no code implementations • 5 Apr 2024 • Shahzad Ali, Yu Rim Lee, Soo Young Park, Won Young Tak, Soon Ki Jung
Downsampling images and labels, often necessitated by limited resources or to expedite network training, leads to the loss of small objects and thin boundaries.
no code implementations • 22 Nov 2023 • Lamyanba Laishram, Muhammad Shaheryar, Jong Taek Lee, Soon Ki Jung
We perform an incremental facial exaggeration from the real image to the caricature faces using the encoder and generator's latent space.
no code implementations • 6 Jul 2022 • Shahzad Ali, Arif Mahmood, Soon Ki Jung
We developed a model that is similar in spirit to the well-established encoder-decoder and residual convolution neural networks.
no code implementations • 12 Feb 2019 • Mustansar Fiaz, Kamran Ali, Abdul Rehman, M. Junaid Gul, Soon Ki Jung
Performance of these classifiers is investigated over different images of brain MRI and the variation in the performance of these classifiers is observed for different brain tissues.
no code implementations • 7 Feb 2019 • Maryam Sultana, Soon Ki Jung
To address this problem, our presented GAN model is trained on background image samples with dynamic changes, after that for testing the GAN model has to generate the same background sample as test sample with similar conditions via back-propagation technique.
no code implementations • 6 Dec 2018 • Mustansar Fiaz, Arif Mahmood, Sajid Javed, Soon Ki Jung
In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms.
no code implementations • 13 Nov 2018 • Thierry Bouwmans, Sajid Javed, Maryam Sultana, Soon Ki Jung
Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies.
no code implementations • 5 Nov 2018 • Maryam Sultana, Arif Mahmood, Sajid Javed, Soon Ki Jung
To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.
no code implementations • 21 May 2018 • Maryam Sultana, Arif Mahmood, Sajid Javed, Soon Ki Jung
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
no code implementations • 9 Feb 2018 • Mustansar Fiaz, Arif Mahmood, Soon Ki Jung
In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise.
no code implementations • 29 Jan 2018 • Mustansar Fiaz, Sajid Javed, Arif Mahmood, Soon Ki Jung
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades.
1 code implementation • 4 Nov 2015 • Thierry Bouwmans, Andrews Sobral, Sajid Javed, Soon Ki Jung, El-Hadi Zahzah
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.