Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741

Browse

Filters

2014 - 2019152020 - 20237

Settings

End date: 2026Type: search.filters.itemtype.Proceedings PaperHas files: NoAuthor: search.filters.author.Arica, Nafiz

Search Results

Now showing 1 - 10 of 22
  • No Thumbnail Available
    PublicationMetadata only
    Creation of Annotated Synthetic UAV Video Dataset for Object Detection and Tracking
    (IEEE, 2023) Yilmaz, Can; Maras, Bahri; Arica, Nafiz; Ertuzun, Aysin Baytan; Bahcesehir University; Bogazici University; Piri Reis University
    In order for object detection and tracking in videos obtained from unmanned aerial vehicles (UAVs) by deep convolutional neural networks (DCNN), extensive ground truth optical flow, occlusion and segmentation datasets, of various objects or vehicles, are required during the training and testing processes. The mentioned ground truth informations are not widely available in the literature due to the difficulty of labeling or extracting them from real-life recorded UAV video images. In this study, ground truth optical flow, occlusion and segmentation datasets were produced synthetically for the first time with the UAV point of view in a novel way, so as to fill the gap in literature. The ground truth datasets were created for each vehicle by subjecting the triangles (mesh) automatically generated by the Unity engine to the homography method. With this method, 1920x1080 and 250x250 sized synthetic datasets consisting of 100 scenarios were obtained.
  • No Thumbnail Available
    PublicationMetadata only
    Enhancing Object Detection Algorithms by Synthetic Aerial Images
    (IEEE, 2023) Yilmaz, Can; Maras, Bahri; Yilmaz, Gorkem; Ceylan, Goksu; Hamamcioglu, Onder; Arica, Nafiz; Ertuzun, Aysin Baytan; Bahcesehir University; Bogazici University; Bahcesehir University; Piri Reis University
    In order to accurately perform object detection by deep convolutional neural networks (DCNN) in videos, obtained from unmanned aerial vehicles (UAVs), many example images of objects containing annotations such as ground truth class information, bounding box, optical flow, occlusion and segmentation are required. Due to the difficulties faced during annotation of scenarios, and due to the inadequacy of the scenario diversity resulting from environmental conditions, a dataset containing above mentioned ground truths has not been found in the literature. In this study, synthetic aerial images with various annotation information were created in different scenarios while composing virtual worlds, and enhancing object detection algorithms is aimed. Enhancement of detection results of DCNN based object detection algorithms, trained with the support of synthetic aerial images, on real-world aerial images significantly, was observed during the experiments, conducted.
  • No Thumbnail Available
    PublicationMetadata only
    A Scoring Method for Interpretability of Concepts in Convolutional Neural Networks
    (IEEE, 2022) Gurkan, Mustafa Kagan; Arica, Nafiz; Vural, Fato Yarman; Bahcesehir University; Middle East Technical University
    In this paper, we propose a scoring algorithm for measuring the interpretability of CNN models by focusing on the feature extraction operation at the convolutional layers. The proposed approach is based on the principal of concept analysis, for a predefined list of concepts. A map of the network is created based on its responsiveness against each concept. Once this map is ready, various images can be applied as inputs and they are matched with the concepts whose hidden nodes are highly activated. Finally, the evaluation algorithm kicks in to use these descriptions during the final prediction and provides human-understandable explanations.
  • No Thumbnail Available
    PublicationMetadata only
    ATMOSPHERIC TURBULENCE MITIGATION USING FEATURE BASED OPTICAL FLOW
    (IEEE, 2014) Caliskan, Tufan; Arica, Nafiz; Deniz Harp Okulu Komutanligi; Bahcesehir University
    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. 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.
  • No Thumbnail Available
    PublicationMetadata only
    Graph Neural Networks Based Approach for Interpersonal Relationship Classification in Images
    (IEEE, 2023) Akay, Simge; Arica, Nafiz; Bahcesehir University; Piri Reis University
    Interpersonal relationships, which are an integral part of our social life, demonstrate how people connect and interact within society. Describing the relationship between two individuals in images depends on many different factors and attributes. In this study, a Graph Neural Network (GNN) based approach is proposed that focuses on the important attributes for describing the relationship between two individuals in images. In the proposed method, each attribute that will be used to describe the relationship is defined as a GNN node. Then, the meaningless connections between nodes are pruned with a pruning operation to obtain the ideal GNN model. In this study, a more robust GNN for classifying interpersonal relationships is created by using rich attributes, unlike the literature, and obtaining significant connections between nodes through pruning. The experiments conducted in this study showed that both using a wider GNN and pruning operation improve the classification performance.
  • No Thumbnail Available
    PublicationMetadata only
    Microservice Interaction Prediction in Communication Platform as a Service
    (IEEE, 2022) Aktas, Kemal; Kilinc, H. Hakan; Arica, Nafiz; Bahcesehir University
    In telecommunication platforms, it is necessary to monitor the system, take quick action against possible and observed errors, and ensure the continuous operability of the system. However, debugging and problem addressing processes can take a long time in microservice architecture-based platforms with high number of users and heavy traffic. This difficulty arises from the necessity of detecting microservice interactions. In the first phase of this study, it has been observed that providing microservice flows and interactions saves operational teams a significant time during the debugging process. In this scope, different machine learning-based models that predict microservice interactions have been developed and performances are compared. In these models, log patterns are extracted on microservice log data and the interaction map of the mentioned microservice is created by estimating the previous and next microservices that the current microservice interacts with at a certain moment. In the experiments on microservice logs working with the basic call scenario, successful estimation results were obtained that could contribute positively to the debugging process.
  • No Thumbnail Available
    PublicationMetadata only
    Atmospheric Turbulence Mitigation Using Optical Flow
    (IEEE COMPUTER SOC, 2014) Caliskan, Tufan; Arica, Nafiz; Deniz Harp Okulu Komutanligi; Bahcesehir University
    Atmospheric 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.
  • No Thumbnail Available
    PublicationMetadata only
    Automatically Inserting Ads into Images
    (IEEE, 2018) Citak, Erol; Eseoglu, Mustafa Furkan; Celik, Ibrahim Omer; Disanli, Onur; Kutluk, Sezer; Arica, Nafiz; Huawei Technologies; Bahcesehir University
    In this study a new method is proposed for inserting advertisement visuals into images automatically and without disturbing the image content. In this method important areas are determined using deep learning based object, face and text detection, edge and saliency maps are obtained, and these information are used for the identification of the best location for inserting the advertisement visual. In order to select the best available advertisement visual from an advertisement pool shape and color features are utilized.
  • No Thumbnail Available
    PublicationMetadata 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 University
    In 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.
  • No Thumbnail Available
    PublicationMetadata only
    Risk Classification For Breast Cancer Diagnosis Using HER2 Testing
    (IEEE, 2016) Aktan, Pinar Ece; Hatipoglu, Gokhan; Arica, Nafiz; Bahcesehir University
    Trastuzumab is one of the effective treatment for breast cancer. It is an antibody against the protein product of the human epidermal growth factor receptor type 2 (HER2) gene. In HER-2 testing protein levels are determined by IHC staining. For a proper treatment IHC stains are evaluated together with ISH stains. The widespread approach done by the experts is to determine membranes manually, yet it leads subjective interpretation by pathologist and it increases their workload. In this paper, we propose a new method for HER-2 testing based on image analysis algorithms on membrane regions. After, maximally stable extremal regions algorithm is employed for detecting the membrane regions, these areas of those regions are analyzed quantitatively for IHC classification. In the next step, complete/incomplete membrane regions are determined for IHC 2+ classification. The experiments conducted on the IHC stains taken from the clinic patients. Analysis resulted with the successful classification rates.