M
Maryam Sultana
Researcher at Kyungpook National University
Publications - 16
Citations - 451
Maryam Sultana is an academic researcher from Kyungpook National University. The author has contributed to research in topics: Background subtraction & Object detection. The author has an hindex of 6, co-authored 13 publications receiving 265 citations.
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Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
TL;DR: The first review of deep neural network concepts in background subtraction for novices and experts is provided in order to analyze this success and to provide further directions.
Posted Content
Unsupervised RGBD Video Object Segmentation Using GANs.
TL;DR: A fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy in videos in the presence of challenges such as illumination variations, shadows, and color camouflage.
Book ChapterDOI
Unsupervised Adversarial Learning for Dynamic Background Modeling
TL;DR: This study proposed an end-to-end framework based on Generative Adversarial Network, which can generate dynamic background information for the task of DBM in an unsupervised manner and has outperformed eight existing methods in many challenging scenarios.
Journal ArticleDOI
Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition
TL;DR: A two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and its significance in recognizing posed and nonposed facial expressions is evaluated and shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches.
Posted Content
Unsupervised Deep Context Prediction for Background Foreground Separation.
TL;DR: A unified framework based on the algorithm of image inpainting is proposed that is an unsupervised visual feature learning hybrid Generative Adversarial algorithm based on context prediction and its stability shows its stability in the application of background estimation and foreground detection.