Xavier Orriols

Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

This paper deals with the problem of precise automatic estimation of the surface geometry of pot sherds uncovered at archaeological excavation sites using dense 3D laser-scan data. Critical to ceramic fragment analysis is the ability to geometrically classify excavated sherds, and, if possible, reconstruct the original pots using the sherd fragments. To do this, archaelogists must estimate the pot geometry in terms of an axis and associated profile curve from the discovered fragments. In this paper, we discuss an automatic method for accurately estimating an axis/profile curve pair for each archeological sherd (even when they are small) based on axially symmetric implicit polynomial surface models. Our method estimates the axis/profile curve for a sherd by finding the axially symmetric algebraic surface which best fits the measured set of dense 3D points and associated normals. We note that this method will work on 3D point data alone and does not require any local surface computations such as differentiation. Axis/profile curve estimates are accompanied by a detailed statistical error analysis. Estimation and error analysis are illustrated with application to a number of sherds. These fragments, excavated from Petra, Jordan, are chosen as exemplars of the families of geometrically diverse sherds commonly found on an archeological excavation site. We then briefly discuss how the estimation results may be integrated into a larger pot reconstruction program.

Bayesian Pot-Assembly from Fragments as Problems in Perceptual-Grouping and Geometric-Learning

A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be an aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.

An EM algorithm for video summarization, generative model approach

In this paper, we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence. Given a set of time correlated frames, we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution. To this end, we present a generative model which involves jointly the representation and the evolution of appearance. Applying Linear Dynamical System theory to this problem, we discuss how the temporal information is encoded yielding a manner of grouping the iconic representations of the video sequence in terms of invariance. The formulation of this problem is driven in terms of a probabilistic approach, which affords a measure of perceptual similarity taking both learned appearance and time evolution models into account.

Chromatic aberration and depth extraction

We introduce chromatic aberration as a source of visual information that can be useful for autofocus and depth estimation. A color video camera equipped with a lens with chromatic aberration has been used to take images of both step and occlusion edges at several distances from the camera. The defocus measures obtained in the three different RGB color channels of each image are different. We suggest the way this information can be exploited in order to design an autofocus sensor, and also how depth information can be derived.

Classifying periodic motions in video sequences

In this paper, we present a new technique for separating different types of periodic motions in a video sequence. We consider different motions those that have different periodic patterns with one or many fundamental frequencies. We select the temporal Fourier transform for each pixel to be the representation space for a sequence of images. The classification is performed using nonnegative matrix factorization (NNMF) over the power spectra data set. The paper we present can be applied on a wide range of applications for video sequences analysis, such as: background subtraction on nonstatic backgrounds framework, object segmentation and classification. We point out the fact that no registration technique is applied in the method that we introduce. Nevertheless, this method can be used as a cooperative tool for the existing techniques based on camera motion models (motion segmentation, layer classification, tracking of moving objects, etc).

Polynomial fiber description of motion for video mosaicing

This paper is about two topics. First, given a video sequence, we introduce a Bayesian framework where background and moving objects are segmented into layers. The model that describes the different layer evolutions in a sequence of images uses the results of a multi-frame optical flow estimation (MFOFE); we present a new technique based on the fact that each pixel in the frame of reference produces a trajectory in the mosaic absolute coordinate system. The second topic is video summarization. A general moisaicing method is presented for describing the background and the trajectories of moving objects in a sequence of frames. Combining layer segmentation and mosaicing, we show different manners of encoding and visualizing temporal information, where the key point is the selection of a certain object in the images as reference in the evolution.

Probabilistic saliency approach for elongated structure detection using deformable models

We address the object recognition problem in a probabilistic framework to detect and describe object appearance through image features organized by means of active contour models. We consider the formulation of saliency in terms of visual similarity embedded in the probabilistic principal component analysis framework. A likelihood of object structure detection is obtained using the relation between the visual field and the internal object representation. Deformable models are employed introducing a computational methodology for a perceptual organisation of image features as an abstract understanding of the integration between structure and constraints of the visual information-processing problem. A specific application of the integrated approach for vessels segmentation in angiography is considered and the results are encouraging.