Sanal giysi deneme kabini uygulaması için kinect ile insan modeli oluşturma ve kontrolü

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada gerçek zamanlı 3B model kontrol yaklaşımı ve kullanıcıların sanal bir ayna önünde giysileri deneme olanağı bulabilecekleri sanal bir giyinme odası tasarlanması amaçlanmıştır. Oluşturulan sanal kabin içerisinde kullanıcının sanal temsilcisi olan 3B insan modeli oluşturulur. Sisteme entegre olan MS Kinect vb. yardımcı bir donanım kullanılarak, el hareketleri ile ekrandaki giysi listesinden kullanıcı tercih ettiği giysiyi seçer. Daha sonra ise bu seçilen 3B giysi sanal ayna uygulaması üzerinden 3B model üzerine otomatik olarak giydirilir. Seçilen 3B giysilerin model üzerinde tam ve uygun bir şekilde giydirilmesi için yerleştirme, ölçüm alma ve döndürme gibi işlemlerde insan eklemlerinin 3B koordinat bilgileri kullanılmıştır. Geliştirdiğimiz algoritma ile kullanıcının vücut ölçümleri dikkate alınarak uygun olan small, medium, large veya xlarge giysi türü otomatik olarak seçilmekte ve bu bilgi ekran üzerinde görüntülenmektedir. Kullanıcı ve model arasındaki uyumsuzlukları önlemek ve giysilerin renk uyumunu anlayabilmek amacıyla ten rengi seçme özelliği ek olarak sanal kabin üzerinde sunulmuştur. İsteğe bağlı ayna seçenekleriyle de giysiler ve model üzerinde farklı bakış açılarından görüntü sağlanabilmektedir. Bu çalışmada, kullanıcı hareketlerini izlemek ve bu hareketlerin 3B model üzerinde uygulanması, eklem koordinatlarının belirlenmesi, kıyafetlerin kullanıcıya göre boyutlandırılması ve insan vücudu ile giysiler arasında etkili efektler oluşturabilmek için Microsoft Kinect SDK ile bize sunulan özelliklerden ve Unity 3D ile oyun motorlarının sağladığı hızlı hesaplayabilme yeteneklerinden yararlanılmıştır. Geliştirilen arayüz ile gerçekçi bir izlenim oluşturulmuş ve sistem çalışması farklı vücut ölçülerine sahip insanlar üzerinde test edilmiştir. Bu uygulama ile kişilerin fiziksel olarak giysileri denemelerinin zorluğu ve deneme kabinleri önünde oluşan uzun kuyruklar düşünülmüş bu sebeple sanal giysi deneme kabini uygulaması üzerinden online alışverişlere katkı sağlanması amaçlanmıştır. Uygulama Kinect desteklidir ve uygulama başlatıldığında sistem için gerekli bazı veriler otomatik olarak bu cihaz üzerinden elde edilmektedir. Bununla birlikte Kinect cihazı bulunmayan sistemlerde, uygulama grafik arayüzündeki düğmeler kullanılarak seçimler yapılabilir.This thesis proposes a real time 3D virtual model controlling approach and a virtual dressing room application to enable users to try virtual garments on in front of a virtual mirror. A virtual representation of the user appears in a virtual changing room. The user's hand motions select the clothes from a list on the screen. Afterwards the selected virtual clothes appears on a humanoid model in the virtual mirror. For the purpose of aligning the 3D garments and shoes with the model, 3D locations of the joints are used for positioning, scaling and rotating. By using our developed algorithm, small, medium, large or xlarge garment size is selected automatically and this information is shown on the screen. Then, we apply skin colour detection to handle the unwanted occlusions between the user and the model. Some optional mirror selection buttons make it possible to have multiple viewing angles on the model. In this study, we benefit from the Microsoft Kinect SDK in order to follow the user's movements, move the avatar, read 3D information of joints' positions, coordinate the suitable clothe try-ons and provide depth sort effect for the human body and clothes. In order to use the rapid calculation attributes of game engines, we used Unity 3D Game Engine. By developed interface of the application, the system works accurately and it is tested by different users. With this application it is aimed to contribute to online shopping and reduce the loss of time in the store shopping and effort of trying garments on in fitting rooms. When the application is started, the system uses Kinect and reads user's information. However, in systems without the Kinect device, application choices can be made using the buttons on the graphical interface

Accurate Analysis of the Spatial Pattern of Reflected Light and Surface Orientations Based on Color Illumination

3D Recovery approaches require a variety of clues to obtain shape information. The shape from shading (SFS) method uses shading information in images to estimate depth maps. Although shading contains detailed information, it causes some well-known ambiguities such as convex-concave ambiguity. In this study, a system installation, using red, green, and blue illumination, and an algorithm, processing reflections on the surface, were proposed for the accurate analysis of surface orientations, and ambiguity problems. Surface orientations, erroneously predicted by six different methods, were improved by implementing the proposed system. Consequently, the correct orientation of the surface points was determined by removing the ambiguities in images taken without considering the location of illumination, and all the tested methods provided successful results using the proposed system

Determining of the effects of paclobutrazol treatments on seedling height control of wild Gypsophila bicolor (Freyn & Sint.) Grossh

Within the scope of this study, seeds were collected from Gypsophila bicolor (Freyn&Sint.) Grossh plants in their natural environment and two different locations (Turkey). The research was carried out to determine the effects of paclobutrazol applied at different concentrations (0.6, 0.9, 1.2, 1.5- and 2.0-ml L-1) on the seedling height control in the cotyledon leaf stage of G. bicolor. As a result of the study, it was determined that the decrease in the number of leaves per plant was determined at a dose of 1.2 mg L-1 paclobutrazol. The effect of paclobutrazol treatments on the seedling height control of G. bicolor changed according to the genotype as well as the dose. As the dose of paclobutrazol applied increased, the number of side branches decreased. It was determined that the seedling stem thickness increased in 47.71% with the dose of 2.0 mg L-1 when compared to the control treatment. The application in which the highest chlorophyll (SPAD) value was obtained from the application with a dose of 1.5 mg L-1. The chlorophyll value of G2 (Genotype 2 (ERZ) (G2)) was higher than that of G1 (Genotype 1 (VAN)). Seedling biomass of G1 increased in 15.87% in 1.2 mg L-1 treatment when compared to control. In the present study, darker green leaves were obtained from the highest dose of paclobutrazol, 2.0 mg L-1. As a general result, it was concluded that 1.5 mg L-1 dose of paclobutrazol was sufficient for both genotypes for plant height control in the seedling period

Who is in charge here? Exploring the missing link in the policy-action continuum of Turkish counterterrorism policies against the PKK

Although Turkey has implemented various counterterrorism policies to address the terror campaign led by the Kurdistan Workers’ Party, the problem remains unresolved. The policymakers blame the implementers for failed counterterrorism policies, yet they are responsible for the context in which the implementation occurs. This paradox raises an intriguing question: Who is in charge here, politics or administration? The query requires the thesis to explore ‘the missing link’ between policy and action in the implementation processes of Turkish counterterrorism policies against the PKK. The implementation stage of policy processes was once ‘the missing link’, in need of scrutiny. Although the implementation literature has explored this missing link across various policies, the implementation stage of counterterrorism policies remains missing. This thesis fills this gap employing a qualitative cases study design accompanied by 30 semi-structured elite interviews. The thesis’ analytical framework was established on the partial use and combination of the top-down, bottom-up and hybrid approaches to implementation and defiance/desistance and deterrence approaches to counterterrorism. Implementers’ roles and behaviours were linked to policies via implementation paradigms (administrative, political, experimental, and symbolic) derived from Matland’s (1995) ambiguity/conflict model. The evidence-based analysis of the components of the paradigms enabled the research to explore the missing link in the policy-action continuum and shown that politics is more likely to be in charge than administration. The thesis argues that Turkish counterterrorism policies remain unproductive, and even counterproductive because they are formulated too ambiguously in contested political contexts and carried out by low-quality implementers in conflictual circumstances at the local level. Decision-makers either intentionally leave ambiguities in policies or they lack the capacity to produce sound policies. Thus, conflicting implementers find rooms to employ divergent roles and behaviours at the street level. This prevents precise implementation and turns the implementation into a barrier, resulting in policy failures

Surface inspection system for industrial components based on shape from shading minimization approach

An inspection system using estimated three-dimensional (3-D) surface characteristics information to detect and classify the faults to increase the quality control on the frequently used industrial components is proposed. Shape from shading (SFS) is one of the basic and classic 3-D shape recovery problems in computer vision. In our application, we developed a system using Frankot and Chellappa SFS method based on the minimization of the selected basis function. First, the specialized image acquisition system captured the images of the component. To eliminate noise, wavelet transform is applied to the taken images. Then, estimated gradients were used to obtain depth and surface profiles. Depth information was used to determine and classify the surface defects. Also, a comparison made with some linearization-based SFS algorithms was discussed. The developed system was applied to real products and the results indicated that using SFS approaches is useful and various types of defects can easily be detected in a short period of time. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE

A linearization-based hybrid approach for 3D reconstruction of objects in a single image

The shape-from-shading (SFS) technique uses the pattern of shading in images in order to obtain 3D view information. By virtue of their ease of implementation, linearization-based SFS algorithms are frequently used in the literature. In this study, Fourier coefficients of central differences obtained from gray-level images are employed, and two basic linearization-based algorithms are combined. By using the functionally generated surfaces and 3D reconstruction datasets, the hybrid algorithm is compared with linearization-based approaches. Five different evaluation metrics are applied on recovered depth maps and the corresponding gray-level images. The results on defective sample surfaces are also included to show the effect of the algorithm on surface reconstruction. The proposed method can prevent erroneous estimates on object boundaries and produce satisfactory 3D reconstruction results in a low number of iterations

A Data-Driven Multi-Regime Approach for Predicting Energy Consumption

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach

The investigation of bioremediation potential of Bacillus subtilis and B. thuringiensis isolates under controlled conditions in freshwater

Bioremediation is widely used to remove water pollution as environmentally friendly smart solutions. In this study, Bacillus isolates were investigated in terms of the effectiveness of single and multiple cultures in eliminating aquatic pollution related to aquaculture activities. In the established experimental setups, the environments where Bacillus isolates were inoculated with single and multiple cultures at 1 x 10(7) CFU/mL were evaluated comparatively with control groups without these isolates, and total aerobic mesophilic bacterial counts were performed in the petri dish by inoculation method. At the end of the 6 days of the experiment, in the environment in which single and multiple cultures of Bacillus isolates were presented with 17-20 +/- 0.05 degrees C temperature and 5.1-8.1 pH 2-4.6 mg/l dissolved oxygen values (O-2), 2% increase in total phosphorus (TP) value was observed. On the other hand, 4% removal of Ammonia-nitrogen (NH3-N), 80% removal of Nitrite-nitrogen (NO2-N), and 100% removal of Nitrate-nitrogen (NO3-N) were observed. In the changes in heavy metal concentrations, the removal of Ni, Cr, Se, Al, Cd, Mn, Fe, and B was observed from highest to lowest as 57%, 50%, 50%, 43%, 40%, 23%, 5%, and 2%, respectively. It also has been seen that B. thuringiensis isolate was observed to be more effective than B.subtilis in metal removal

Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions

Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches the highest accuracy rate was obtained using the MobileNetV2 model with a rate of 80.79% accuracy