Araç-yaya kazalarını önlemek için stereo görüntü tabanlı uzaklık tespit sistemi geliştirilmesi

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.Anahtar Kelimeler: Stereo görüntü tabanlı araç-yaya kaza önleme sistemi, yaya tespit ve uzaklık tespiti, engel tanımlama, stereo görüntü tabanlı araç çevresel bölge tanımlama. Otonom araçlar ile sürüş destek sistemleri, trafik güvenliği ve araç sürüş kolaylığı açısından önemli avantajlar sunmaktadır. Ayrıca yeni sensör ve donanımsal teknolojilerin gelişimi, daha hızlı ve yüksek başarımlı yazılım metotları ile bu akıllı araç sistemleri her geçen gün daha etkin ve daha güvenli hale gelmektedir. Tez kapsamında, görüntü işleme yöntemleri kullanılarak yayaların, diğer engellerin tespiti, araca olan uzaklıklarının hesaplanması ve sürüş-yer düzleminin bulunmasında kullanılabilecek stereo kamera tabanlı bir sistem geliştirilmiştir. Kamera sistemlerinin yaygınlığı göz önüne alındığında, bu sistemlerin araçlara entegrasyonu diğer yaklaşımlara göre (LIDAR gibi) ekonomik bir çözüm ortaya koymaktadır. Tez çalışması sırasında, görüntü üzerindeki yol, nesne ve yayaların renk özelliklerine göre birbirinden ayırımı için yeni kümeleme ve renk segmentasyonu algoritmaları geliştirilmiş ve sisteme uygulanmıştır. Geliştirilen kümeleme algoritması, verilerin gözetimsiz ve parametre olmadan uzaklık ve yoğunluk özelliklerine göre ayrımını sağlamış ve bu metot görüntü üzerinde renk bölgelerinin bulunmasında kullanılmıştır. Bunun yanında, sisteme HOG filtresi ilave edilerek araç çevresindeki yayalar belirlenmiştir. Ayrıca, yeni bir engel tespit algoritması geliştirilmiş ve diğer yöntemlerle birlikte yol üzerindeki engellerin renk, uzaklık ve komşuluk gibi özelliklerine göre birbirinden ayrımı ve aracın gittiği ortamdaki bölgelerin tespiti sağlanmıştır. Olası yaya bölgeleri incelenirken antropometrik oranlar ve uzaklığa bağlı alansal büyüklükler de göz önüne alınmıştır. Bu tez çalışmasında üretilen sonuçlar ile sürüş destek sistemleri ve otonom araçlar içerisinde, stereo kamera tabanlı ekonomik sistemlerin kullanımının yaygınlaşacağı düşünülmektedir. Yaygınlaşan sürüş destek sistemleri ve otonom araçlar, araç-yaya kazalarını azaltacağından maddi ve manevi kayıplar da azalmış olacak DEVELOPMENT OF DISTANCE ESTIMATION SYSTEM BASED ON STEREO VISION TO PREVENT VEHICLE-PEDESTRIAN ACCIDENTSKeywords: Stereo image-based vehicle-pedestrian accident prevention system, pedestrian detection and distance estimation, obstacle detection, stereo camera-based vehicle environment segmentation. Autonomous vehicles and driving support systems offer significant advantages in terms of traffic safety and vehicle driving convenience. With the development of new sensors and hardware technologies, faster and more powerful software methods, these intelligent vehicle systems are becoming more effective and safer every day. In this study, a stereo vision-based system is developed which identify ground-plane for driving, detect obstacles, calculate the distances of pedestrians and other objects in the driving region. Given the prevalence of camera systems, integration of these systems into vehicles offers an economical solution to other approaches (such as LIDAR). During the thesis study, new clustering and color segmentation algorithms have been developed and applied to the system to distinguish road, objects and pedestrians using color features of the image. The developed clustering algorithm distinguishes the data according to the distance and density properties without parameters and supervision, and this method is used to find color spaces on the image. In addition, the HOG filter is added to the system to determine the pedestrians around the vehicle. Besides, a new obstacle detection algorithm has been developed, and it has been possible to distinguish the obstacles on the road according to the characteristics such as color, depth and neighborhood with identifying the regions in the environment where the vehicle is going. Anthropometric proportions and spatial ranges depending on the distance are also taken into consideration when examining possible pedestrian zones. It is considered that the use of stereo camera based economical systems in driving support systems and autonomous vehicles will be widespread with the results produced in this thesis study. Increased driving support systems and autonomous vehicles will reduce vehicle-pedestrian accidents and will also reduce financial and moral losse

Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions

Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause leakage of uncommon features among contrasts, compromising diagnostic utility. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Theory: Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. Methods: The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. Results: The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms. Conclusion: The proposed compressive sensing method performs fast reconstruction of multi-channel multi-contrast MRI data with improved image quality. It offers reliability against feature leakage in joint reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio

An augmented Lagrangian method for image reconstruction with multiple features

We present an Augmented Lagrangian Method (ALM) for solving image reconstruction problems with a cost function consisting of multiple regularization functions with a data fidelity constraint. The presented technique is used to solve inverse problems related to image reconstruction, including compressed sensing formulations. Our contributions include an improvement for reducing the number of computations required by an existing ALM method, an approach for obtaining the proximal mapping associated with p-norm based regularizers, and lastly a particular ALM for the constrained image reconstruction problem with a hybrid cost function including a weighted sum of the p-norm and the total variation of the image. We present examples from Synthetic Aperture Radar imaging and Computed Tomography

An augmented Lagrangian method for autofocused compressed SAR imaging

We present an autofocus algorithm for Compressed SAR Imaging. The technique estimates and corrects for 1-D phase errors in the phase history domain, based on prior knowledge that the reflectivity field is sparse, as in the case of strong scatterers against a weakly-scattering background. The algorithm relies on the Sparsity Driven Autofocus (SDA) method and Augmented Lagrangian Methods (ALM), particularly Alternating Directions Method of Multipliers (ADMM). In particular, we propose an ADMM-based algorithm that we call Autofocusing Iteratively Re-Weighted Augmented Lagrangian Method (AIRWALM) to solve a constrained formulation of the sparsity driven autofocus problem with an ℓp-norm, p ≤ 1 cost function. We then compare the performance of the proposed algorithm's performance to Phase Gradient Autofocus (PGA) and SDA [2] in terms of autofocusing capability, phase error correction, and computation time

Prognostic value of 18F-FDG PET/CT for identifying high- and low-risk endometrial cancer patients

Objectives: To assess the usefulness of adding PET/CT as a preoperative test for determining the extent of endometrial cancer and discriminating low- and high-risk patients to identify candidates for surgical staging. Material and methods: We retrospectively reviewed 86 patients with pathologically proven endometrial cancer who had undergone preoperative 18F-FDG PET/CT. The prognostic relationships between PET/CT parameters and pathology reports were assessed. Results: The SUVmax was significantly higher in patients with FIGO stage IB or higher compared with those with stage IA; for stage III–IV compared with stage I–II; and for patients with lymph node metastasis compared with those without lymph node metastasis. Using 6.70 as a cut-off for SUVmax, low-risk patients can be identified with a sensitivity of 92.9%. Conclusions: PET/CT imaging can be used not only for determining malignancy and lymph node involvement but also for determining candidates for surgical staging with high sensitivity

Autofocused compressive SAR imaging based on the alternating direction method of multipliers

We present an alternating direction method of multipliers (ADMM) based autofocused Synthetic Aperture Radar (SAR) imaging method in the presence of unknown 1-D phase errors in the phase history domain, with undersampled measurements. We formulate the problem as one of joint image formation and phase error estimation. We assume sparsity of strong scatterers in the image domain, and as such use sparsity priors for reconstruction. The algorithm uses l(p)-norm minimization (p <= 1) [8] with an improvement by integrating the phase error updates within the alternating direction method of multipliers (ADMM) steps to correct the unknown 1-D phase error. We present experimental results comparing our proposed algorithm with a coordinate descent based algorithm in terms of convergence speed and reconstruction quality

Evaluation of the Burnout Levels of Health Care Workers During the Pandemic in Pediatric and Adult Emergency Services

Introduction:During the Coronavirus disease-2019 (COVID-19) pandemic, the emergency services provide seven days/24-hour regular health care, while the risk of burnout is gradually increasing among healthcare workers struggling with the increasing workload. Few studies monitored the mental health of doctors, nurses, and allied health personnel working in emergency departments to support frontline health workers and more data is needed.Methods:The Maslach burnout scale (MBI) was administered voluntarily to doctors, nurses, and allied health personnel in the pediatric emergency and adult emergency services in May 2020 and December 2020. A socio-demographic information form containing questions about the institution, profession, and working conditions was distributed. The socio-demographic data of the same participants on both dates were compared with the effects of the pandemic on their daily lives, working conditions, and subscales of MBI (emotional exhaustion, depersonalization, and personal achievement scores) with an interval of six months (May 2020-December 2020).Results:One hundred seventeen health personnel participated in our study in May 2020 and 122 in December 2020. 95.7% of respondents (112/117) in May 2020; in December 2020, 69.9% (84/122) of them met the criteria in two or more of the subscales of MBI (high emotional exhaustion and depersonalization scores, low personal achievement scores) and were found to be exhausted. In a six-month comparison, it was found that physicians, among physicians, pediatric assistants working in the pediatric emergency department, and healthcare workers aged 29 and younger were better able to cope with burnout.Conclusion:Considering the known harmful effects of burnout on patient care and the well-being of healthcare workers, frontline personnel in emergency services may need more mental support during and after the COVID-19 pandemic. There is a need for more preventive, descriptive, protective, and remedial studies on frontline health workers’ physical and mental health

Joint reconstruction of multi-contrast images: compressive sensing reconstruction using both joint and individual regularization functions

In many clinical settings, multi-contrast images of a patient are acquired to maximize complementary information. With the underlying anatomy being the same, the mutual information in multi-contrast data can be exploited to improve image reconstruction, especially in accelerated acquisition schemes such as Compressive Sensing (CS). This study proposes a CS-reconstruction algorithm that uses four regularization functions; joint L1-sparsity and TV-regularization terms to exploit the mutual information, and individual L1-sparsity and TV-regularization terms to recover unique features in each image. The proposed method is shown to be robust against leakage-of-features across contrasts, and is demonstrated using simulations and in-vivo experiments