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Start date: 2020End date: 2026Type: search.filters.itemtype.Proceedings PaperAuthor: search.filters.author.Islam, Md BaharulSubject: search.filters.subject.Engineering, Electrical & Electronic

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Now showing 1 - 5 of 5
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    SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes
    (IEEE, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Daffodil International University; Khulna University of Engineering & Technology (KUET); Bahcesehir University; Daffodil International University
    Skin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.
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    Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition
    (IEEE, 2021) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Struc, V; Ivanovska, M; Bahcesehir University
    With the emergence of the global epidemic of COVID-19, face recognition systems have achieved much attention as contactless identity verification methods. However, covering a considerable part of the face by the mask poses severe challenges for conventional face recognition systems. This paper proposes an automated Masked Face Recognition (MFR) system based on the combination of a mask occlusion discarding technique and a deep-learning model. Initially, a pre-processing step is carried out in which the images pass three filters. Then, a Convolutional Neural Network (CNN) model is proposed to extract the features from unoccluded regions of the faces (i.e., eyes and forehead). These feature maps are employed to obtain covariance-based features. Two extra layers, i.e., Bitmap and Eigenvalue, are designed to reduce the dimension and concatenate these covariance feature matrices. The deep covariance features are quantized to codebooks combined based on Bag-of-Features (BoF) paradigm. Finally, a global histogram is created based on these codebooks and utilized for training an SVM classifier. The proposed method is trained and evaluated on Real-World-Masked-Face-Recognition-Dataset (RMFRD) and Simulated-Masked-Face-Recognition-Dataset (SMFRD) achieves an accuracy of 95.07% and 92.32%, respectively, showing its competitive performance compared to the state-of-the-art. Experimental results prove that our system has high robustness against noisy data and illumination variations.
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    AN EFFICIENT END-TO-END IMAGE COMPRESSION TRANSFORMER
    (IEEE, 2022) Jeny, Afsana Ahsan; Junayed, Masum Shah; Islam, Md Baharul; Bahcesehir University
    Image and video compression received significant research attention and expanded their applications. Existing entropy estimation-based methods combine with hyperprior and local context, limiting their efficacy. This paper introduces an efficient end-to-end transformer-based image compression model, which generates a global receptive field to tackle the long-range correlation issues. A hyper encoder-decoder-based transformer block employs a multi-head spatial reduction self-attention (MHSRSA) layer to minimize the computational cost of the self-attention layer and enable rapid learning of multi-scale and high-resolution features. A Casual Global Anticipation Module (CGAM) is designed to construct highly informative adjacent contexts utilizing channel-wise linkages and identify global reference points in the latent space for end-to-end rate-distortion optimization (RDO). Experimental results demonstrate the effectiveness and competitive performance of the KODAK dataset.
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    DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation
    (IEEE, 2021) Jeny, Afsana Ahsan; Islam, Md Baharul; Aydin, Tarkan; Cree, MJ; Bahcesehir University
    Recent advances in optical flow prediction have been made possible by using feature pyramids and iterative refining. Though downsampling in feature pyramids may cause foreground items to merge with the background, the iterative processing could be incorrect in optical flow experiments. Particularly the outcomes of the movement of narrow and tiny objects can be more invisible in the flow scene. We introduce a novel method called DeepPyNet for optical flow estimation that includes feature extractor, multi-channel cost volume, and flow decoder. In this method, we propose a deep recurrent feature pyramid-based network for the end-to-end optical flow estimation. The feature extraction from each pixel of the feature map keeps essential information without modifying the feature receptive field. Then, a multi-scale 4 Dc orrelation volume is built from the visual similarity of each pair of pixels. Finally, we utilize the multi-scale correlation volumes to continuously update the flow field through an iterative recurrent method. Experimental results demonstrate that DeepPyNet significantly eliminates flow errors and provides state-of-the-art performance in various datasets. Moreover, DeepPyNet is less complex and uses only 6.1M parameters 81% and 35% smaller than the popular FlowNet and PWC-Net+, respectively.
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    An Effective Multi-Camera Dataset and Hybrid Feature Matcher for Real-Time Video Stitching
    (IEEE, 2021) Hosen, Md Imran; Islam, Md Baharul; Sadeghzadeh, Arezoo; Cree, MJ; Bahcesehir University
    Multi-camera video stitching combines several videos captured by different cameras into a single video for a wide Field-of-View (FOV). In this paper, a novel dataset is developed for video stitching which consists of 30 video sets captured by four static cameras in various environmental scenarios. Then, a new video stitching method is proposed based on a hybrid matcher for stitching four videos with over 200 degrees FOV. The keypoints and descriptors are obtained by the scale-invariant feature transform (SIFT) and Root-SIFT, respectively. Then, these keypoint descriptors are matched by applying a hybrid matcher, a combination of Brute Force (BF), and Fast Linear Approximated Nearest Neighbours (FLANN) matchers. After geometrical verification and eliminating outlier matching points, one-time homography is estimated based on Random Sample Consensus (RANSAC). The proposed method is implemented and evaluated in different indoor/outdoor video settings. Experimental results demonstrate the capability, high accuracy, and robustness of the proposed method.