Search Results for author: Rangachar Kasturi

Found 9 papers, 0 papers with code

Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment

no code implementations3 Dec 2020 Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration.

First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

no code implementations24 Mar 2020 Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain.

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

no code implementations25 Aug 2019 Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing.

A method to Suppress Facial Expression in Posed and Spontaneous Videos

no code implementations4 Oct 2018 Ghada Zamzmi, Gabriel Ruiz, Matthew Shreve, Dmitry Goldgof, Rangachar Kasturi, Sudeep Sarkar

We address the problem of suppressing facial expressions in videos because expressions can hinder the retrieval of important information in applications such as face recognition.

Face Recognition Retrieval

Neonatal Pain Expression Recognition Using Transfer Learning

no code implementations4 Jul 2018 Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun

In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning.

Face Recognition General Classification +2

Machine-based Multimodal Pain Assessment Tool for Infants: A Review

no code implementations1 Jul 2016 Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun, Terri Ashmeade

In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.

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