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Publication Metadata only Assistive Visual Tool: Enhancing Safe Navigation with Video Remapping in AR Headsets(SPRINGER INTERNATIONAL PUBLISHING AG, 2025) Sadeghzadeh, Arezoo; Islam, Md Baharul; Uddin, Md Nur; Aydin, Tarkan; DelBue, A; Canton, C; Pont-Tuset, J; Tommasi, T; Bahcesehir University; State University System of Florida; Florida Gulf Coast UniversityVisual Field Loss (VFL) is characterized by blind spots or scotomas that poses detrimental impact on fundamental movement activities of individuals. Addressing the challenges (e.g., low video quality, content loss, high levels of contradiction, and limited mobility assessment) faced by existing Extended Reality (XR) systems as vision aids, we introduce a groundbreaking method that enriches the real-time navigation using Augmented Reality (AR) glasses. Our novel vision aid employs advanced video processing techniques to enhance visual perception in individuals with moderate to severe VFL, bridging the gap to healthy vision. A unique optimal video remapping function, tailored to our selected AR glasses characteristics, dynamically maps live video content to the largest intact region of the Visual Field (VF) map. Our method preserves video quality, minimizing blurriness and distortion. Through a comprehensive empirical user study involving 29 subjects with artificially induced scotomas, statistical analyses of object counting and multi-tasking walking track tests demonstrate the promising performance of our method in enhancing visual awareness and navigation capability in real-time.Publication Metadata only HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model(IEEE, 2022) Junayed, Masum Shah; Sadeghzadeh, Arezoo; Islam, Md Baharul; Wong, Lai-Kuan; Aydin, Tarkan; Bahcesehir University; Multimedia UniversityMonocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360 degrees surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets, Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360 degrees monocular depth estimation. Complete project code and supplementary materials are available at https://github.com/himode5008/HiMODE.Publication Metadata only BinoVFAR: An Efficient Binocular Visual Field Assessment Method using Augmented Reality Glasses(ASSOC COMPUTING MACHINERY, 2021) Islam, Md Baharul; Sadeghzadeh, Arezoo; Bahcesehir University; Bahcesehir UniversityVirtual Reality (VR)-based Visual Field Assessment (VFA) methods completely isolate the users from the real world, which results in nausea, eye strain, and lack of concentration and patience for the time-consuming test. In this paper, a robust binocular visual field assessment method based on novel Augmented Reality (AR) glasses is presented, namely, BinoVFAR that can simultaneously find the VF of both eyes. In this method, 60 stimuli in an arrangement of 6 rows and 10 columns randomly appear on a white background on the display of the AR glasses. These stimuli are displayed for 2 seconds that continuously change the intensities from light gray to black. Wearing the AR glasses and focusing on the central fixation point, the users are asked to click the clicker by seen a stimulus. The visible stimuli's intensities and positions are recorded in a 6 x 10 matrix based on the users' responses. A bi-cubic interpolation is applied to compute the binocular visual field map (as a 600 x 1000 matrix). A set of experiments (with an average accuracy of 99.93%), including repeatability and reproducibility tests (with an average Intra-class correlation coefficient (ICC) of 99.72%), are conducted to evaluate the BinoVFAR method.Publication Metadata only 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 UniversityWith 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.Publication Metadata only 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 UniversityMulti-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.