Thesis advisor: Noah Snyder / This study considers the feasibility and accuracy of using the Structure from Motion (SfM) technique to quantify changes in stream channel morphology. The SfM method utilizes common points across multiple photographs to create a three-dimensional representation of a study area. This model can then be georeferenced using ground control points. The camera locations and optics do not need to be known in this technique, making it simpler to implement in the field than traditional photogrammetry or ground-based lidar methods. Preliminary testing of this method was conducted in and around the Boston College campus during summer 2012 to determine the most appropriate tools and data collection plan for further fieldwork. I then applied the SfM method to a field site on the Souhegan River in southern New Hampshire, where I photographed two cross sections (one boulder-bedded, one sand-bedded) using a camera mounted on a 4.8 m pole. On the same day, I surveyed both cross sections using a total station with mm-scale accuracy. Inputting the photographs into the Agisoft PhotoScan software used for SfM reconstruction yielded several noteworthy results. First, when certain conditions are met, the model generated through SfM, built from a complex, high density (for example ~2,900 points per m2) point cloud, can then be used to deduce elevation data. Based on a point-by-point comparison, the SfM cross section averaged 3.6 cm (±3.4 cm standard deviation) higher than the total station survey. In other portions of the study site imaged for SfM reconstruction, a variety of difficulties prevented the development of a georeferenced three-dimensional model. These limitations, including shadowing, vegetation, camera vantage point, and location of ground control points, can be minimized in future studies to allow for better use of the SfM technique. As results of this study demonstrate, SfM reconstruction has the potential to generate accurate topographic data, which will be a powerful tool for future geomorphic studies, particularly for sites with relatively sparse vegetation and limited water. / Thesis (BS) — Boston College, 2013. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Geology & Geophysics Honors Program. / Discipline: Department of Earth and Environmental Sciences.

This work introduces a direct method called FIXATION for solving the general motion vision problem. This Fixation method results in a constraint equation between translational and rotational velocities that in combination with the Brightness-Change Constraint Equation (BCCE) solves the general motion vision problem, arbitrary motion with respect to an arbitrary rigid environment. Neither Correspondence nor Optical Flow has been used here. Recently Direct Motion Vision methods have used the BCCE for solving the motion vision problem of special motions or environments. In contrast to those solutions, the Fixation method does not put such severe restrictions on the motion or the environment.

We study the problem of recovering the structure from motion of figures which are allowed to perform a controlled non-rigid motion. We use Regge Calculus to approximate a general surface by a net of triangles. The non- rigid flexing motion we deal with corresponds to keeping the triangles rigid and allowing bending only at the joins between triangles. We show that depth information can be obtained by using a modified version of the Incremental Rigidity Scheme devised by Ullman (1984). We modify this scheme to allow for flexing motion and call our version the Incremental Semirigidity Scheme.

Three-dimensional (3D) object perception is critical for comprehending and interacting with the world. It develops during infancy and continues through adulthood. One powerful cue used for object perception is uniform coherent motion. The present paper first briefly reviews the current literature concerning object perception using random-dot stimuli and structure-from-motion (SFM) displays. To extend our knowledge in this area, two new studies were conducted to further our understanding of how infants process 3D shape in SFM stimuli. Study 1 examined infants of two age groups (3-5 month-olds and 8-9 month-olds) in a familiarization phase and a test phase. In the familiarization phase, infants were exposed to one of two SFM shapes (cube or cylinder) and in the test phase infants viewed both SFM shapes side-by-side. Extraction of shape was measured through novelty preferences. Results of Study 1 suggest that both age groups successfully extracted 3D shape. Study 2 served as a replication and extension, with the added control for the variable rotational axis. When this variable was controlled for, 3-5 month-olds failed to show a novelty preference during the test phase. These results suggest not only that infants were attending to both the global shape presented in the SFM stimuli as well as the detailed component of the rotational axis of the stimuli, but also that adding the extra change in the component of rotational axis to SFM stimuli makes the task of extracting shape more difficult for infants. These findings contribute to the infant literature by furthering the understanding of infant shape perception.

The non-rigid structure from motion problem typically involves recovering the 3D trajectories of a set of scene points, from their corresponding image trajectories. In this thesis, the assumption of locally-rigid motion is used to regularize this otherwise underconstrained problem. The key idea is that even when a scene undergoes complex global deformations, the trajectories of local triplets of scene points can often be approximated by the vertices of a rigidly moving triangle. This intuition informs our bottom-up reconstruction procedure, which discovers such triplets through a hypothesis and test framework. To this end, a rigid triangle model is fit to the proposed image trajectories and evaluated using a procedure that we call 3-SFM. The recovered triangle models are then integrated into a global solution, by resolving their orthographic depth ip and translation ambiguities. Lastly, we consider using this solution to initialize an energy based model, subject to a set of soft isometric constraints, in order to allow each observation to constrain the global scene structure. Results on several sequences, both our own and from related work, suggest that these models are applicable in diverse and challenging scenes, such as those including multiple deforming bodies.

In a roofing project, acquiring the underlying as-built 3D geometry and visualizing the roof structure is needed in different phases of the project life-cycle. Architectural drawings, building information model (BIM) files, or aerial photogrammetry are used to estimate the roofing area in the bidding process. However, as a roof structure is never built to the exact drawing dimensions, as-built dimensions of boundaries of every roof plane have to be obtained several times during the course of its build. There are a number of surveying methods that can be used for this purpose: tape measuring, total station surveying, aerial photogrammetry, and laser scanning. However, obtaining measurements using these methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards. Aiming to address this limitation and provide roofing practitioners with an alternative roof surveying and visualization method that is simple to use, automated, inexpensive, and safe, a close-range videogrammetric roof 3D reconstruction framework is presented in this research. When using this method, a roofing contractor will simply collect stereo video streams of a target roof. The captured data is processed to generate a 3D wire-diagram for every roof plane. In this process, distinctive visual features of the scene (e.g., 2D points and lines) are first automatically detected and matched between video frames. Matched features and the camera calibration information are used to compute an initial estimation of the 3D structure. Then, a hybrid bundle adjustment algorithm is used to refine the result and acquire the geometry that has the maximum likelihood. Afterwards, different roof planes are found and a measurable 3D wire-diagram is generated for each plane.

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Made available in DSpace on 2014-12-17T14:55:51Z (GMT). No. of bitstreams: 1 BrunoMFS_DISSERT.pdf: 2462891 bytes, checksum: b8ea846d0fcc23b0777a6002e9ba92ac (MD5) Previous issue date: 2011-02-15 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Visual Odometry is the process that estimates camera position and orientation based solely on images and in features (projections of visual landmarks present in the scene) extraced from them. With the increasing advance of Computer Vision algorithms and computer processing power, the subarea known as Structure from Motion (SFM) started to supply mathematical tools composing localization systems for robotics and Augmented Reality applications, in contrast with its initial purpose of being used in inherently offline solutions aiming 3D reconstruction and image based modelling. In that way, this work proposes a pipeline to obtain relative position featuring a previously calibrated camera as positional sensor and based entirely on models and algorithms from SFM. Techniques usually applied in camera localization systems such as Kalman filters and particle filters are not used, making unnecessary additional information like probabilistic models for camera state transition. Experiments assessing both 3D reconstruction quality and camera position estimated by the system were performed, in which image sequences captured in reallistic scenarios were processed and compared to localization data gathered from a mobile robotic platform / Odometria Visual ? o processo pelo qual consegue-se obter a posi??o e orienta??o de uma c?mera, baseado somente em imagens e consequentemente, em caracter?sticas (proje??es de marcos visuais da cena) nelas contidas. Com o avan?o nos algoritmos e no poder de processamento dos computadores, a sub?rea de Vis?o Computacional denominada de Structure from Motion (SFM) passou a fornecer ferramentas que comp?em sistemas de localiza??o visando aplica??es como rob?tica e Realidade Aumentada, em contraste com o seu prop?sito inicial de ser usada em aplica??es predominantemente offline como reconstru??o 3D e modelagem baseada em imagens. Sendo assim, este trabalho prop?e um pipeline de obten??o de posi??o relativa que tem como caracter?sticas fazer uso de uma ?nica c?mera calibrada como sensor posicional e ser baseado interamente nos modelos e algoritmos de SFM. T?cnicas usualmente presentes em sistemas de localiza??o de c?mera como filtros de Kalman e filtros de part?culas n?o s?o empregadas, dispensando que informa??es adicionais como um modelo probabil?stico de transi??o de estados para a c?mera sejam necess?rias. Experimentos foram realizados com o prop?sito de avaliar tanto a reconstru??o 3D quanto a posi??o de c?mera retornada pelo sistema, atrav?s de sequ?ncias de imagens capturadas em ambientes reais de opera??o e compara??es com um ground truth fornecido pelos dados do od?metro de uma plataforma rob?tica

The recent emergence of Structure-from-Motion Photogrammetry (SfM) has created a cost-effective alternative to conventional laser scanning for the production of high-resolution topographic datasets. There has been an explosion of applications of SfM within the geomorphological community in recent years, however, the focus of these has largely been small-scale (102 - 103 m2), building on innovations in low altitude Unmanned Aircraft Systems (UAS). This thesis examines the potential to extend the scope of SfM photogrammetry in order to quantify of landscape scale processes. This is examined through repeat surveys of a ~35 km2 reach of the Dart River, New Zealand. An initial SfM survey of this reach was conducted in April 2014, following a large landslide at the Slipstream debris fan. Validation of the resulting digital elevation models using Independent Control Point's (ICPs) suggested encouraging results, however benchmarking the survey against a long-range laser scanned surface indicated the presence of significant systematic errors associated with inaccurate estimation of the SfM bundle adjustment. Using a combination of scaled laboratory field experiments, this research aimed to develop and test photogrammetric data collection and modelling strategies to enhance modelling of 3D scene structure using limited constraints. A repeat survey in 2015 provided an opportunity to evaluate a new survey strategy, incorporating a convergent camera network and a priori measurement of camera pose. This resulted in halving of mean checkpoint residuals and a reduction in systematic error. The models produced for both 2014 and 2015 were compared using a DEM differencing (DoD) methodology to assess the applicability of wide-area SfM models for the analysis of geomorphic change detection. The systematic errors within the 2014 model confound reliable change detection, although strategies to correlate the two surveys and measure the residual change show promise. The future use of SfM over broad landscape scales has significant potential, however, this will require robust data collection and modelling strategies and improved error modelling to increase user confidence.

Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.