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Publication Metadata only SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(Institute of Electrical and Electronics Engineers Inc., 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Jeny, Afsana Ahsan, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Sakib, Abu Noman Md, Department of Cse, Khulna University of Engineering and Technology, Khulna, Bangladesh; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Lima, Khadija Akter, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Ahmed, Ikhtiar, Department of CSE, Daffodil International University, Dhaka, Bangladesh; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, American University of Malta, Cospicua, MaltaSkin 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. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset(Springer Science and Business Media Deutschland GmbH, 2021) Sadeghzadeh, Arezoo; Islam, Md Baharul; Zaker, Reza; Vincze, M.; Patten, T.; Christensen, H.I.; Nalpantidis, L.; Liu, M.; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, Malta; Zaker, Reza, Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, IranVideo desnowing/deraining plays a vital role in outdoor vision systems, such as autonomous driving and surveillance systems, since the weather conditions significantly degrade their performance. Although numerous approaches have been reported for video snow/rain removal, they are limited to a few videos and did not consider the variations that occurred for the camera and background in real applications. We build a complete snow and rain dataset to overcome this limitation, consisting of 577 videos with synthetic snow and rain, quasi-snow, and real snow and rain. All possible variations of the background and the camera are considered in the dataset. Then, an efficient pixel-wise video desnowing/deraining method is proposed based on the color and temporal information in consecutive video frames. It is highly likely for a single pixel to be a background pixel rather than a snowy pixel at least once in the consecutive frames. Inspiring from this fact along with the color information of the snow pixels, we extract the background pixels from different consecutive frames by searching for the minimum gray-scale intensity. Experimental results demonstrate and validate the proposed method’s robustness to illumination and high-performance static background and camera. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Towards Stereoscopic Video Deblurring Using Deep Convolutional Networks(Springer Science and Business Media Deutschland GmbH, 2021) Imani, Hassan; Islam, Md Baharul; Bebis, G.; Athitsos, V.; Yan, T.; Lau, M.; Li, F.; Shi, C.; Yuan, X.; Mousas, C.; Bruder, G.; Imani, Hassan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThese days stereoscopic cameras are commonly used in daily life, such as the new smartphones and emerging technologies. The quality of the stereo video can be affected by various factors (e.g., blur artifact due to camera/object motion). For solving this issue, several methods are proposed for monocular deblurring, and there are some limited proposed works for stereo content deblurring. This paper presents a novel stereoscopic video deblurring model considering the consecutive left and right video frames. To compensate for the motion in stereoscopic video, we feed consecutive frames from the previous and next frames to the 3D CNN networks, which can help for further deblurring. Also, our proposed model uses the stereoscopic other view information to help for deblurring. Specifically, to deblur the stereo frames, our model takes the left and right stereoscopic frames and some neighboring left and right frames as the inputs. Then, after compensation for the transformation between consecutive frames, a 3D Convolutional Neural Network (CNN) is applied to the left and right batches of frames to extract their features. This model consists of the modified 3D U-Net networks. To aggregate the left and right features, the Parallax Attention Module (PAM) is modified to fuse the left and right features and create the output deblurred frames. The experimental results on the recently proposed Stereo Blur dataset show that the proposed method can effectively deblur the blurry stereoscopic videos. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only PoseTED: A Novel Regression-Based Technique for Recognizing Multiple Pose Instances(Springer Science and Business Media Deutschland GmbH, 2021) Jeny, Afsana Ahsan; Junayed, Masum Shah; Islam, Md Baharul; Bebis, G.; Athitsos, V.; Yan, T.; Lau, M.; Li, F.; Shi, C.; Yuan, X.; Mousas, C.; Bruder, G.; Jeny, Afsana Ahsan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Junayed, Masum Shah, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, TurkeyPose estimation for multiple people can be viewed as a hierarchical set predicting challenge. Algorithms are needed to classify all persons according to their physical components appropriately. Pose estimation methods are divided into two categories: (1) heatmap-based, (2) regression-based. Heatmap-based techniques are susceptible to various heuristic designs and are not end-to-end trainable, while regression-based methods involve fewer intermediary non-differentiable stages. This paper presents a novel regression-based multi-instance human pose recognition network called PoseTED. It utilizes the well-known object detector YOLOv4 for person detection, and the spatial transformer network (STN) used as a cropping filter. After that, we used a CNN-based backbone that extracts deep features and positional encoding with an encoder-decoder transformer applied for keypoint detection, solving the heuristic design problem before regression-based techniques and increasing overall performance. A prediction-based feed-forward network (FFN) is used to predict several key locations’ posture as a group and display the body components as an output. Two available public datasets are tested in this experiment. Experimental results are shown on the COCO and MPII datasets, with an average precision (AP) of 73.7% on the COCO val. dataset, 72.7% on the COCO test dev. dataset, and 89.7% on the MPII datasets, respectively. These results are comparable to the state-of-the-art methods. © 2021 Elsevier B.V., All rights reserved.Publication Open Access A Deep CNN Model for Skin Cancer Detection and Classification(Vaclav Skala Union Agency, 2021) Junayed, Masum Shah; Anjum, Nipa; Sakib, Abu Noman Md; Islam, Md Baharul; Skala, V.; Junayed, Masum Shah, Bahçeşehir Üniversitesi, Istanbul, Turkey; Anjum, Nipa, Khulna University of Engineering and Technology, Khulna, Bangladesh; Sakib, Abu Noman Md, Khulna University of Engineering and Technology, Khulna, Bangladesh; Islam, Md Baharul, American University of Malta, Cospicua, MaltaSkin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection of skin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-based methods help dermatologists in classifying medical images. This paper proposes a deep learning-based model to detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, we collected a dataset that includes four skin cancer image data before applying them in augmentation techniques to increase the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data, our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNet by 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while being computationally comparable. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation(IEEE Computer Society, 2021) Jeny, Afsana Ahsan; Islam, Md Baharul; Aydin, Tarkan; Cree, M.J.; Jeny, Afsana Ahsan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydin, Tarkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyRecent 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 4D correlation 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. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only An Effective Multi-Camera Dataset and Hybrid Feature Matcher for Real-Time Video Stitching(IEEE Computer Society, 2021) Hosen, Md Imran; Islam, Md Baharul; Sadeghzadeh, Arezoo; Cree, M.J.; Hosen, Md Imran, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyMulti-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° 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. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Struc, V.; Ivanovska, M.; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, MaltaWith 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. © 2025 Elsevier B.V., All rights reserved.Publication Metadata only Machine vision-based expert system for automated cucumber diseases recognition and classification(Institute of Electrical and Electronics Engineers Inc., 2021) Jeny, Afsana Ahsan; Junayed, Masum Shah; Islam, Md Baharul; Imani, Hassan; Shah, A. F.M.Shahen; Kilimci, Z.H.; Yildirim, T.; Piuri, V.; Czarnowski, I.; Camacho, D.; Manolopoulos, Y.; Solak, S.; Jeny, Afsana Ahsan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, Malta; Imani, Hassan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Shah, A. F.M.Shahen, Department of Electronics and Communication Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyAutomated cucumber disease detection may significantly provide agricultural assistance for remote farmers. Due to having the similarity symptoms, it is challenging to differentiate between various forms of cucumber disease. This paper proposes an automated solution to recognize and classify the cucumber disease using different computer vision-based techniques. In light of this circumstance, we design a computerized cucumber disease recognition system that analyzes images collected by mobile phones and can recognize diseases to assist rural farmers in dealing with the situation. In our method, a discriminating feature set is initially extracted from the input images. Then, K-means clustering segmentation separates the disease-affected regions from the remaining image part. Finally, the diseases are classified using five different classification algorithms. Different evaluation metrics, including accuracy, precision, sensitivity, specificity, False-Positive Rate (FPR), False-Negative Rate (FNR), are used to analyze the classifier's performance. We have carried out several experiments to illustrate the use of the proposed expert system. Our experiments showed that random forest exceeds all other classifiers regarding the total number of metrics used, with an accuracy of 85.84% on our dataset. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Real-Time YOLO-based Heterogeneous front vehicles detection(Institute of Electrical and Electronics Engineers Inc., 2021) Junayed, Masum Shah; Islam, Md Baharul; Sadeghzadeh, Arezoo; Aydin, Tarkan; Kilimci, Z.H.; Yildirim, T.; Piuri, V.; Czarnowski, I.; Camacho, D.; Manolopoulos, Y.; Solak, S.; Junayed, Masum Shah, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey, Daffodil International University, Dhaka, Bangladesh; Islam, Md Baharul, Bahçeşehir Üniversitesi, Istanbul, Turkey, College of Data Science and Engineering, American University of Malta, Cospicua, Malta; Sadeghzadeh, Arezoo, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydin, Tarkan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe perception of the complex road environment is a critical factor in autonomous driving, which has become the research focus in intelligent vehicles. In this paper, a real-time front vehicle detection system is proposed to ensure safe driving in a complex environment, particularly in congested megacities. This system is based on the YOLO model, which effectively detects and classifies various vehicles from both images and videos. It improves detection accuracy by modifying a feature extraction-based backbone. To the authors' best knowledge, this is the first time that vehicle detection is implemented on the recently published DhakaAI dataset. Compared to the other available datasets for object detection, such as KITTI, the DhakaAI dataset has a complex environment with numerous vehicles (21 different types). Experimental results demonstrate that the proposed system outperforms the state-of-the-art object detectors. In this method, the mAP (mean average precision) and the FPS (frame per second) is increased by 2.97% and 1.47, 4.64% and 5.57, 4.75% and 3.02, compared to the RetinaNet, SSD, and Faster RCNN on this dataset, respectively. © 2021 Elsevier B.V., All rights reserved.