Javier Flavio Vigueras-Gomez

Bio: Javier Flavio Vigueras-Gomez is an academic researcher from Universidad Autónoma de San Luis Potosí. The author has contributed to research in topics: Image registration & Fourier transform. The author has an hindex of 2, co-authored 4 publications receiving 42 citations.

Phase correlation based image alignment with subpixel accuracy

Abstract: The phase correlation method is a well-known image alignment technique with broad applications in medical image processing, image stitching, and computer vision. This method relies on estimating the maximum of the phase-only correlation (POC) function, which is defined as the inverse Fourier transform of the normalized cross-spectrum between two images. The coordinates of the maximum correspond to the translation between the two images. One of the main drawbacks of this method, in its basic form, is that the location of the maximum can only be obtained with integer accuracy. In this paper, we propose a new technique to estimate the location with subpixel accuracy, by minimizing the magnitude of gradient of the POC function around a point near the maximum. We also present some experimental results where the proposed method shows an increased accuracy of at least one order of magnitude with respect to the base method. Finally, we illustrate the application of the proposed algorithm to the rigid registration of digital images.

An exploration of multimodal similarity metrics for parametric image registration based on particle filtering

Abstract: This paper presents an analysis of different multimodal similarity metrics for parametric image registration based on particle filtering. Our analysis includes four similarity metrics found in the literature and we propose a new metric based on the discretization of the kernel predictability, function recently introduced by Gomez-Garcia et al. (2008), that we call histogram kernel predictability (HKP). Hence the metrics studied in this work are mutual information, normalized mutual information, kernel predictibility with gaussian and truncated parabola functions, and HKP. The evaluations include tests varying the number of particles in the filter, the type of pixel sampling, the number of bins used to calculate the histograms, the noise in the images, and the computation time. Furthermore, we also conducted a geometric analysis to inspect convexity properties of the metrics under discussion. The overall evaluation suggests that the normalized mutual information is the best similarity metric for parametric image registration.

Real-time structure and motion recovery from two views of a multiplanar scene

Abstract: This demo addresses the problem of recovering the structure of a 3D scene from two views of a scene composed by textured planar surfaces. In this work, we propose a framework that exploits geometrical and algebraic constraints induced by rigidity and planarity in the scene. We present the 3D reconstruction of multi-planar environments as a multi-linear problem expressed in terms of the planar homographies and the projection of intersections between the observed planes. But, instead of solving a complex multi-linear system, our algorithm iteratively solves several linear problems: (i) coplanar feature segmentation, (ii) planar projective transfer estimation, (iii) the epipole computation, and (iv) finding intersections between planes. Linear methods allow our approach to be fast, hence suitable for online 3D reconstruction and real-time localization of a camera moving within a scene.

Fast vision-based scene modeling for augmented reality in unprepared man-made environments

Abstract: In this paper, we propose a general purpose Augmented Reality AR system that allows to add easily 3D computer generated CG objects into real man-made environments. Our system goes to a very intuitive and easy in situ 3D structure recovery of planar piecewise scenes without using powerful hardware nor commodity sensors. The user simply has to move the camera translation of the focus is mandatory and take two different pictures of the scene and our approach obtains a rough planar piecewise representation of the environment suitable to conduct multi-planar tracking for visual model-based augmented reality and to augment it with virtual objects coherently. Polyhedral representations of scenes are very convenient for manmade environments indoor e.g., offices, rooms, classrooms and outdoor e.g., looking at facades, floor, hence we focus the potential applications of our system to augment simple rooms or urban scenes with virtual imagery.

Image Alignment for Panorama Stitching in Sparsely Structured Environments

Abstract: Panorama stitching of sparsely structured scenes is an open research problem. In this setting, feature-based image alignment methods often fail due to shortage of distinct image features. Instead, direct image alignment methods, such as those based on phase correlation, can be applied. In this paper we investigate correlation-based image alignment techniques for panorama stitching of sparsely structured scenes. We propose a novel image alignment approach based on discriminative correlation filters (DCF), which has recently been successfully applied to visual tracking. Two versions of the proposed DCF-based approach are evaluated on two real and one synthetic panorama dataset of sparsely structured indoor environments. All three datasets consist of images taken on a tripod rotating 360 degrees around the vertical axis through the optical center. We show that the proposed DCF-based methods outperform phase correlation-based approaches on these datasets.

Optimization of incremental structure from motion combining a random k-d forest and pHash for unordered images in a complex scene

Abstract: On the basis of today’s popular virtual reality and scientific visualization, three-dimensional (3-D) reconstruction is widely used in disaster relief, virtual shopping, reconstruction of cultural relics, etc. In the traditional incremental structure from motion (incremental SFM) method, the time cost of the matching is one of the main factors restricting the popularization of this method. To make the whole matching process more efficient, we propose a preprocessing method before the matching process: (1) we first construct a random k-d forest with the large-scale scale-invariant feature transform features in the images and combine this with the pHash method to obtain a value of relatedness, (2) we then construct a connected weighted graph based on the relatedness value, and (3) we finally obtain a planned sequence of adding images according to the principle of the minimum spanning tree. On this basis, we attempt to thin the minimum spanning tree to reduce the number of matchings and ensure that the images are well distributed. The experimental results show a great reduction in the number of matchings with enough object points, with only a small influence on the inner stability, which proves that this method can quickly and reliably improve the efficiency of the SFM method with unordered multiview images in complex scenes.