Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor

Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level. Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a scene’s geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques. Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate. Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the scene’s geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation

Automated Vector-to-Raster Image Registration

The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and compared against existing vector layer(s) to be registered. Given that available automated feature extraction (AFE) methods quite often produce false features and miss some features, we use additional information to improve AFE. This information is the existing vector data, but the vector data are not perfect as well. To deal with this problem the VRR process uses an algebraic structural algorithm (ASA), similarity transformation of local features algorithm (STLF), and a multi-loop process that repeats (AFE-VRR) process several times. The experiments show that it was successful in registering road vectors to commercial panchromatic and multi-spectral imagery

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

UAV-Multispectral Sensed Data Band Co-Registration Framework

Precision farming has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farms to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among the bands, which makes the alignment process complex. In this work, we propose a new framework for the band co-registration process based on two premises: i) the natural misalignment is an attribute of the camera, so it does not change during the acquisition process; ii) the speed of displacement of the UAV when compared to the speed between the acquisition of the first to the last band, is not sufficient to create significant distortions. We compared our results with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of 0.425 pixels, this result being 335% better than the evaluated frameworks.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)A agricultura de precisão se beneficiou muito das novas tecnologias ao longo dos anos. O uso de sensores multiespectrais e hiperespectrais acoplados aos Veículos Aéreos Não Tripulados (VANT) permitiu que as fazendas monitorassem as lavouras, melhorassem o uso de recursos e reduzissem os custos. Apesar de amplamente utilizadas, as imagens multiespectrais apresentam um desalinhamento natural entre os vários espectros devido ao uso de diferentes sensores. A variação do espectro analisado também leva à perda de características entre as bandas, o que dificulta o processo de detecção de atributos entre as bandas, o que torna complexo o processo de alinhamento. Neste trabalho, propomos um novo framework para o processo de alinhamento entre as bandas com base em duas premissas: i) o desalinhamento natural é um atributo da câmera, e por esse motivo ele não é alterado durante o processo de aquisição; ii) a velocidade de deslocamento do VANT, quando comparada à velocidade entre a aquisição da primeira e a última banda, não é suficiente para criar distorções significativas. Os resultados obtidos foram comparados com o padrão ouro gerado por um especialista e com outros métodos presentes na literatura. O framework proposto teve um back-projection error (BP) de 0, 425 pixels, sendo este resultado 335% melhor aos frameworks avaliados