@article{scholars8102,
             doi = {10.1186/s13640-017-0212-3},
          number = {1},
           title = {Quantitative comparison of motion history image variants for video-based depression assessment},
          volume = {2017},
            note = {cited By 13},
         journal = {Eurasip Journal on Image and Video Processing},
            year = {2017},
       publisher = {Springer International Publishing},
        keywords = {Classification (of information); Image processing; Learning systems; Neural networks, Affective Computing; Depression assessment; Facial images; Facial landmark; Motion history images, Video streaming},
          author = {Pampouchidou, A. and Pediaditis, M. and Maridaki, A. and Awais, M. and Vazakopoulou, C.-M. and Sfakianakis, S. and Tsiknakis, M. and Simos, P. and Marias, K. and Yang, F. and Meriaudeau, F.},
            issn = {16875176},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029085354&doi=10.1186\%2fs13640-017-0212-3&partnerID=40&md5=dcb9ebb1cff424e3d8e6d1668ae60349},
        abstract = {Depression is the most prevalent mood disorder and a leading cause of disability worldwide. Automated video-based analyses may afford objective measures to support clinical judgments. In the present paper, categorical depression assessment is addressed by proposing a novel variant of the Motion History Image (MHI) which considers Gabor-inhibited filtered data instead of the original image. Classification results obtained with this method on the AVEC{\^a}??14 dataset are compared to those derived using (a) an earlier MHI variant, the Landmark Motion History Image (LMHI), and (b) the original MHI. The different motion representations were tested in several combinations of appearance-based descriptors, as well as with the use of convolutional neural networks. The F1 score of 87.4 achieved in the proposed work outperformed previously reported approaches. {\^A}{\copyright} 2017, The Author(s).}
}