Learning Pose Invariant and Covariant Classifiers from Image Sequences

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely open problem. Moreover, abundance of low cost mobile platforms and novel application areas, such as real-time Augmented Reality, constantly push the performance limits of existing methods. There is a need to modify and adapt these to meet more stringent speed and capacity requirements. In this thesis, we aim to overcome the difficulties due to the multi-view nature of the object detection task. We significantly improve upon existing statistical keypoint matching algorithms to perform fast and robust recognition of image patches independently of object pose. We demonstrate this on various 2D and 3D datasets. The statistical keypoint matching approaches require massive amounts of training data covering a wide range of viewpoints. We have developed a weakly supervised algorithm to greatly simplify their training for 3D objects. We also integrate this algorithm in a 3D tracking-by-detection system to perform real-time Augmented Reality. Finally, we extend the use of a large training set with smooth viewpoint variation to category-level object detection. We introduce a new dataset with continuous pose annotations which we use to train pose estimators for objects of a single category. By using these estimators' output to select pose specific classifiers, our framework can simultaneously localize objects in an image and recover their pose. These decoupled pose estimation and classification steps yield improved detection rates. Overall, we rely on image and video sequences to train classifiers that can either operate independently of the object pose or recover the pose parameters explicitly. We show that in both cases our approaches mitigate the effects of viewpoint changes and improve the recognition performance

CA-ARBAC: privacy preserving using context-aware role-based access control on Android permission system

Existing 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

Pose Estimation for Category Specific Multiview Object Localization

We propose an approach to overcome the two main challenges of 3D multiview object detection and localization: The variation of object features due to changes in the viewpoint and the variation in the size and aspect ratio of the object. Our approach proceeds in three steps. Given an initial bounding box of fixed size, we first refine its aspect ratio and size. We can then predict the viewing angle, under the hypothesis that the bounding box actually contains an object instance. Finally, a classifier tuned to this particular viewpoint checks the existence of an instance. As a result, we can find the object instances and estimate their poses, without having to search over all window sizes and potential orientations. We train and evaluate our method on a new object database specifically tailored for this task, containing real-world objects imaged over a wide range of smoothly varying viewpoints and significant lighting changes. We show that the successive estimations of the bounding box and the viewpoint lead to better localization results

Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods

Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.   Keywords: light rail transit, multiple regression, artificial neural networks, public transportatio

THE EVALUATION OF THE APPLICABILITY OF BRT SYSTEMS IN İZMIR

Bu çalışmada, otobüs ve hafif raylı sistemin kendine özgü avantajlarını bir arada barındıran hızlı otobüs sistemi, planlama ve uygulama açısından irdelenmiş; planlamada ve karar aşamasında karşılaşılabilecek problemler ortaya konulmaya çalışılmıştır. İzmir kenti örneği üzerinde, planlamanın ilk aşamasını oluşturabilecek basit bir mevcut sistem verimlilik analizi uygulanmış; kent merkezini körfezin güney şeridiyle birleştiren bir hızlı otobüs uygulamasının yararlı olabileceği sonucuna varılmıştır. In this study, bus rapid transit systems which include some specific advantages of urban bus and light rail transit systems are investigated for planning and application points of views and the problems which may be encountered in decision making process are evaluated. An elementary efficiency analysis for urban bus systems which may be reconstructed for a bus rapid system is suggested. The analysis of İzmir urban bus system shows that a bus rapid system connecting the central business district and south coast of the İzmir Bay can be an efficient transportation investmen

AN INVESTIGATION BASED ON PASSENGER AND FREIGHT DEMANDS VARIATION OF ADNAN MENDERES AIRPORT

İzmir, ülkenin en önemli sanayi ve ticaret merkezlerinden biri olmasının yanısıra son yıllarda artan turizm hacmiyle de oldukça önemli bir merkez haline gelmiştir. Özellikle 2003 yılından sonra sivil havacılığın teşvik edilmesi ve artan talepler doğrultusunda hızla gelişen havayolu taşımacılığında İzmir/Adnan Menderes Havaalanı önemli bir konumdadır. Sosyo–ekonomik kalkınma, bireylerin ulaşım alışkanlıklarının değişmesine neden olmuş ve havaalanları için yıllara bağlı olarak sosyo–ekonomik veriler doğrultusunda yolcu ve yük taşımacılığında nasıl bir değişim olacağı önem arz eden bir konu haline gelmiştir. Çalışma kapsamında, İzmir/Adnan Menderes Havaalanı yolcu ve yük sayısındaki değişimler, sosyo –ekonomik göstergeler ile incelenmiştir. Çalışmalar sonucunda yolcu ve yük değerlerinin özellikle Gayrisafi Yurtiçi Hasıla ile yakından ilişkili olduğu görülmüştür. İthalat ve ihracat verileri ile havayolu yük taşımacılığının, araç sayısı ile yolcu ve uçak sayısının ilişkili olduğu ve bu nedenle havalanına gelecekteki taleplerin hesaplanmasında kullanılabilecekleri anlaşılmıştır. Ayrıca, iç hatlardaki yüksek doluluk oranı dış hatlarda yakalanamamakta, İzmir'in uluslararası uçuş olanaklarının geliştirilmemesi durumunda dış hatlar terminalinin kapasitesinin çok altında hizmet vermeye devam edeceği belirlenmiştir. Izmir is one of the most important industrial and commercial center of Turkey. In recent years with the increasing volume of tourism the city has become a significant city of the country. After 2003, by the promotion of civil aviation and the increasing demands for air transportation Izmir has become one of the most important air transportation center. With the increasing in socio–economic standards, transportation habits of people have also changed. The changing in the passenger and freight transport for the future has become one of the most important issues for İzmir/Adnan Menderes Airport. In this study, the changes in passenger and freight values have investigated with socio–economic data. The results have shown that, passenger and freight values have a significant relation with Gross Domestic Product (GDP). There is a similar relation between the values of import, export and air freight transportation. The number of total vehicle and the number of passenger – freight have also significant relation. By this way, all these data have been able to use to determine the future demand of the airport

Accelerating mobile object tracking

24th Signal Processing and Communication Application Conference, SIU 2016; Zonguldak; Turkey; 16 May 2016 through 19 May 2016Bu bildiride modern işlemcilerin Tekil İşlem Çoklu Veri (TİÇV) komutlarıyla hızlandırılmış bir nesne takip modülü içeren mobil bir artırılmış gerçeklik uygulaması sunulmaktadır. Hem standart C++ hem de ARM işlemciler için geliştirilen TİÇV komut seti olan NEON ile kodlanmış verimli bir Sıfır Ortalamalı Farkların Kareleri Toplamı (SOFKT) yöntemi detaylandırılmıştır. Bu iki yöntemin mobil cihaz üzerinde çalışma hızları ölçülerek karşılaştırılmıştır.In this paper, we present a mobile augmented reality implementation with an accelerated tracking module using Single Instruction Multiple Data (SIMD) instructions available in modern CPUs. We detail the implementation of an efficient Zero-Mean Sum of Squared Differences (ZMSSD) algorithm both using standard C++ and the SIMD NEON instruction set of ARM processors. We compare the numerical measurements of tracking speed on a mobile device of both versions

Ground texture classification with deep learning

26th IEEE Signal Processing and Communications Applications Conference, SIU 2018; Altın Yunus Resort ve Thermal Hotel, Izmir; Turkey; 2 May 2018 through 5 May 2018Bu çalışmada ImageNet veri setinde daha önceden eğitilmiş farklı mimarideki derin sinir ağlarının transfer öğrenmesi yolu ile zemin dokularının sınıflandırılması için kullanılması araştırılmıştır. Yedi farklı zeminden toplanan görüntüler ile yeni bir zemin dokusu veri seti oluşturulmuştur. Bu veri seti ile derin sinir ağları kısmen ya da mümkün olduğunda tüm katmanlarıyla yeniden eğitilmiştir. Sonuçlar küçük imgeler kullanıldığında bile zemin dokularının başarıyla sınıflandırıldığını göstermektedir.In this study, we investigate the use of transfer learning on various deep neural network architectures pretained on the ImageNet data set for ground texture classification purposes. We introduce a new ground texture data set collected from seven different areas. We retrain deep neural network's last layer or when possible the full set of layers on this data set. The results show that it is possible to discriminate the ground textures even when very small images are used

Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama

Bu çalışmada mobil artırılmış gerçeklik için kullanılabilecek bir nesne arama yöntemi sunulmaktadır. Temel olarak yöntem anahtar nokta betimleyicilerinin eşleştirilmesine ve bu anahtar nokta eşlerinin geometrik kıstaslar ile süzülmesine dayanmaktadır. Eşlemenin hızlandırılması için gerekli iyileştirmeler detayları ile verilmektedir. Ayrıca, Yerelliğe Duyarlı Karma işleminin performansının bilgi erişim yaklaşımlarından faydalanılarak arttırılabileceği de gösterilmiştir