1,520 research outputs found
Camera calibration from surfaces of revolution
This paper addresses the problem of calibrating a pinhole camera from images of a surface of revolution. Camera calibration is the process of determining the intrinsic or internal parameters (i.e., aspect ratio, focal length, and principal point) of a camera, and it is important for both motion estimation and metric reconstruction of 3D models. In this paper, a novel and simple calibration technique is introduced, which is based on exploiting the symmetry of images of surfaces of revolution. Traditional techniques for camera calibration involve taking images of some precisely machined calibration pattern (such as a calibration grid). The use of surfaces of revolution, which are commonly found in daily life (e.g., bowls and vases), makes the process easier as a result of the reduced cost and increased accessibility of the calibration objects. In this paper, it is shown that two images of a surface of revolution will provide enough information for determining the aspect ratio, focal length, and principal point of a camera with fixed intrinsic parameters. The algorithms presented in this paper have been implemented and tested with both synthetic and real data. Experimental results show that the camera calibration method presented here is both practical and accurate.published_or_final_versio
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Learning Silhouette Features for Control of Human Motion
We present a vision-based performance interface for controlling animated human characters. The system interactively combines information about the user's motion contained in silhouettes from three viewpoints with domain knowledge contained in a motion capture database to produce an animation of high quality. Such an interactive system might be useful for authoring, for teleconferencing, or as a control interface for a character in a game. In our implementation, the user performs in front of three video cameras; the resulting silhouettes are used to estimate his orientation and body configuration based on a set of discriminative local features. Those features are selected by a machine-learning algorithm during a preprocessing step. Sequences of motions that approximate the user's actions are extracted from the motion database and scaled in time to match the speed of the user's motion. We use swing dancing, a complex human motion, to demonstrate the effectiveness of our approach. We compare our results to those obtained with a set of global features, Hu moments, and ground truth measurements from a motion capture system.Engineering and Applied Science
3D object reconstruction using computer vision : reconstruction and characterization applications for external human anatomical structures
Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
Shape from inconsistent silhouette: Reconstruction of objects in the presence of segmentation and camera calibration error
Silhouettes are useful features to reconstruct the object shape when the object is textureless or the shape classes of objects are unknown. In this dissertation, we explore the problem of reconstructing the shape of challenging objects from silhouettes under real-world conditions such as the presence of silhouette and camera calibration error. This problem is called the Shape from Inconsistent Silhouettes problem. A psuedo-Boolean cost function is formalized for this problem, which penalizes differences between the reconstruction images and the silhouette images, and the Shape from Inconsistent Silhouette problem is cast as a psuedo-Boolean minimization problem. We propose a memory and time efficient method to find a local minimum solution to the optimization problem, including heuristics that take into account the geometric nature of the problem. Our methods are demonstrated on a variety of challenging objects including humans and large, thin objects. We also compare our methods to the state-of-the-art by generating reconstructions of synthetic objects with induced error. ^ We also propose a method for correcting camera calibration error given silhouettes with segmentation error. Unlike other existing methods, our method allows camera calibration error to be corrected without camera placement constraints and allows for silhouette segmentation error. This is accomplished by a modified Iterative Closest Point algorithm which minimizes the difference between an initial reconstruction and the input silhouettes. We characterize the degree of error that can be corrected with synthetic datasets with increasing error, and demonstrate the ability of the camera calibration correction method in improving the reconstruction quality in several challenging real-world datasets
Surface Reconstruction and Evolution from Multiple Views
Applications like 3D Telepresence necessitate faithful 3D surface reconstruction
of the object and 3D data compression in both spatial and
temporal domains. This makes us feel immersed in virtual environments
there by making 3D Telepresence a powerful tool in many applications.
Hence 3D surface reconstruction and 3D compression are two challenging
problems which are addressed in this thesis
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