Uncalibrated stereo vision applied to breast cancer treatment aesthetic assessment

Mestrado Integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201

Non-parametric Models of Distortion in Imaging Systems.

Traditional radial lens distortion models are based on the physical construction of lenses. However, manufacturing defects and physical shock often cause the actual observed distortion to be different from what can be modeled by the physically motivated models. In this work, we initially propose a Gaussian process radial distortion model as an alternative to the physically motivated models. The non-parametric nature of this model helps implicitly select the right model complexity, whereas for traditional distortion models one must perform explicit model selection to decide the right parametric complexity. Next, we forego the radial distortion assumption and present a completely non-parametric, mathematically motivated distortion model based on locally-weighted homographies. The separation from an underlying physical model allows this model to capture arbitrary sources of distortion. We then apply this fully non-parametric distortion model to a zoom lens, where the distortion complexity can vary across zoom levels and the lens exhibits noticeable non-radial distortion. Through our experiments and evaluation, we show that the proposed models are as accurate as the traditional parametric models at characterizing radial distortion while flexibly capturing non-radial distortion if present in the imaging system.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120690/1/rpradeep_1.pd

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Toward Automated Aerial Refueling: Relative Navigation with Structure from Motion

The USAF\u27s use of UAS has expanded from reconnaissance to hunter/killer missions. As the UAS mission further expands into aerial combat, better performance and larger payloads will have a negative correlation with range and loiter times. Additionally, the Air Force Future Operating Concept calls for \formations of uninhabited refueling aircraft...[that] enable refueling operations partway inside threat areas. However, a lack of accurate relative positioning information prevents the ability to safely maintain close formation flight and contact between a tanker and a UAS. The inclusion of cutting edge vision systems on present refueling platforms may provide the information necessary to support a AAR mission by estimating the position of a trailing aircraft to provide inputs to a UAS controller capable of maintaining a given position. This research examines the ability of SfM to generate relative navigation information. Previous AAR research efforts involved the use of differential GPS, LiDAR, and vision systems. This research aims to leverage current and future imaging technology to compliment these solutions. The algorithm used in this thesis generates a point cloud by determining 3D structure from a sequence of 2D images. The algorithm then utilizes PCA to register the point cloud to a reference model. The algorithm was tested in a real world environment using a 1:7 scale F-15 model. Additionally, this thesis studies common 3D rigid registration algorithms in an effort characterize their performance in the AAR domain. Three algorithms are tested for runtime and registration accuracy with four data sets