based software tools and libraries for cooperative data analysis. Tools are designed, developed and operated to handle multi-dimensional data from various fields like marine environments or tissue samples.
The tool development includes novel visualization strategies, efficient methods of data retrieval, fusion of different data types and much else. We utilize on modern web technologies and frameworks such as Laravel or Vue.js.