240 research outputs found
Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices
In this paper, an automatic method is proposed to perform image registration
in visible and infrared pair of video sequences for multiple targets. In
multimodal image analysis like image fusion systems, color and IR sensors are
placed close to each other and capture a same scene simultaneously, but the
videos are not properly aligned by default because of different fields of view,
image capturing information, working principle and other camera specifications.
Because the scenes are usually not planar, alignment needs to be performed
continuously by extracting relevant common information. In this paper, we
approximate the shape of the targets by polygons and use affine transformation
for aligning the two video sequences. After background subtraction, keypoints
on the contour of the foreground blobs are detected using DCE (Discrete Curve
Evolution)technique. These keypoints are then described by the local shape at
each point of the obtained polygon. The keypoints are matched based on the
convexity of polygon's vertices and Euclidean distance between them. Only good
matches for each local shape polygon in a frame, are kept. To achieve a global
affine transformation that maximises the overlapping of infrared and visible
foreground pixels, the matched keypoints of each local shape polygon are stored
temporally in a buffer for a few number of frames. The matrix is evaluated at
each frame using the temporal buffer and the best matrix is selected, based on
an overlapping ratio criterion. Our experimental results demonstrate that this
method can provide highly accurate registered images and that we outperform a
previous related method
Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery
In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data
Reconstruction of 3D Surface Maps from Anterior Segment Optical Coherence Tomography Images Using Graph Theory and Genetic Algorithms
Automatic segmentation of anterior segment optical coherence tomography images provides an important tool to aid management of ocular diseases. Previous studies have mainly focused on 2D segmentation of these images. A novel technique capable of producing 3D maps of the anterior segment is presented here. This method uses graph theory and dynamic programming with shape constraint to segment the anterior and posterior surfaces in individual 2D images. Genetic algorithms are then used to align 2D images to produce a full 3D representation of the anterior segment. In order to validate the results of the 2D segmentation comparison is made to manual segmentation over a set of 39 images. For the 3D reconstruction a data set of 17 eyes is used. These have each been imaged twice so a repeatability measurement can be made. Good agreement was found with manual segmentation for the 2D segmentation method achieving a Dice similarity coefficient of 0.96, which is comparable to the inter-observer agreement. Good repeatability of results was demonstrated with the 3D registration method. A mean difference of 1.77 pixels was found between the anterior surfaces found from repeated scans of the same eye
Melhoria do alinhamento de imagens RGB-D usando marcadores fiduciais
3D reconstruction is the creation of three-dimensional models from the captured
shape and appearance of real objects. It is a field that has its roots in
several areas within computer vision and graphics, and has gained high importance
in others, such as architecture, robotics, autonomous driving, medicine,
and archaeology. Most of the current model acquisition technologies are
based on LiDAR, RGB-D cameras, and image-based approaches such as visual
SLAM. Despite the improvements that have been achieved, methods that
rely on professional instruments and operation result in high costs, both capital
and logistical. In this dissertation, we develop an optimization procedure
capable of enhancing the 3D reconstructions created using a consumer level
RGB-D hand-held camera, a product that is widely available, easily handled,
with a familiar interface to the average smartphone user, through the utilisation
of fiducial markers placed in the environment. Additionally, a tool was
developed to allow the removal of said fiducial markers from the texture of the
scene, as a complement to mitigate a downside of the approach taken, but
that may prove useful in other contexts.A reconstrução 3D é a criação de modelos tridimensionais a partir da forma
e aparência capturadas de objetos reais. É um campo que teve origem em
diversos ramos da visão computacional e computação gráfica, e que ganhou
grande importância em áreas como a arquitetura, robótica, condução autónoma,
medicina e arqueologia. A maioria das tecnologias de aquisição de
modelos atuais são baseadas em LiDAR, câmeras RGB-D e abordagens baseadas
em imagens, como o SLAM visual. Apesar das melhorias que foram
alcançadas, os métodos que dependem de instrumentos profissionais e da
sua operação resultam em elevados custos, tanto de capital, como logísticos.
Nesta dissertação foi desenvolvido um processo de otimização capaz
de melhorar as reconstruções 3D criadas usando uma câmera RGB-D portátil,
disponível ao nível do consumidor, de fácil manipulação e que tem uma
interface familiar para o utilizador de smartphones, através da utilização de
marcadores fiduciais colocados no ambiente. Além disso, uma ferramenta
foi desenvolvida para permitir a remoção dos ditos marcadores fiduciais da
textura da cena, como um complemento para mitigar uma desvantagem da
abordagem adotada, mas que pode ser útil em outros contextos.Mestrado em Engenharia de Computadores e Telemátic
Automatic behavior recognition in laboratory animals using kinect
Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201
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