Object Detection Proposals

Evaluation of Image Processing Algorithms for Detecting Airborne Fungal Spores in Microscopic Images

full documentation, results, and source codes can be found via this link and this link.

Abstract

The present research project studies the application of software algorithms for detecting object proposals in digital images based on their graphical characteristics. The fast generation of accurate object proposals is widely seen as a promising solution to the efficiency problems of heavy classification and object recognition algorithms, which require long time and huge computational power when applied on entire images. Instead, object detection proposals (ODPs) with smaller sizes and focused content can provide efficient input for such classification algorithms.

The research provides a systematic review and benchmarking of the state-of-the-art ODP algorithms. It further introduces a novel algorithm “Smart-Superpixels”, specifically developed for the purpose of airborne fungal spores’ detection in microscopic images. The benchmarking considers several qualitative and quantitative criteria regarding the algorithms speed, spatial efficiency, recall accuracy, localization precision and redundancy.

The benchmarking results show that the introduced algorithm: Smart-Superpixels has the highest overall performance with its fast operation, relatively high spatial efficiency, high recall accuracy and localization precision, as well as low redundancy.

Project Aim

To provide a systematic benchmarking of state-of-the-art object detection proposal (ODP) algorithms and find the best method for airborne fungal spores’ detection.