Özuysal, Mustafa
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Ozuysal, M.
Oezuysal, Mustafa
Ozuysal, M
Özuysal, M
Ozuysal, Mustafa
Özuysal, M.
Oezuysal, Mustafa
Ozuysal, M
Özuysal, M
Ozuysal, Mustafa
Özuysal, M.
Job Title
Email Address
mustafaozuysal@iyte.edu.tr
Main Affiliation
03.04. Department of Computer Engineering
Status
Former Staff
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Turkish CoHE Profile ID
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WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
Research Products
3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
1
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
0
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
5
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
1
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
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14LIFE BELOW WATER
0
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
0
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Documents
24
Citations
2555
h-index
11
Documents
15
Citations
1514
Scholarly Output
31
Articles
10
Views / Downloads
330632/50635
Supervised MSc Theses
9
Supervised PhD Theses
2
WoS Citation Count
389
Scopus Citation Count
513
Patents
0
Projects
5
WoS Citations per Publication
12.55
Scopus Citations per Publication
16.55
Open Access Source
21
Supervised Theses
11
| Journal | Count |
|---|---|
| Signal Image and Video Processing | 2 |
| 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings | 2 |
| 2023 IEEE International Conference On Cyber Security and Resilience, Csr | 1 |
| 23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019 | 1 |
| 24th Signal Processing and Communication Application Conference, SIU 2016 | 1 |
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Now showing 1 - 10 of 31
Article Citation - WoS: 55Citation - Scopus: 56Evaluation of an Artificial Intelligence System for Diagnosing Scaphoid Fracture on Direct Radiography(Springer Verlag, 2020) Özkaya, Emre; Topal, Fatih Esad; Bulut, Tuğrul; Gürsoy, Merve; Özuysal, Mustafa; Karakaya, ZeynepPurpose The aim of this study is to determine the diagnostic performance of artificial intelligence with the use of convolutional neural networks (CNN) for detecting scaphoid fractures on anteroposterior wrist radiographs. The performance of the deep learning algorithm was also compared with that of the emergency department (ED) physician and two orthopaedic specialists (less experienced and experienced in the hand surgery). Methods A total 390 patients with AP wrist radiographs were included in the study. The presence/absence of the fracture on radiographs was confirmed via CT. The diagnostic performance of the CNN, ED physician and two orthopaedic specialists (less experienced and experienced) as measured by AUC, sensitivity, specificity, F-Score and Youden index, to detect scaphoid fractures was evaluated and compared between the groups. Results The CNN had 76% sensitivity and 92% specificity, 0.840 AUC, 0.680 Youden index and 0.826Fscore values in identifying scaphoid fractures. The experienced orthopaedic specialist had the best diagnostic performance according to AUC. While CNN's performance was similar to a less experienced orthopaedic specialist, it was better than the ED physician. Conclusion The deep learning algorithm has the potential to be used for diagnosing scaphoid fractures on radiographs. Artificial intelligence can be useful for scaphoid fracture diagnosis particularly in the absence of an experienced orthopedist or hand surgeon.Master Thesis Keypoint Matching Based on Descriptor Statistics(Izmir Institute of Technology, 2016) Uzyıldırım, Furkan Eren; Uzyıldırım, Furkan Eren; Özuysal, Mustafa; Özuysal, MustafaThe binary descriptors are the representation of choice for real-time keypoint matching. However, they suffer from reduced matching rates due to their discrete nature. In this thesis, we propose an approach that can augment their performance by searching in the top K near neighbor matches instead of just the single nearest neighbor one. To pick the correct match out of the K near neighbors, we exploit statistics of descriptor bit variations collected for each keypoint individually in an off-line training phase. This is similar in spirit to approaches that learn a patch specific keypoint representation. Unlike these approaches, we limit the use of a keypoint specific score only to rank the list of K near neighbors. Since this list can be efficiently computed with approximate nearest neighbor algorithms, our approach scales well to large descriptor collections.Article Citation - WoS: 8Citation - Scopus: 11Ca-Arbac: Privacy Preserving Using Context-Aware Role-Based Access Control on Android Permission System(Hindawi Publishing Corporation, 2016) Abdella, Juhar Ahmed; Özuysal, Mustafa; Tomur, EmrahExisting mobile platforms are based on manual way of granting and revoking permissions to applications. Once the user grants a given permission to an application, the application can use it without limit, unless the user manually revokes the permission. This has become the reason for many privacy problems because of the fact that a permission that is harmless at some occasion may be very dangerous at another condition. One of the promising solutions for this problem is context-aware access control at permission level that allows dynamic granting and denying of permissions based on some predefined context. However, dealing with policy configuration at permission level becomes very complex for the user as the number of policies to configure will become very large. For instance, if there are A applications, P permissions, and C contexts, the user may have to deal with A × P × C number of policy configurations. Therefore, we propose a context-aware role-based access control model that can provide dynamic permission granting and revoking while keeping the number of policies as small as possible. Although our model can be used for all mobile platforms, we use Android platform to demonstrate our system. In our model, Android applications are assigned roles where roles contain a set of permissions and contexts are associated with permissions. Permissions are activated and deactivated for the containing role based on the associated contexts. Our approach is unique in that our system associates contexts with permissions as opposed to existing similar works that associate contexts with roles. As a proof of concept, we have developed a prototype application called context-aware Android role-based access control. We have also performed various tests using our application, and the result shows that our model is working as desired.Doctoral Thesis Planar Geometry Estimation With Deep Learning(Izmir Institute of Technology, 2022) Uzyıldırım, Furkan Eren; Özuysal, MustafaUnderstanding the geometric structure of any scene is one of the oldest problems in Computer Vision. Most scenes include planar regions that provide information about the geometric structure and their automatic detection and segmentation plays an important role in many computer vision applications. In recent years, convolutional neural network architectures have been introduced for piece-wise planar segmentation. They outperform the traditional approaches that generate plane candidates with 3D segmentation methods from the explicitly reconstructed 3D point cloud. However, most of the convolutional neural network architectures are not designed and trained for outdoor scenes, because they require manual annotation, which is a time-consuming task that results in a lack of training data. In this thesis,we propose and develop a deep learning based framework for piece-wise plane detection and segmentation of outdoor scenes without requiring manually annotated training data. We exploit a network trained on imagery with annotated targets and an automatically reconstructed point cloud from either Structure from Motion-Multi View Stereo pipeline or monocular depth estimation network to estimate the training ground truth on the outdoor images in an iterative energy minimization framework. We show that the resulting ground truth estimate of various sets of images in the outdoor domain is good enough to improve network weights of different architectures trained on ground truth annotated images. Moreover, we demonstrate that this transfer learning scheme can be repeated multiple times iteratively to further improve the accuracy of plane detection and segmentation on monocular images of outdoor scenes.Conference Object Deep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic Microscopy(The Chemical and Biological Microsystems Society (CBMS), 2019) Delikoyun, Kerem; Çine, Ersin; Anıl İnevi, Müge; Özçivici, Engin; Özuysal, Mustafa; Tekin, Hüseyin CumhurCell viability analysis is one of the most widely used protocols in the fields of biomedical sciences. Traditional methods are prone to human error and require high-cost and bulky instrumentations. Lensless digital inline holographic microscopy (LDIHM) offers low-cost and high resolution imaging. However, recorded holograms should be digitally reconstructed to obtain real images, which requires intense computational work. We introduce a deep transfer learning-based cell viability classification method that directly processes the hologram without reconstruction. This new model is only trained once and viability of each cell can be predicted from its hologram. © 2019 CBMS-0001.Master Thesis Privacy Preservation on Mobile Systems Using Context-Aware Role Based Accss Control(Izmir Institute of Technology, 2016) Abdella, Juhar Ahmed; Özuysal, Mustafa; Tomur, Emrah; Özuysal, Mustafa; Tomur, EmrahExisting mobile platforms require the user to manually grant and revoke permissions to applications. Once the user grants a given permission to an application, the application can use it without limit unless the user manually revokes the permission. This has become the reason for a lot of privacy problems. One of the solutions suggested by a lot of researchers is Context Aware Access Control (CAAC). However, dealing with policy configurations at permission level becomes very complex as the number of policy rules to configure will become very large. For instance, if there are A applications, P permissions and C contexts, the user may have to deal with A x P x C number of policy configurations. Therefore, we propose a Context-Aware Role-Based Access Control (CA-RBAC) model that can provide dynamic permission granting and revoking while keeping the number of policy rules as small as possible. We demonstrate our model based on Android. In our model, Android applications are assigned roles where roles contain a set of permissions and contexts are associated with permissions. Permissions are activated and deactivated for the containing role based on the associated contexts. Our approach is unique in that our system associates contexts with permissions as opposed to existing similar works which associate contexts with roles. As a proof of concept, we have developed a prototype application called CA-ARBAC (Context-Aware Android Role Based Access Control). We have also performed various tests using our application and the result shows that our model is working as desired.Conference Object Citation - WoS: 3Citation - Scopus: 7A Taxonomic Survey of Model Extraction Attacks(IEEE, 2023) Genç, Didem; Özuysal, Mustafa; Tomur, EmrahA model extraction attack aims to clone a machine learning target model deployed in the cloud solely by querying the target in a black-box manner. Once a clone is obtained it is possible to launch further attacks with the aid of the local model. In this survey, we analyze existing approaches and present a taxonomic overview of this field based on several important aspects that affect attack efficiency and performance. We present both early works and recently explored directions. We conclude with an analysis of future directions based on recent developments in machine learning methodology.Master Thesis A Tool for Synthetic Evaluation of Active Calibration Algorithms(01. Izmir Institute of Technology, 2022) Dönmez, Buğrahan; Özuysal, Mustafa; Özuysal, MustafaTo calibrate a camera, the choice of poses is very important and different angled poses can increase accuracy. Gathering those poses needs expert intuition in order to constrain all parameters accurately. There are various tools to help users calibrate the camera with its guidance. In this study, two successful calibration tools are tested. Both of them guide the user interactively to obtain the best poses. The first method tries to avoid singular poses and captures the poses that reduce the uncertainty of calibration. The second method uses a different approach. It uses the current calibration state to suggest the next pose. In the end, it verifies the parameters with the specified ones by the user. To test these two methods, ground truth data is needed. The ground truth data is obtained with the help of a 3D modeling program. The suggested poses are generated also with the modeling program and knowing the ground truth camera parameters given in the program, the results of the tools are compared.Article Citation - WoS: 2Citation - Scopus: 2Improving Outdoor Plane Estimation Without Manual Supervision(Springer, 2022) Uzyıldırım, Furkan Eren; Özuysal, MustafaRecently, great progress has been made in the automatic detection and segmentation of planar regions from monocular images of indoor scenes. This has been achieved thanks to the development of convolutional neural network architectures for the task and the availability of large amounts of training data usually obtained with the help of active depth sensors. Unfortunately, it is much harder to obtain large image sets outdoors partly due to limited range of active sensors. Therefore, there is a need to develop techniques that transfer features learned from the indoor dataset to segmentation of outdoor images. We propose such an approach that does not require manual annotations on the outdoor datasets. Instead, we exploit a network trained on indoor images and an automatically reconstructed point cloud to estimate the training ground truth on the outdoor images in an energy minimization framework. We show that the resulting ground truth estimate is good enough to improve the network weights. Moreover, the process can be repeated multiple times to further improve plane detection and segmentation accuracy on monocular images of outdoor scenes.Conference Object Robust Keypoint Matching for Three Dimensional Scenes and Object Recognition(IEEE, 2017) Koksal, Ali; Uzyildirim, Furkan Eren; Ozuysal, MustafaIn this paper, we adapt a recently proposed keypoint matching approach for binary descriptors and planar objects to three dimensional objects. We also evaluate the performance of this approach for a museum object recognition application containing more than one hundred paintings. Moreover, we quantify the effect of selecting only descriptors with high matching ratio on the success rate of the object recognition application.