Digital Security for Physical World


Innovative solutions for physical object protection developed by the SIP group.



PharmaPack: mobile fine-grained recognition of pharma packages

O. Taran, S. Rezaeifar, O. Dabrowski, J. Schlechten, T. Holotyak, S. Voloshynovskiy

We consider the problem of fine-grained physical object recognition and introduce a dataset PharmaPack containing 1000 unique pharma packages enrolled in a controlled environment using consumer mobile phones as well as several recognition sets representing various scenarios. For performance evaluation, we extract two types of recently proposed local feature descriptors and aggregate them using popular tools. All enrolled raw and pre-processed images, extracted and aggregated descriptors are made public to promote reproducible research. To evaluate the baseline performance, we compare the methods based on aggregation of local descriptors with methods based on geometrical matching.

@inproceedings { Taran:2017:EUSIPCO,
    author = { Olga Taran and Shideh Rezaeifar and Oscar Dabrowski and Jonathan Schlechten and Taras Holotyak and Slava Voloshynovskiy },
    title = { PharmaPack: mobile fine-grained recognition of pharma packages },
    booktitle = { European Signal Processing Conference (EUSIPCO) },
    month = { 28 August - 2 September },
    year = { 2017 },
    address = { Kos, Grece }

Fine-grained identification of pharmaceutical products based on local descriptors

Oscar Dabrowski, Prof. Sviatoslav Voloshynovskiy, Dr. Taras Holotyak

In this work, the problem of fine-grained identification of pharmaceutical products is addressed. A large database of images of pharma packages acquired by modern mobile phones was created. The contribution of this thesis encompasses the development of 2 different approaches based on local descriptors and the investigation of the identification performance of these methods. Moreover, our contribution entails the setup and creation of a new database of images suitable for testing fine-grained recognition algorithms, which we hope can benefit to the computer vision community.