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

Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades

The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

Analyzing Spatial Patterns in Reefscape Ecology Via Remote Sensing, Benthic Habitat Mapping, and Morphometrics

A growing number of scientists are investigating applications of landscape ecology principles to marine studies, yet few coral reef scientists have examined spatial patterns across entire reefscapes with a holistic ecosystem-based view. This study was an effort to better understand reefscape ecology by quantitatively assessing spatial structures and habitat arrangements using remote sensing and geographic information systems (GIS). Quantifying recurring patterns in reef systems has implications for improving the efficiency of mapping efforts and lowering costs associated with collecting field data and acquiring satellite imagery. If a representative example of a reef is mapped with high accuracy, the data derived from habitat configurations could be extrapolated over a larger region to aid management decisions and focus conservation efforts. The aim of this project was to measure repeating spatial patterns at multiple scales (10s m2 to 10s km2) and to explain the environmental mechanisms which have formed the observed patterns. Because power laws have been recognized in size-frequency distributions of reef habitat patches, this study further investigated whether the property exists for expansive reefs with diverse geologic histories. Intra- and inter-reef patch relationships were studied at three sites: Andavadoaka (Madagascar), Vieques (Puerto Rico), and Saipan (Commonwealth of the Northern Mariana Islands). In situ ecological information, including benthic species composition and abundance, as well as substrate type, was collected with georeferenced video transects. LiDAR (Light Detection and Ranging) surveys were assembled into digital elevation models (DEMs), while vessel-based acoustic surveys were utilized to empirically tune bathymetry models where LiDAR data were unavailable. A GIS for each site was compiled by overlying groundtruth data, classifications, DEMs, and satellite images. Benthic cover classes were then digitized and analyzed based on a suite of metrics (e.g. patch complexity, principle axes ratio, and neighborhood transitions). Results from metric analyses were extremely comparable between sites suggesting that spatial prediction of habitat arrangements is very plausible. Further implications discussed include developing an automated habitat mapping technique and improving conservation planning and delimitation of marine protected areas

The evaluation of Corona and Ikonos satellite imagery for archaeological applications in a semi-arid environment

Archaeologists have been aware of the potential of satellite imagery as a tool almost since the first Earth remote sensing satellite. Initially sensors such as Landsat had a ground resolution which was too coarse for thorough archaeological prospection although the imagery was used for geo-archaeological and enviro-archaeological analyses. In the intervening years the spatial and spectral resolution of these sensing devices has improved. In recent years two important occurrences enhanced the archaeological applicability of imagery from satellite platforms: The declassification of high resolution photography by the American and Russian governments and the deregulation of commercial remote sensing systems allowing the collection of sub metre resolution imagery. This thesis aims to evaluate the archaeological application of three potentially important resources; Corona space photography and Ikonos panchromatic and multispectral imager). These resources are evaluated in conjunction with Landsat Thematic Mapper (TM) imagery over a 600 square km study area in the semi-arid environment around Homs, Syria. The archaeological resource in this area is poorly understood, mapped and documented. The images are evaluated for their ability to create thematic layers and to locate archaeological residues in different environmental zones. Further consideration is given to the physical factors that allow archaeological residues to be identified and how satellite imagery and modern technology may impact on Cultural Resource Management. This research demonstrates that modern high resolution and historic satellite imagery can be important tools for archaeologists studying in semi-arid environments. The imagery has allowed a representative range of archaeological features and landscape themes to be identified. The research shows that the use of satellite imagery can have significant impact on the design of the archaeological survey in the middle-east and perhaps in other environments

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

Building change detection from remotely sensed data using machine learning techniques

As remote sensing data plays an increasingly important role in many fields, many countries have established geographic information systems. However, such systems usually suffer from obsolete scene details, making the development of change detection technology critical. Building changes are important in practice, as they are valuable in urban planning and disaster rescue. This thesis focuses on building change detection from remotely sensed data using machine learning techniques. Supervised classification is a traditional method for pixel level change detection, and relies on a suitable training dataset. Since different training datasets may affect the learning performance differently, the effects of dataset characteristics on pixel level building change detection are first studied. The research is conducted from two angles, namely the imbalance and noise in the training dataset, and multiple correlations among different features. The robustness of some supervised learning algorithms to unbalanced and noisy training datasets is examined, and the results are interpreted from a theoretical perspective. A solution for handling multiple correlations is introduced, and its performance on and applicability to building change detection is investigated. Finally, an object-based post processing technique is proposed using prior knowledge to further suppress false alarms. A novel corner based Markov random field (MRF) method is then proposed for exploring spatial information and contextual relations in changed building outline detection. Corners are treated as vertices in the graph, and a new method is proposed for determining neighbourhood relations. Energy terms in the proposed method are constructed using spatial features to describe building characteristics. An optimal solution indicates spatial features belonging to changed buildings, and changed areas are revealed based on novel linking processes. Considering the individual advantages of pixel level, contextual and spatial features, an MRF based combinational method is proposed that exploits spectral, spatial and contextual features in building change detection. It consists of pixel level detection and corner based refinement. Pixel level detection is first conducted, which provides an initial indication of changed areas. Corner based refinement is then implemented to further refine the detection results. Experimental results and quantitative analysis demonstrate the capacity and effectiveness of the proposed methods