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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 Face Recognition under Ageing Effect: A Comparative Analysis(SPRINGER-VERLAG BERLIN, 2013) Akhtar, Zahid; Rattani, Ajita; Hadid, Abdenour; Tistarelli, Massimo; Petrosino, A; Bahcesehir University; Michigan State University; University of Oulu; University of SassariPrevious studies indicate that performance of the face recognition system severely degrades under the ageing effect. Despite the rising attention to facial ageing, there exist no comparative evaluation of the existing systems under the impact of ageing. Moreover, the compound effect of ageing and other variate such as glasses, gender etc, that are known to influence the performance, remain overlooked till date. To this aim, the contribution of this work are as follows: 1) evaluation of six baseline facial representations, based on local features, under the ageing effect, and 2) analysis of the compound effect of ageing with other variates, i.e., race, gender, glasses, facial hair etc.Publication Metadata only RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks(IEEE, 2018) Bircanoglu, Cenk; Atay, Meltem; Beser, Fuat; Genc, Ozgun; Kizrak, Merve Ayyuce; Yildirim, T; Manolopoulos, Y; Angelov, P; Iliadis, L; Bahcesehir University; Middle East Technical University; Yildiz Technical University; Koc UniversityWaste management and recycling is the fundamental part a sustainable economy. For more efficient and safe recycling, it is necessary to use intelligent systems instead of employing humans as workers in the dump-yards. This is one of the early works demonstrating the efficiency of latest intelligent approaches. In order to provide the most efficient approach, we experimented on well-known deep convolutional neural network architectures. For training without any pre-trained weights, Inception-Resnet, Inception-v4 outperformed all others with 90% test accuracy. For transfer learning and fine-tuning of weight parameters using ImageNet, DenseNet121 gave the best result with 95% test accuracy. One disadvantage of these networks, however, is that they are slightly slower in prediction time. To enhance the prediction performance of the models we altered the connection patterns of the skip connections inside dense blocks. Our model RecycleNet is carefully optimized deep convolutional neural network architecture for classification of selected recyclable object classes. This novel model reduced the number of parameters in a 121 layered network from 7 million to about 3 million.Publication Metadata only Machine Vision-Based Expert System for Automated Skin Cancer Detection(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Rada, Lavdie; Islam, Md Baharul; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Daffodil International UniversitySkin cancer is the most frequently occurring kind of cancer, accounting for about one-third of all cases. Automatic early detection without expert intervention for a visual inspection would be of great help for society. The image processing and machine learning methods have significantly contributed to medical and biomedical research, resulting in fast and exact inspection in different problems. One of such problems is accurate cancer detection and classification. In this study, we introduce an expert system based on image processing and machine learning for skin cancer detection and classification. The proposed approach consists of three significant steps: pre-processing, feature extraction, and classification. The pre-processing step uses the grayscale conversion, Gaussian filter, segmentation, and morphological operation to represent skin lesion images better. We employ two feature extractors, i.e., the ABCD scoring method (asymmetry, border, color, diameter) and gray level co-occurrence matrix (GLCM), to extract cancer-affected areas. Finally, five different machine learning classifiers such as logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) used to detect and classify skin cancer. Experimental results show that random forest exceeds all other classifiers achieving an accuracy of 97.62% and 0.97 Area Under Curve (AUC), which is state-of-the-art on the experimented open-source dataset PH2.Publication Metadata only Neural Architecture Search Using Differential Evolution in MAML Framework for Few-Shot Classification Problems(SPRINGER INTERNATIONAL PUBLISHING AG, 2023) Gulcu, Ayla; Kus, Zeki; DiGaspero, L; Festa, P; Nakib, A; Pavone, M; Bahcesehir UniversityModel-Agnostic Meta-Learning (MAML) algorithm is an optimization based meta-learning algorithm which aims to find a good initial state of the neural network that can then be adapted to any novel task using a few optimization steps. In this study, we take MAML with a simple four-block convolution architecture as our baseline, and try to improve its few-shot classification performance by using an architecture generated automatically through the neural architecture search process. We use differential evolution algorithm as the search strategy for searching over cells within a predefined search space. We have performed our experiments using two well-known few-shot classification datasets, mini-ImageNet and FC100 dataset. For each of those datasets, the performance of the original MAML is compared to the performance of our MAML-NAS model under both 1-shot 5-way and 5-shot 5-way settings. The results reveal that MAML-NAS results in better or at least comparable accuracy values for both of the datasets in all settings. More importantly, this performance is achieved by much simpler architectures, that is architectures requiring less floating-point operations.Publication Metadata only Augmented Reality Tool for Markerless Virtual Try-on around Human Arm(IEEE, 2015) Gunes, San; Sanli, Okan; Ergun, Ovgu Ozturk; Stadon, J; Gwilt, I; Smith, CH; Bahcesehir UniversityWe present a Markerless 3D Augmented Reality Application for virtual accessory try-on applications around human arm. The system is based on a Kinect sensor and a multi-layer rendering framework to render RGB, depth data and 3D model of accessories simultaneously. The aim is to support realistic visualization of virtual objects around human arm, by detecting wrist pose and handling occlusion for various interactive marketing and retail applications, such as virtual watch try-on.Publication Metadata only An anomaly intrusion detection approach using Cellular Neural Networks(SPRINGER-VERLAG BERLIN, 2006) Yang, Zhongxue; Karahoca, Adem; Levi, A; Savas, E; Yenigun, H; Balcisory, S; Saygin, Y; Nanjing Xiaozhuang University; Bahcesehir UniversityThis paper presents an anomaly detection approach for the network intrusion detection based on Cellular Neural Networks (CNN) model. CNN has features with multi-dimensional array of neurons and local interconnections among cells. Recurrent Perceptron Learning Algorithm (RPLA) is used to learn the templates and bias in CNN classifier. Experiments with KDD Cup 1999 network traffic connections which have been preprocessed with methods of features selection and normalization have shown that CNN model is effective for intrusion detection. In contrast to back propagation neural network, CNN model exhibits an excellent performance owing to the higher attack detection rate with lower false positive rate.Publication Metadata only Atmospheric Turbulence Mitigation Using Optical Flow(IEEE COMPUTER SOC, 2014) Caliskan, Tufan; Arica, Nafiz; Deniz Harp Okulu Komutanligi; Bahcesehir UniversityAtmospheric turbulence causes blurring and geometrical distortion in images acquired from a long distance. In this paper, we propose a fast and effective feature based registration technique in removing the distortions caused by atmospheric turbulence. We utilize optical flow method and combine the advantages of previous approaches based on space-invariant deconvolution and lucky frame idea. After an optical flow based registration of degraded image sequence, a patch-wise multi-frame reconstruction technique is applied to fuse the registered images. Finally, a blind-deconvolution technique is implemented to deblur the fused image to obtain a single high quality image. The experiments performed on common datasets show that the proposed method produces higher quality images more efficiently than the available methods.Publication Metadata only Network intrusion detection by using Cellular Neural Network with Tabu Search(IEEE COMPUTER SOC, 2008) Yang, Zhongxue; Karahoca, Adem; Yang, Ning; Aydin, Nizamettin; Stoica, A; Arslan, T; Howard, D; Higuchi, T; ElRayis, A; Nanjing Xiaozhuang University; Bahcesehir UniversityThis paper presents a novel Cellular Neural Network (CNN) templates learning approach based on Tabu Search (TS) for detecting network intrusions. The TS method was applied to CNN with symmetric templates and was verified by simulations. Simulation experiments on intrusion detection have shown that the TS-based template learning algorithm exhibits superior performance in computation time to find the optimal solution and in the solution quality in contrast to the algorithm of genetic algorithm (GA) and simulated annealing (SA).Publication Metadata only Hierarchical Image Representation Using Deep Network(SPRINGER INTERNATIONAL PUBLISHING AG, 2015) Ergul, Emrah; Erturk, Sarp; Arica, Nafiz; Murino, V; Puppo, E; Kocaeli University; Bahcesehir UniversityIn this paper, we propose a new method for features learning from unlabeled data. Basically, we simulate k-means algorithm in deep network architecture to achieve hierarchical Bag-of-Words (BoW) representations. We first learn visual words in each layer which are used to produce BoW feature vectors in the current input space. We transform the raw input data into new feature spaces in a convolutional manner such that more abstract visual words are extracted at each layer by implementing Expectation-Maximization (EM) algorithm. The network parameters are optimized as we keep the visual words fixed in the Expectation step while the visual words are updated with the current parameters of the network in the Maximization step. Besides, we embed spatial information into BoW representation by learning different networks and visual words for each quadrant regions. We compare the proposed algorithm with the similar approaches in the literature using a challenging 10-class-dataset, CIFAR-10.
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