Baptist Vandersmissen

Journal Article
A1
open access

Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems

Utku Özbulak, Baptist Vandersmissen, Azarakhsh Jalalvand (UGent) , Ivo Couckuyt (UGent) , Arnout Van Messem (UGent) and Wesley De Neve (UGent)

(2021) COMPUTER VISION AND IMAGE UNDERSTANDING. 202.

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Journal Article
A1
open access

Indoor human activity recognition using high-dimensional sensors and deep neural networks

Baptist Vandersmissen, Nicolas Knudde, Azarakhsh Jalalvand (UGent) , Ivo Couckuyt (UGent) , Tom Dhaene (UGent) and Wesley De Neve (UGent)

(2020) NEURAL COMPUTING & APPLICATIONS. 32(16). p.12295-12309

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PhD Thesis
open access

Augmented vision using high-dimensional sensors and deep neural networks

Baptist Vandersmissen

(2020)

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Conference Paper
P1
open access

Radar signal processing for human identification by means of reservoir computing networks

Azarakhsh Jalalvand (UGent) , Baptist Vandersmissen, Wesley De Neve (UGent) and Erik Mannens (UGent)

(2019) 2019 IEEE RADAR CONFERENCE (RADARCONF). p.1-6

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Conference Paper
P1
open access

Automated assessment of bone age using deep learning and Gaussian process regression

Tom Van Steenkiste, Joeri Ruyssinck (UGent) , Olivier Janssens (UGent) , Baptist Vandersmissen, Florian Vandecasteele, Pieter Devolder (UGent) , Eric Achten (UGent) , Sofie Van Hoecke (UGent) , Dirk Deschrijver (UGent) and Tom Dhaene (UGent)

(2018) 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). p.674-677

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Conference Paper
P1
open access

Structured inference networks using high-dimensional sensors for surveillance purposes

Vincent Polfliet, Nicolas Knudde, Baptist Vandersmissen, Ivo Couckuyt (UGent) and Tom Dhaene (UGent)

(2018) ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2018. In Communications in Computer and Information Science 893. p.16-27

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Journal Article
A1
open access

Indoor person identification using a low-power FMCW radar

Baptist Vandersmissen, Nicolas Knudde, Azarakhsh Jalalvand (UGent) , Ivo Couckuyt (UGent) , Andre Bourdoux, Wesley De Neve (UGent) and Tom Dhaene (UGent)

(2018) IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 56(7). p.3941-3952

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Conference Paper
P1
open access

Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar

Nicolas Knudde, Baptist Vandersmissen, K. Parashar, Ivo Couckuyt (UGent) , Azarakhsh Jalalvand (UGent) , A Bourdoux, Wesley De Neve (UGent) and Tom Dhaene (UGent)

(2017) 2017 EUROPEAN RADAR CONFERENCE (EURAD). In European Radar Conference EuRAD p.61-64

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Conference Paper
C1
open access

Spatio-temporal wardrobe generation of actor's clothing in video content

Florian Vandecasteele, Jeroen Vervaeke (UGent) , Baptist Vandersmissen, Michel De Wachter and Steven Verstockt (UGent)

(2016) In Lecture notes Computer Science 9733. p.448-459

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An automated end-to-end pipeline for fine-grained video annotation using deep neural networks

Baptist Vandersmissen, Lucas Sterckx (UGent) , Thomas Demeester (UGent) , Azarakhsh Jalalvand (UGent) , Wesley De Neve (UGent) and Rik Van de Walle (UGent)

(2016) ICMR'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL. p.409-412

Academic Bibliography

Baptist Vandersmissen

Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems

Indoor human activity recognition using high-dimensional sensors and deep neural networks

Augmented vision using high-dimensional sensors and deep neural networks

Radar signal processing for human identification by means of reservoir computing networks

Automated assessment of bone age using deep learning and Gaussian process regression

Structured inference networks using high-dimensional sensors for surveillance purposes

Indoor person identification using a low-power FMCW radar

Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar

Spatio-temporal wardrobe generation of actor's clothing in video content

An automated end-to-end pipeline for fine-grained video annotation using deep neural networks

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