from multiview image curves to 3d drawings
Digital Archaeology: Applications of Calculator Vision to Archaeology
We present a consummate organisation for the purpose of automatically assembling 3D pots given 3D measurements of their fragments commonly called sherds. A Bayesian approach formulated which, at present, models the data given a set sherd geometric parameters. Dense sherd measurement is obtained by scanning the outside surface of each with a light amplification by stimulated emission of radiation scanner. Mathematical models, specified past a set of geometric parameters, represent the sherd surface and interruption curves on the outer surface (where sherds take broken apart). Optimal alignment of assemblies sherds, called configurations, is implemented equally maximum likelihood estimation (MLE) of the surface and bend parameters given the measured sherd information for sherds in a configuration.
Fragment Assembly
REVEAL (Reconstruction and Exploratory Visualization: Engineering meets ArchaeoLogy) An NSF Project consisting of:
Reveal
Object Recognition and Detection
In this project, we are developing an object recognition and segmentation framework that uses a shock graph based shape model. Our fragment-based generative model is capable of generating a wide variation of shapes as instances of a given object category. In lodge to recognize and segment objects, nosotros make use of a progressive selection mechanism to search amongst the generated shapes for the category instances that are present in the paradigm. The search begins with a large pool of candidates identified by the dynamic programming (DP) algorithm and progressively reduces it in size past applying serial of criteria.
Object Recognition and Partitioning Using a Daze Graph Based Shape Model
A semantic clarification of 3-d scenes is essential to many urban and surveillance applications. The general problems of object localization and course recognition in Computer Vision are traditionally performed in 2D images. In dissimilarity, this project aims to reason about the state of the 3-d globe. More specifically, this project uses probabilistic volumetric models of a scene geometry and appearance to perform object categorization tasks directly in 3-d. The methods and results presented here were fisrt accustomed as a full paper (xxx min. oral presentation) at the International Conference of Pattern Recognition Application and Methods, ICPRAM 20112. An more recent and comprenhensive evaluation has been accepted for publication at the IEEE Journal of Selected Topics in Bespeak Processing
Object Recognition in Probabilistic 3D Scenes
Visualization and Human Interfaces
This project explores a multi‐stage program of research, implementation, and evaluation of collaborative, interactive, large‐screen, gesture‐driven displays used to enhance a wide range of scholarly activities and artistic expressions. Although this project includes research topics such as: seamless imaging, touch‐enabled computing, parallel rendering, pattern methodologies and intelligent networking; our master focus is camera-based interaction, i.eastward., study how to runway people's locations, their features, hand-held objects, and hand gestures; using this information to trigger actions and to accordingly render imagery and sound, making possible an heady multi-user experience with the computer arrangement.
Advancing Digital Scholarship with Bear upon‐Surfaces and Big‐Format Interactive Brandish Walls
As an initial accomplishment, nosotros have constructed the kickoff version of our scalable, loftier-resolution display wall...
Multiview Geometry Reconstruction and Scale
Pollard and Mundy (2007) proposed a probabilistic book model that can represent the ambiguity and uncertainty in iii-d models derived from multiple image views. In Pollard's model, a region of three-dimensional infinite is decomposed into a regular 3-d filigree of cells, chosen voxels. A voxel stores two kinds of land data: (i) the probability that the voxel contains a surface chemical element and (ii) a mixture of Gaussians that models the surface appearance of the voxel as learned from a sequence of images. The surface probability is updated by incremental Bayesian learning , where the probability of a voxel containing a surface element afterwards N+1 images increases if the Gaussian mixture at that voxel explains the intensity observed in the N+ane image better than any other voxelalong the project ray. In a fixed-grid voxel representation, about of the voxels may correspond to empty areas of a scene, making storage of...
Probabilistic Volumetric Modeling
This project presents a novel framework for surface reconstruction from multi-view aerial imagery of large scale urban scenes, which combines probabilistic volumetric modeling with smooth signed distance surface estimation, to produce very detailed and accurate surfaces. Using a continuous probabilistic volumetric model which allows for explicit representation of ambiguities caused by moving objects, cogitating surfaces, areas of abiding advent, and self-occlusions, the algorithm learns the geometry and appearance of a scene from a calibrated image sequence. An online implementation of Bayesian learning precess in GPUs significantly reduces the fourth dimension required to procedure a large number of images. The probabilistic volumetric model of occupancy is afterwards used to estimate a smooth approximation of the signed distance role to the surface. This pace, which reduces to the solution of a thin linear system, is very efficient and scalable to large information sets. The proposed...
High Resolution Surface Reconstruction from Aerial Images
The goal of this project is to generate clean, authentic and reliable 3D drawings from multiview epitome data. Our output representation is a 3D graph representing the geometry and the arrangement of the scene, and can be regarded as a 3D version of architectural drawings and blueprints. The input can be a sequence of either video frames or discrete images. If the calibration is non already available, we beginning by calibrating the...
From Multi-view Epitome Curves to 3D Drawings
Multiview Geometry Reconstruction and Calibration
3D Surface Representation, Design, and Scanning
This work studies the quality of probabilistic model registration using feature-matching techniques based on the the FPFH and the SHOT descriptors. Furthermore, the quality of the underlying geometry, and therefore the effectiveness of the descriptors for matching purposes, is affected by variations in the weather of the information collection. A major contribution of this piece of work is to evaluate the quality of characteristic-based registration of PVM models under dissimilar scenarios that reflect the kind of variability observed across collections from different times instances. More than precisely, this work investigates variability in terms of model discretization, resolution and sampling density, errors in the camera orientation, and changes illumination and geographic characteristics. A corresponding manuscript is nether training.
Registration of PVM
Source: https://vision.lems.brown.edu/research_projects
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