Project

Topic Description: Camera traps are widely used in wildlife monitoring, but automated species recognition remains challenging due to low image quality, occlusions, and morphological similarities between species. Most existing approaches rely on single-frame classification, disregarding valuable t...

Video-based animal re-identification has the potential to outperform image-based approaches by leveraging not only spatial but also temporal features. However, progress in this field is severely hindered by the lack of publicly available datasets. To address this, our group has developed one of the ...

Depression is one of the most common mental health disorders, significantly reducing an individual’s quality of life. Psychologists use various methods to detect and assess depression, such as analysing facial expressions, speech patterns, and head movements. In this research, our goal is to detect ...

Background Current our project relies only on raw time-series IMU data for word/sentence-based online handwriting recognition. However, handwriting motion contains patterns that may be more distinguishable in the frequency domain. By converting IMU signals into spectrograms, we capture both tempora...

Description:  This project focuses on developing a deep learning model that accurately segments tissues in a wound by incorporate prior knowledge about the wound structure using spatial priors. This targeted solution aims to streamline the tissue segmentation process, ultimately contributing to the...

Description:  This project focuses on developing a deep learning model that accurately segments tissues in a wound using super pixel based image segmentation algorithms. This targeted solution aims to streamline the tissue segmentation process, ultimately contributing to the SWODDYS project's large...