Combining Features and Semantics for Low-level Computer Vision

Visual perception of depth and motion plays a significant role in understanding and navigating the environment. Reconstructing outdoor scenes in 3D and estimating the motion from video cameras are of utmost importance for applications like autonomous driving. The corresponding problems in computer vision have witnessed tremendous progress over the last decades, yet some aspects still remain challenging today. Striking examples are reflecting and textureless surfaces or large motions which cannot be easily recovered using traditional local methods. Further challenges include occlusions, large distortions and difficult lighting conditions. In this thesis, we propose to overcome these challenges by modeling non-local interactions leveraging semantics and contextual information. Firstly, for binocular stereo estimation, we propose to regularize over larger areas on the image using object-category specific disparity proposals which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The disparity proposals encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel-based graphical model and demonstrate its benefits especially in reflective regions. Secondly, for 3D reconstruction, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. Evaluations with respect to LIDAR ground-truth on a novel challenging suburban dataset show the advantages of modeling structural dependencies between objects. Finally, motivated by the success of deep learning techniques in matching problems, we present a method for learning context-aware features for solving optical flow using discrete optimization. Towards this goal, we present an efficient way of training a context network with a large receptive field size on top of a local network using dilated convolutions on patches. We perform feature matching by comparing each pixel in the reference image to every pixel in the target image, utilizing fast GPU matrix multiplication. The matching cost volume from the network's output forms the data term for discrete MAP inference in a pairwise Markov random field. Extensive evaluations reveal the importance of context for feature matching.Die visuelle Wahrnehmung von Tiefe und Bewegung spielt eine wichtige Rolle bei dem Verständnis und der Navigation in unserer Umwelt. Die 3D Rekonstruktion von Szenen im Freien und die Schätzung der Bewegung von Videokameras sind von größter Bedeutung für Anwendungen, wie das autonome Fahren. Die Erforschung der entsprechenden Probleme des maschinellen Sehens hat in den letzten Jahrzehnten enorme Fortschritte gemacht, jedoch bleiben einige Aspekte heute noch ungelöst. Beispiele hierfür sind reflektierende und texturlose Oberflächen oder große Bewegungen, bei denen herkömmliche lokale Methoden häufig scheitern. Weitere Herausforderungen sind niedrige Bildraten, Verdeckungen, große Verzerrungen und schwierige Lichtverhältnisse. In dieser Arbeit schlagen wir vor nicht-lokale Interaktionen zu modellieren, die semantische und kontextbezogene Informationen nutzen, um diese Herausforderungen zu meistern. Für die binokulare Stereo Schätzung schlagen wir zuallererst vor zusammenhängende Bereiche mit objektklassen-spezifischen Disparitäts Vorschlägen zu regularisieren, die wir mit inversen Grafik Techniken auf der Grundlage einer spärlichen Disparitätsschätzung und semantischen Segmentierung des Bildes erhalten. Die Disparitäts Vorschläge kodieren die Tatsache, dass die Gegenstände bestimmter Kategorien nicht willkürlich geformt sind, sondern typischerweise regelmäßige Strukturen aufweisen. Wir integrieren sie für die komplexe Objektklasse 'Auto' in Form eines nicht-lokalen Regularisierungsterm in ein Superpixel-basiertes grafisches Modell und zeigen die Vorteile vor allem in reflektierenden Bereichen. Zweitens nutzen wir für die 3D-Rekonstruktion die Tatsache, dass mit der Größe der rekonstruierten Fläche auch die Wahrscheinlichkeit steigt, Objekte von ähnlicher Art und Form in der Szene zu enthalten. Dies gilt besonders für Szenen im Freien, in denen Gebäude und Fahrzeuge oft vorkommen, die unter fehlender Textur oder Reflexionen leiden aber ähnlichkeit in der Form aufweisen. Wir nutzen diese ähnlichkeiten zur Lokalisierung von Objekten mit Detektoren und zur gemeinsamen Rekonstruktion indem ein volumetrisches Modell ihrer Form erlernt wird. Dies ermöglicht auftretendes Rauschen zu reduzieren, während fehlende Flächen vervollständigt werden, da Objekte ähnlicher Form von allen Beobachtungen der jeweiligen Kategorie profitieren. Die Evaluierung auf einem neuen, herausfordernden vorstädtischen Datensatz in Anbetracht von LIDAR-Entfernungsdaten zeigt die Vorteile der Modellierung von strukturellen Abhängigkeiten zwischen Objekten. Zuletzt, motiviert durch den Erfolg von Deep Learning Techniken bei der Mustererkennung, präsentieren wir eine Methode zum Erlernen von kontextbezogenen Merkmalen zur Lösung des optischen Flusses mittels diskreter Optimierung. Dazu stellen wir eine effiziente Methode vor um zusätzlich zu einem Lokalen Netzwerk ein Kontext-Netzwerk zu erlernen, das mit Hilfe von erweiterter Faltung auf Patches ein großes rezeptives Feld besitzt. Für das Feature Matching vergleichen wir mit schnellen GPU-Matrixmultiplikation jedes Pixel im Referenzbild mit jedem Pixel im Zielbild. Das aus dem Netzwerk resultierende Matching Kostenvolumen bildet den Datenterm für eine diskrete MAP Inferenz in einem paarweisen Markov Random Field. Eine umfangreiche Evaluierung zeigt die Relevanz des Kontextes für das Feature Matching

RbA: Segmenting Unknown Regions Rejected by All

Standard semantic segmentation models owe their success to curated datasets with a fixed set of semantic categories, without contemplating the possibility of identifying unknown objects from novel categories. Existing methods in outlier detection suffer from a lack of smoothness and objectness in their predictions, due to limitations of the per-pixel classification paradigm. Furthermore, additional training for detecting outliers harms the performance of known classes. In this paper, we explore another paradigm with region-level classification to better segment unknown objects. We show that the object queries in mask classification tend to behave like one \vs all classifiers. Based on this finding, we propose a novel outlier scoring function called RbA by defining the event of being an outlier as being rejected by all known classes. Our extensive experiments show that mask classification improves the performance of the existing outlier detection methods, and the best results are achieved with the proposed RbA. We also propose an objective to optimize RbA using minimal outlier supervision. Further fine-tuning with outliers improves the unknown performance, and unlike previous methods, it does not degrade the inlier performance

Karma bir balast suyu arıtım sistemi ve elektrokimyasal teknoloji

The transportation of exotic species in ballast tanks is one of the most important environmental problems of the ship industry at the global levels. The technologies that will be adapted to both existing and new built ships should be developed immediately to minimize problems caused by the ballast water and sediment. There are a number of techniques taken into consideration to eliminate the organisms in ballast water. However, it is generally agreed that a single treatment method would not be sufficient to prevent the translocation these organisms. Consequently various projects which focus on the hybrid systems were initiated. These systems generally include one primary treatment and one or more secondary treatment techniques. Primary treatment is achieved by mechanical treatment such as filters. Secondary treatment may consist several physical and chemical options. Chlorine disinfection is one of the most applied techniques. The main subjects of concern about employing chlorine disinfection for ballast water organisms is the safety risk during handling and onboard storage of chlorine gas or HOCl solutions. On the other hand, electrochemically generation of disinfectants, especially oxidants as HOCl, is an emerging technique. Electrochemical generation of active chlorine on board would eliminate those unfavorable features of chlorine disinfection. This work has been prepared from the doctoral thesis which is titled as "Electrochemical Cell Application for Ballast Water Treatment" and submitted to Institute of Science and Technology of Istanbul Technical University. This study has been conducted within the project "BaWaPla - Sustainable Ballast Water Management Plant", funded by the European Union under contract number 031529, which is started at 15/11/2006 and finalized at 15/05/2010. A new hybrid ballast water treatment system has been developed within the project. This self-controlled system consists of filter systems, UV and electrochemical technologies. The electrochemical component of BaWaPla produces active substances onboard through electrolysis of seawater and eliminates the require-ment to carry or store hazardous and corrosive chemicals. A laboratory system has been prepared by Project partner LVPG GmbH, Germany and provided to Istanbul Technical University. This system is used for test assumptions and proposals for the best and optimal cell design. Employing electrolysis techniques to produce disinfectants, saline water/seawater is introduced into an electrochemical cell in the heart of the test system. Electrochemical reaction within the cell results in the production of highly effective "Hypochlorous acid rich" disinfectant. Disinfectant fluid can be affected by the design of the fluid path within the electrochemical cell, the selection of material used to produce the permeable membrane that separates the fluid paths or to direct solution past the anode and cathode (electrodes) as well as the electrical current applied to the electrodes. The choice of materials used for coating the relevant electrodes must also be considered. In this study five different electrochemical cells are assessed for BaWaPla system. The cells are supplied from FumaTech GmbH, Germany. The cells are referred as "standard cell, FTEC 100, FTEC 500, EC 100 Nr. 201, EC 100Nr. 240". The changing parameters of the cell designs are the geometry of electrodes, the dimensions of electrodes and the materials used for electrodes and their coatings. The results show that, the enlargement of electrode surface results in more chlorine figures in di-sinfectant. On the other hand, suitable electrode and coating material are essential for "reverse polarity" operation to avoid scaling of Ca2+ and Mg2+ on electrodes and clogging the membrane. Taken into consideration of these results of laboratory works, FumaTech GmbH produced new cells for BaWaPla. These cells have the electrode dimensions as FTEC 500 and the material used for electrodes and their coatings is the same as EC 100 Nr. 201. The cells have capacity of 500 L/h disinfectant production and they have the ability to be run reverse polarity so that a self cleaning process takes place. Six of these cells are employed within the land based pilot BaWaPla system at Blyth-England on August 2009. The pilot system achieved IMO (International Maritime Organi-zation) standards and it is ready for IMO approval.  Keywords: Ballast water treatment, electrochemical cell, chlorine generation.Balast tanklarında taşınan yabancı türler dünya gemi inşa endüstrisinin küresel boyuttaki en önemli çevresel problemlerinden birisidir. Bu problemin çözümüne yönelik olarak son 10-15 yılda birçok çalışma tamamlanmıştır. Bununla birlikte bu çalışmalar, kullanılan yönteme ve balast suyunda yer alan organizmalara bağlı olarak farklı sonuçlar vermektedir. Bu nedenle günümüzde gemi üzerinde balast suyu arıtımı konusunda yapılan çalışmaların çoğu birden fazla yöntemin bir arada kullanıldığı karma sistemler üzerinde yoğunlaşmaktadır. Klor gerek içme suyu dezenfeksiyonunda kullanılan en eski ve en genel yöntem olması, gerekse büyük hacimlerdeki sularda istenmeyen organizmaları gidermede de kullanılabilmesi nedeniyle balast suyu dezenfeksiyonu için önemli bir alternatif oluşturmaktadır. Ancak başta klor olmak üzere, dezenfektanların gemi üzerinde depolanması ve kullanılması gemi ve mürettebat güvenliği açısından riskler içermektedir. Diğer taraftan Cl2 gazı HOCl olmak üzere çeşitli dezenfektanların elektrokimyasal olarak üretimi gün geçtikçe önem kazanmakta ve balast suyu arıtımı için de alternatif haline gelmektedir. Bu çalışma Avrupa Birliği 6. Çerçeve Programı tarafından desteklenen 031529 kontrat numaralı araştırma projesi BaWaPla (Sustainable Ballast Water Management Plant) sonunda hayata geçirilen filtre, UV ve elektrokimyasal teknolojilerin bir arada kullanıldığı karma sistem için elektrokimyasal hücrelerin geliştirilme ve optimizasyon aşamasındaki laboratuvar çalışmalarının bir kısmını içermektedir. 3.5 yıllık Proje süresinin iki yıllık dönemde birbirinden farklı şekilde tasarlanmış 5 elektroliz hücresi farklı çalışma koşullarında test edilmiştir. Laboratuvar çalışmalarının sonuçları dikkate alınarak yeni bir hücre tasarımı gerçekleştirilmiş ve işletim parametreleri belirlenmiştir. Geliştirilen hücreler, Ağustos ve Eylül 2009’da Blyth-İngiltere’de kurulan büyük ölçekli pilot sistemde de kullanılarak test edilmiştir. Gerçekleştirilen testlerde IMO (International Maritime Organization-Uluslararası Denizcilik Örgütü) tarafından imzaya açılan “Gemilerin Balast Suları ve Sedimanının Kontrolü ve Yönetimi” sözleşmesinde yer alan deşarj standartları sağlamıştır. BaWaPla sistemi, IMO onayına hazır durumdadır. Anahtar Kelimeler: Balast suyu arıtımı, elektrokimyasal hücre, klor üretimi

Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes

In the life cycle of highly automated systems operating in an open and dynamic environment, the ability to adjust to emerging challenges is crucial. For systems integrating data-driven AI-based components, rapid responses to deployment issues require fast access to related data for testing and reconfiguration. In the context of automated driving, this especially applies to road obstacles that were not included in the training data, commonly referred to as out-of-distribution (OoD) road obstacles. Given the availability of large uncurated recordings of driving scenes, a pragmatic approach is to query a database to retrieve similar scenarios featuring the same safety concerns due to OoD road obstacles. In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis. Our proposed method leverages the recent advances in OoD segmentation and multi-modal foundation models to identify and efficiently extract safety-relevant scenes from unlabeled videos. We present a first approach for the novel task of text-based OoD object retrieval, which addresses the question ''Have we ever encountered this before?''.Comment: 11 pages, 7 figures, and 3 table

NOMA TABANLI BİLİŞSEL RADYO SİSTEMLERİNDE SİNİR AĞI YÖNTEMLERİ İLE ERGODİK KAPASİTE TAHMİNİ VE BAŞARIM ANALİZİ

Bu çalışmada, bilişsel radyo (BR) tabanlı dikgen olmayan çoklu erişim tekniği (NOMA) kullanılarak, yakın kullanıcıya ait toplam ergodik kapasite değerinin, önerilen ileri beslemeli geri yayılımlı yapay sinir ağı (YSA) ve doğrusal olmayan dışsal girdili otoregresif ağ (Nonlinear Autoregressive Network with Exogenous Inputs, NARX) modeli ile farklı eğitim algoritmaları yoluyla yüksek doğruluk oranında ve hızlı eğitim sürelerinde tahmin edilmesi amaçlanmıştır. Sinir ağında kullanılan veri seti, üstel sönümleme kanalı karakteristiği ile modellenen BR-NOMA sistem modelinden elde edilmiştir. Denetimli öğrenme yöntemi kullanılarak tasarlanan YSA’ya girdi ve çıktı verileri öğretilerek yakın kullanıcıya ait ergodik kapasite tahmini yapılmıştır. YSA ve NARX sinir ağları başarımı değerlendirilirken eğitim süresi, iterasyon sayısı, ağın doygunluğa ulaşmaması durumları göz önünde bulundurulmuştur. Yakın kullanıcıya ait gerçek ergodik kapasite değeri ile ileri beslemeli geri yayılımlı YSA ve NARX ağlarının tahmin etmiş olduğu değerler karşılaştırılmıştır. Önerilen sinir ağlarının Levenberg-Marquardt, Bayesian ve Scaled-Conjugate eğitim algoritmaları altındaki performans analizi, hatanın minimuma ulaştığı epok değer grafiği, hata histogram analizi ve eğitim durum analizi açılarından incelenmiştir

İki Uçlu Bozukluk Sağaltım Kılavuzu

Bilim dünyasında daha kırk yıl öncesine kadar çocukların depresyon geçirip geçirmediklerineyönelik derin şüpheler varken bugün erişkin duygudurum bozukluğunun yüzde kaçınıngerçekten “erişkin başlangıçlı” duygudurum bozukluğu olduğu tartışılmaktadır.Çocuk ve ergenlerde tanımlanan kronik, dönemsel olmayan tablolar nedeniyle süren tanısalölçüt tartışmaları, iki uçlu bozukluğun toplumdaki sıklığının tam olarak saptanmasınıgüçleştirmektedir. Özellikle ergenlik öncesi dönemde görülen duygudurum dalgalanması,aşırı irritabilite ve hızlı duygudurum değişimlerinin doğru tanılanması konusunda halen ortakbir yol izlemek mümkün olmamaktadır. Bunlar gerçekte bir iki uçlu duygudurum bozukluğuolup olmadığı; ya da başka türlü bir sınıflandırılma yapılması gerekip gerekmediğihususundaki sorular güncelliğini korumaktadır. Bu bölümde çocuk ve gençlerde görülen ikiuçlu bozukluğa ilişkin sağaltım seçeneklerinin gözden geçirilmesi planlanmaktadır.</p