Disparate View Matching

Matching of disparate views has gained significance in computer vision due to its role in many novel application areas. Being able to match images of the same scene captured during day and night, between a historic and contemporary picture of a scene, and between aerial and ground-level views of a building facade all enable novel applications ranging from loop-closure detection for structure-from-motion and re-photography to geo-localization of a street-level image using reference imagery captured from the air. The goal of this work is to develop novel features and methods that address matching problems where direct appearance-based correspondences are either difficult to obtain or infeasible because of the lack of appearance similarity altogether. To address these problems, we propose methods that span the appearance-geometry spectrum in terms of both the use of these cues as well as the ability of each method to handle variations in appearance and geometry. First, we consider the problem of geo-localization of a query street-level image using a reference database of building facades captured from a bird\u27s eye view. To address this wide-baseline facade matching problem, a novel scale-selective self-similarity feature that avoids direct comparison of appearance between disparate facade images is presented. Next, to address image matching problems with more extreme appearance variation, a novel representation for matchable images expressed in terms of the eigen-functions of the joint graph of the two images is presented. This representation is used to derive features that are persistent across wide variations in appearance. Next, the problem setting of matching between a street-level image and a digital elevation map (DEM) is considered. Given the limited appearance information available in this scenario, the matching approach has to rely more significantly on geometric cues. Therefore, a purely geometric method to establish correspondences between building corners in the DEM and the visible corners in the query image is presented. Finally, to generalize this problem setting we address the problem of establishing correspondences between 3D and 2D point clouds using geometric means alone. A novel framework for incorporating purely geometric constraints into a higher-order graph matching framework is presented with specific formulations for the three-point calibrated absolute camera pose problem (P3P), two-point upright camera pose problem (Up2p) and the three-plus-one relative camera pose problem

Multi-view monocular pose estimation for spacecraft relative navigation

This paper presents a method of estimating the pose of a non-cooperative target for spacecraft rendezvous applications employing exclusively a monocular camera and a threedimensional model of the target. This model is used to build an offline database of prerendered keyframes with known poses. An online stage solves the model-to-image registration problem by matching two-dimensional point and edge features from the camera to the database. We apply our method to retrieve the motion of the now inoperational satellite ENVISAT. The combination of both feature types is shown to produce a robust pose solution even for large displacements respective to the keyframes which does not rely on real-time rendering, making it attractive for autonomous systems applications

Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

Build 3D Abstractions with Wireframes

This chapter serves as an introduction to 3D representations of scenes or Structure From Motion (SfM) from straight line segments. Lines are frequently found in captures of man-made environments, and in nature are mixed with more organic shapes. The inclusion of straight lines in 3D representations provide structural information about the captured shapes and their limits, such as the intersection of planar structures. Line based SfM methods are not frequent in the literature due to the difficulty of detecting them reliably, their morphological changes under changes of perspective and the challenges inherent to finding correspondences of segments in images between the different views. Additionally, compared to points, lines add the dimensionalities carried by the line directions and lengths, which prevents the epipolar constraint to be valid along a straight line segment between two different views. This chapter introduces the geometrical relations which have to be exploited for SfM sketch or abstraction based on line segments, the optimization methods for its optimization, and how to compare the experimental results with Ground-Truth measurements

Writer identification using curvature-free features

Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results

Correlation of partial frames in video matching

Correlating and fusing video frames from distributed and moving sensors is important area of video matching. It is especially difficult for frames with objects at long distances that are visible as single pixels where the algorithms cannot exploit the structure of each object. The proposed algorithm correlates partial frames with such small objects using the algebraic structural approach that exploits structural relations between objects including ratios of areas. The algorithm is fully affine invariant, which includes any rotation, shift, and scaling

Reconhecimento automático de moedas medievais usando visão por computador

Dissertação de mestrado em Engenharia InformáticaThe use of computer vision for identification and recognition of coins is well studied and of renowned interest. However the focus of research has consistently been on modern coins and the used algorithms present quite disappointing results when applied to ancient coins. This discrepancy is explained by the nature of ancient coins that are manually minted, having plenty variances, failures, ripples and centuries of degradation which further deform the characteristic patterns, making their identification a hard task even for humans. Another noteworthy factor in almost all similar studies is the controlled environments and uniform illumination of all images of the datasets. Though it makes sense to focus on the more problematic variables, this is an impossible premise to find outside the researchers’ laboratory, therefore a problematic that must be approached. This dissertation focuses on medieval and ancient coin recognition in uncontrolled “real world” images, thus trying to pave way to the use of vast repositories of coin images all over the internet that could be used to make our algorithms more robust. The first part of the dissertation proposes a fast and automatic method to segment ancient coins over complex backgrounds using a Histogram Backprojection approach combined with edge detection methods. Results are compared against an automation of GrabCut algorithm. The proposed method achieves a Good or Acceptable rate on 76% of the images, taking an average of 0.29s per image, against 49% in 19.58s for GrabCut. Although this work is oriented to ancient coin segmentation, the method can also be used in other contexts presenting thin objects with uniform colors. In the second part, several state of the art machine learning algorithms are compared in the search for the most promising approach to classify these challenging coins. The best results are achieved using dense SIFT descriptors organized into Bags of Visual Words, and using Support Vector Machine or Naïve Bayes as machine learning strategies.O uso de visão por computador para identificação e reconhecimento de moedas é bastante estudado e de reconhecido interesse. No entanto o foco da investigação tem sido sistematicamente sobre as moedas modernas e os algoritmos usados apresentam resultados bastante desapontantes quando aplicados a moedas antigas. Esta discrepância é justificada pela natureza das moedas antigas que, sendo cunhadas à mão, apresentam bastantes variações, falhas e séculos de degradação que deformam os padrões característicos, tornando a sua identificação dificil mesmo para o ser humano. Adicionalmente, a quase totalidade dos estudos usa ambientes controlados e iluminação uniformizada entre todas as imagens dos datasets. Embora faça sentido focar-se nas variáveis mais problemáticas, esta é uma premissa impossível de encontrar fora do laboratório do investigador e portanto uma problemática que tem que ser estudada. Esta dissertação foca-se no reconhecimento de moedas medievais e clássicas em imagens não controladas, tentando assim abrir caminho ao uso de vastos repositórios de imagens de moedas disponíveis na internet, que poderiam ser usados para tornar os nossos algoritmos mais robustos. Na primeira parte é proposto um método rápido e automático para segmentar moedas antigas sobre fundos complexos, numa abordagem que envolve Histogram Backprojection combinado com deteção de arestas. Os resultados são comparados com uma automação do algoritmo GrabCut. O método proposto obtém uma classificação de Bom ou Aceitável em 76% das imagens, demorando uma média de 0.29s por imagem, contra 49% em 19,58s do GrabCut. Não obstante o foco em segmentação de moedas antigas, este método pode ser usado noutros contextos que incluam objetos planos de cor uniforme. Na segunda parte, o estado da arte de Machine Learning é testado e comparado em busca da abordagem mais promissora para classificar estas moedas. Os melhores resultados são alcançados usando descritores dense SIFT, organizados em Bags of Visual Words e usando Support Vector Machine ou Naive Bayes como estratégias de machine learning

Representations for Cognitive Vision : a Review of Appearance-Based, Spatio-Temporal, and Graph-Based Approaches

The emerging discipline of cognitive vision requires a proper representation of visual information including spatial and temporal relationships, scenes, events, semantics and context. This review article summarizes existing representational schemes in computer vision which might be useful for cognitive vision, a and discusses promising future research directions. The various approaches are categorized according to appearance-based, spatio-temporal, and graph-based representations for cognitive vision. While the representation of objects has been covered extensively in computer vision research, both from a reconstruction as well as from a recognition point of view, cognitive vision will also require new ideas how to represent scenes. We introduce new concepts for scene representations and discuss how these might be efficiently implemented in future cognitive vision systems

Pedestrian Detection and Tracking in Urban Context Using a Mono-camera

Jalakäijate tuvastus ja jälgimine on üks tähtsamaid aspekte edasijõudnud sõitja abisüsteemides. Need süsteemid aitavad vältida ohtlikke olukordi, juhendades sõitjaid ja hoiatades ettetulevate riskide eest. Jalakäijate tuvastuse ja jälgimise põhiideed on tuvastada jalakäijad siis, kui nad on turvalises tsoonis ja ennustada nende asukohta ja suunda. Selle lõputöö eesmärk on uurida võimalikke meetodeid ja arendada nende põhjal hea algoritm jalakäijate tuvastuseks ja jälgimiseks.Selles lõputöös arendatud lahendus keskendub jalakäija täpsele tuvastamisele ja jälgimisele. Süsteemi täpsuse hindamiseks on saadud tulemusi võrreldud olemasolevate lahendustega.Pedestrian detection and tracking are one of the important aspects in Advanced Driver Assistance Systems. These systems help to avoid dangerous situations, by guiding drivers and warning them about the upcoming risks. The main ideas of pedestrian detection and tracking are to detect pedestrians, while they are in the secure zone, and predict their position and direction.The goal of this thesis is to examine possible methods and based on these, to develop a good pedestrian detection and tracking algorithm. The solution developed in this thesis, focuses on accurately detecting and tracking a pedestrian. In order to estimate the accuracy of the system, obtained results will be compared to the existing solutions