Revisiting Viewing Graph Solvability: an Effective Approach Based on Cycle Consistency.

In the structure from motion, the viewing graph is a graph where the vertices correspond to cameras (or images) and the edges represent the fundamental matrices. We provide a new formulation and an algorithm for determining whether a viewing graph is solvable, i.e., uniquely determines a set of projective cameras. The known theoretical conditions either do not fully characterize the solvability of all viewing graphs, or are extremely difficult to compute because they involve solving a system of polynomial equations with a large number of unknowns. The main result of this paper is a method to reduce the number of unknowns by exploiting cycle consistency. We advance the understanding of solvability by (i) finishing the classification of all minimal graphs up to 9 nodes, (ii) extending the practical verification of solvability to minimal graphs with up to 90 nodes, (iii) finally answering an open research question by showing that finite solvability is not equivalent to solvability, and (iv) formally drawing the connection with the calibrated case (i.e., parallel rigidity). Finally, we present an experiment on real data that shows that unsolvable graphs may appear in practice.

Bearing-Based Network Localizability: A Unifying View.

This paper provides a unifying view and offers new insights on bearing-based network localizability, that is the problem of establishing whether a set of directions between pairs of nodes uniquely determines (up to translation and scale) the position of the nodes in d-space. If nodes represent cameras then we are in the context of global structure from motion. The contribution of the paper is theoretical: first, we rewrite and link in a coherent structure several results that have been presented in different communities using disparate formalisms; second, we derive some new localizability results within the edge-based formulation.

Automation of peak-tracking analysis of stepwise perturbed NMR spectra.

We describe a new algorithmic approach able to automatically pick and track the NMR resonances of a large number of 2D NMR spectra acquired during a stepwise variation of a physical parameter. The method has been named Trace in Track (TINT), referring to the idea that a gaussian decomposition traces peaks within the tracks recognised through 3D mathematical morphology. It is capable of determining the evolution of the chemical shifts, intensity and linewidths of each tracked peak.The performances obtained in term of track reconstruction and correct assignment on realistic synthetic spectra were high above 90% when a noise level similar to that of experimental data were considered. TINT was applied successfully to several protein systems during a temperature ramp in isotope exchange experiments. A comparison with a state-of-the-art algorithm showed promising results for great numbers of spectra and low signal to noise ratios, when the graduality of the perturbation is appropriate. TINT can be applied to different kinds of high throughput chemical shift mapping experiments, with quasi-continuous variations, in which a quantitative automated recognition is crucial.

Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images.

The limited depth-of-field of some cameras prevents them from capturing perfectly focused images when the imaged scene covers a large distance range. In order to compensate for this problem, image fusion has been exploited for combining images captured with different camera settings, thus yielding a higher quality all-in-focus image. Since most current approaches for image fusion rely on maximizing the spatial frequency of the composed image, the fusion process is sensitive to noise. In this paper, a new algorithm for computing the all-in-focus image from a sequence of images captured with a low depth-of-field camera is presented. The proposed approach adaptively fuses the different frames of the focus sequence in order to reduce noise while preserving image features. The algorithm consists of three stages: 1) focus measure; 2) selectivity measure; 3) and image fusion. An extensive set of experimental tests has been carried out in order to compare the proposed algorithm with state-of-the-art all-in-focus methods using both synthetic and real sequences. The obtained results show the advantages of the proposed scheme even for high levels of noise.

Patch-based background initialization in heavily cluttered video.

In this paper, we propose a patch-based technique for robust background initialization that exploits both spatial and temporal consistency of the static background. The proposed technique is able to cope with heavy clutter, i.e, foreground objects that stand still for a considerable portion of time. First, the sequence is subdivided in patches that are clustered along the time-line in order to narrow down the number of background candidates. Then, a tessellation is grown incrementally by selecting at each step the best continuation of the current background. The method rests on sound principles in all its stages and only few, intelligible parameters are needed. Experimental results show that the proposed algorithm is effective and compares favorably with existing techniques.

Globally convergent autocalibration using interval analysis.

We address the problem of autocalibration of a moving camera with unknown constant intrinsic parameters. Existing autocalibration techniques use numerical optimization algorithms whose convergence to the correct result cannot be guaranteed, in general. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of Interval Analysis this method converges to the global solution with mathematical certainty and arbitrary accuracy and the only input information it requires from the user are a set of point correspondences and a search interval. The cost function is based on the Huang-Faugeras constraint of the essential matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, experimental results are presented.

Augmented scene modeling and visualization by optical and acoustic sensor integration.

In this paper, underwater scene modeling from multisensor data is addressed. Acoustic and optical devices aboard an underwater vehicle are used to sense the environment in order to produce an output that is readily understandable even by an inexperienced operator. The main idea is to integrate multiple-sensor data by geometrically registering such data to a model. The geometrical structure of this model is a priori known but not ad hoc designed for this purpose. As a result, the vehicle pose is derived and model objects can be superimposed upon actual images, thus generating an augmented-reality representation. Results on a real underwater scene are reported, showing the effectiveness of the proposed approach.