Nabil Ibtehaz, M. Sohel Rahman
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentat...
Umme Sara, Morium Akter, Mohammad Shorif Uddin
Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image qua...
Stephanie Kramer‐Schadt, Jürgen Niedballa, John D. Pilgrim, Boris Schröder et al.
Abstract Aim Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly samp...
Tawsifur Rahman, Amith Khandakar, Muhammad Abdul Kadir, Khandaker Reajul Islam et al.
Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images u...
M. C. Shewry, Henry P. Wynn
(1987). Maximum entropy sampling. Journal of Applied Statistics: Vol. 14, No. 2, pp. 165-170.
Roberta L. Klatzky, Susan J. Lederman, Victoria A. Metzger
Lianlin Li, Hengxin Ruan, Che Liu, Ying Li et al.
Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager ...
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localize and amplify significant parts to learn fine-grained details, which often suffer from a limited number of parts and hea...
Shanto Rahman, Md. Mostafijur Rahman, M. Abdullah‐Al‐Wadud, Golam Dastegir Al-Quaderi et al.
Due to the limitations of image-capturing devices or the presence of a non-ideal environment, the quality of digital images may get degraded. In spite of much advancement in imaging science, captured images do not always fulfill users’ expectations of clear and soothing views. Most of the existing m...
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Håvard D. Johansen et al.
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the incr...
Habiba Afrin
Soil stabilization is the process of improving the shear strength parameters of soil and thus increasing the bearing capacity of soil. It is required when the soil available for construction is not suitable to carry structural load. Soils exhibit generally undesirable engineering properties. Soil St...
Mahbuba Begum, Mohammad Shorif Uddin
Digital image authentication is an extremely significant concern for the digital revolution, as it is easy to tamper with any image. In the last few decades, it has been an urgent concern for researchers to ensure the authenticity of digital images. Based on the desired applications, several suitabl...
Md. Eshmam Rayed, S. M. Sajibul Islam, Sadia Islam Niha, Jamin Rahman Jim et al.
Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in higher layers and basic edge identification i...
Shalini Stalin, Vandana Roy, Prashant Kumar Shukla, Atef Zaguia et al.
The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artif...
Samiul Alam, M. I. H. Bhuiyan
In this paper, a method using higher order statistical moments of EEG signals calculated in the empirical mode decomposition (EMD) domain is proposed for detecting seizure and epilepsy. The appropriateness of these moments in distinguishing the EEG signals is investigated through an extensive analys...