YONA FALINIE A. GAUS
I am a post-doctoral research associate at Department of Computer Science, Durham University working with Prof. Toby Breckon. I work at the intersection of image processing, computer vision, image understanding and machine learning. My research revolves around topics such as object detection, object recognition, object tracking, anomaly detection, in multiple modalities (visible, thermal, xray, drones) imagery.
I publish my research in the computer vision conference where I am also an active reviewer. Recently, I co-organized workshops on anomaly detection (VAND@CVPR2024) in CVPR 2024.
E-Mail Google Scholar Twitter LinkedInYona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina, Toby P. Breckon
TL;DR: access the effectiveness of SAM via three different prompting strategies applied on X-ray security and thermal imagery
arXiv pdf code project page
Abril Corona-Figueroa, Sam Bond-Taylor, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon, Hubert P. H. Shum, Chris G. Willcocks
TL;DR: a simple and novel 2D to 3D synthesis via transformers, where discrete compressed space allows for both fast and high-resolution image synthesis and can be trained independently without requiring aligned datasets
arXiv pdf poster video project page
Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina, Hubert P. H. Shum, Amir Atapour-Abarghouei, Toby P. Breckon
TL;DR: leveraging both visual appearance and localized motion characteristics, derived from optic flow for anomaly detection under thermal imagery
arXiv pdf video poster
Jack W. Barker, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
TL;DR: simple approach where DAE is optimised to reconstruct the denoised input from the noised input,vastly improve performance on 'leave-one-out' and challenging anomaly detection tasks
arXiv pdf
Neelanjan Bhowmik, Jack W. Barker, Yona Falinie A. Gaus, Toby P. Breckon
TL;DR: re-training models on lossy JPEG compression notably ameliorated performances, tiny and small objects are more sensitive to compression than medium and large object
arXiv pdf video
Thomas W. Webb, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
TL;DR: retraining the networks on lossy compression reduce storage overhead for deployment within X-ray scanner applications
arXiv pdf video
Joyal Raju, Yona Falinie A. Gaus, Toby P. Breckon
TL;DR: investigate the SOTA deep learning and its variants to bridge the gap across different culture in emotion prediction
arXiv pdf video
Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
TL;DR: training using combination of RGB with energy response and effective-z gives maximal performance over 6-class prohibited item detection problem
arXiv pdf poster video
Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K.S. Isaac-Medina, and Toby P. Breckon
TL;DR: leveraging prior training on visible-band image datasets, and then subsequently only requiring a secondary, smaller volume of infrared-band imagery for CNN model fine-tuning
arXiv pdf video
Neelanjan Bhowmik, Yona Falinie A. Gaus, Samet Akcay, Jack W. Barker, Toby P. Breckon
TL;DR: demonstrates the superiority of a varying segmentation (object and sub-component) plus fine-grain CNN architectures
arXiv pdf video
Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akcay, Toby P. Breckon
TL;DR: exploration of CNN transferability between X-ray scanner with differing geometric, resolution and colour profile, generate adversarial dataset for firearms detection
arXiv pdf poster video
Yona Falinie A. Gaus, Neelanjan Bhowmik, Toby P. Breckon
TL;DR: evaluate CNN object detection over two-cass prohibited item detection in X-ray imagery, one-stage detector offer low FP while two-stage achieves high TP
arXiv pdf poster
Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
TL;DR: offers the automatic first-stage screening of aviation baggage for anomalous electronic item detection at the component level as an indicator of potential threat presence
arXiv pdf
Neelanjan Bhowmik, Qian Wang, Yona Falinie A. Gaus, Marcin Szarek, Toby P. Breckon
TL;DR: generate high-quality synthetically composited X-ray images using TIP approach, extensive comparison on how real and synthetic X-ray affects the performance of CNN architecture
arXiv
Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akcay, Paolo M. Guillen-Garcia, Jack W. Barker, Toby P. Breckon
TL;DR: combine CNN object and fine-grained approaches for object anomaly detection problem, FP remains a significant issue
arXiv pdf
Yona Falinie A. Gaus
TL;DR: summary of my work prior to 2018, learning continuous emotion recognition using feature representation and machine learning strategy.
thesis
Yona Falinie A Gaus, Hongying Meng
TL;DR: combine audio, video and text by quantify the relationship within these modality via multimodal fusion approaches,
arXiv pdf
Yi Liu, Hongying Meng, Mohammad Rafiq Swash,Yona Falinie A. Gaus,Rui Qin
TL;DR: unique holoscopic 3D micro-gesture database (HoMG) which supports robust 3D depth micro lens array is created.
arXiv dataset pdf
Asim Jan, Hongying Meng, Yona Falinie A. Gaus, Fan Zhang
TL;DR: feature extraction via deep learning on audio and video then fused together to capture temporal movement on feature space.
pdf
Yona Falinie A. Gaus, Hongying Meng, Asim Jan
TL;DR: 2-stage regression approach can match or outperform SVR models on naturalistic affect recognition task
arXiv pdf
Yona Falinie A. Gaus, Temitayo Olugbade, Asim Jan, Rui Qin, Jingxin Liu, Fan Zhang, Hongying Meng, Nadia Bianchi-Berthouze
TL;DR: classification between different touch behaviour via ensemble of learning methods such as Random Forest and Boosting
arXiv pdf
Yona Falinie A. Gaus, Hongying Meng, Asim Jan, Fan Zhang, and Saeed Turabzadeh
TL;DR: Haar wavelet transform was used to remove noise, fusion process was used for combining video and audio modalities
paper pdf video
Asim Jan, Hongying Meng, Yona Falinie A. Gaus, Fan Zhang, Saeed Turabzadeh
TL;DR: 1-D MHH applied on dynamic feature for the video, PLS and Linear regression were used to capture correlation between feature space and depression scales
paper pdf