https://elcvia.cvc.uab.es/issue/feed ELCVIA Electronic Letters on Computer Vision and Image Analysis 2021-07-21T13:39:15+02:00 Electronic Letters on Computer Vision and Image Analysis elcvia@cvc.uab.cat Open Journal Systems Electronic Journal on Computer Vision and Image Analysis https://elcvia.cvc.uab.es/article/view/v20-n2-anthwal Modelling and Analysis of Facial Expressions Using Optical Flow Derived Divergence and Curl Templates 2021-04-18T16:00:04+02:00 Shivangi Anthwal shivangianthwal@hotmail.com Facial expressions are integral part of non-verbal paralinguistic communication as they provide cues significant in perceiving one’s emotional state. Assessment of emotions through expressions is an active research domain in computer vision due to its potential applications in multi-faceted domains. In this work, an approach is presented where facial expressions are modelled and analyzed with dense optical flow derived divergence and curl templates that embody the ideal motion pattern of facial features pertaining to unfolding of an expression on the face. Two types of classification schemes based on multi-class support vector machine and k-nearest neighbour are employed for evaluation. Promising results obtained from comparative analysis of the proposed approach with state-of-the-art techniques on the Extended Cohn Kanade database and with human cognition and pre-trained Microsoft face application programming interface on the Karolinska Directed Emotional Faces database validate the efficiency of the approach. 2021-06-01T00:00:00+02:00 Copyright (c) 2021 Shivangi Anthwal https://elcvia.cvc.uab.es/article/view/1220 Accuracy improvement of the inSAR quality-guided phase unwrapping based on a modified PDV map. 2021-07-21T13:39:15+02:00 Tarek Bentahar tarek.bentahar@univ-tebessa.dz <p class="AbstractBodytext"><span lang="EN-GB">In this paper, an accuracy improvement of the quality-guided phase unwrapping algorithm is proposed. Our proposal is based on a modified phase derivative variance which provides more details on local variations especially for important patterns such as fringes and edges, hence distorted regions may be re-unwrapped according to this new reliable PDV. The proposed improvement is not only effective on accuracy but also on time, the obtained results have shown that the running time with our proposal is less than that of a skillful optimization-based algorithm. To prove effectiveness, the experimental test is carried out on simulated and real data, and the comparison is made under several relevant criteria.<strong></strong></span></p> 2021-08-18T00:00:00+02:00 Copyright (c) 2021 Tarek Bentahar