Mireille Boutin

On reconstructing n-point configurations from the distribution of distances or areas

One way to characterize configurations of points up to congruence is by considering the distribution of all mutual distances between points. This paper deals with the question if point configurations are uniquely determined by this distribution. After giving some counterexamples, we prove that this is the case for the vast majority of configurations. In the second part of the paper, the distribution of areas of sub-triangles is used for characterizing point configurations. Again it turns out that most configurations are reconstructible from the distribution of areas, though there are counterexamples.

Robust Bundle Adjustment for Structure from Motion

Structure from motion (SFM) is the problem of reconstructing the geometry of a scene from a stream of images. In this problem, the geometry of the scene must be inferred from images, along with the camera pose parameters. Bundle adjustment (BA) is a refinement method used to improve SFM solutions. It consists in simultaneously improving a set of initial estimates for all parameters (structure and camera pose) by minimizing a global cost function. It is generally considered to be highly accurate, and so is typically used as a last refinement step in most current SFM methods. Unfortunately, estimating the pose of the camera from a stream of images is an ill-conditioned problem. We thus propose a BA adjustment formulation which does not involve solving for the camera orientations. We tested this approach on several real world models. The numerical results obtained show that this approach is much less affected by noise than traditional BA.

Quantized Distributed Reception for MIMO Wireless Systems Using Spatial Multiplexing

We study a quantized distributed reception scenario in which a transmitter equipped with multiple antennas sends multiple streams via spatial multiplexing to a large number of geographically separated single antenna receive nodes. This approach is applicable to scenarios such as those enabled by the Internet of Things (IoT) which holds much commercial potential and could facilitate distributed multiple-input multiple-output (MIMO) communication in future systems. The receive nodes quantize their received signals and forward the quantized received signals to a receive fusion center. With global channel knowledge and forwarded quantized information from the receive nodes, the fusion center attempts to decode the transmitted symbols. We assume the transmit vector consists of arbitrary constellation points, and each receive node quantizes its received signal with one bit for each of the real and imaginary parts of the signal to minimize the transmission overhead between the receive nodes and the fusion center. Fusing this data is a nontrivial problem because the receive nodes cannot decode the transmitted symbols before quantization. We develop an optimal maximum likelihood (ML) receiver and a low-complexity zero-forcing (ZF)-type receiver at the fusion center. Despite its suboptimality, the ZF-type receiver is simple to implement and shows comparable performance with the ML receiver in the low signal-to-noise ratio (SNR) regime but experiences an error rate floor at high SNR. It is shown that this error floor can be overcome by increasing the number of receive nodes.

A Low Complexity Sign Detection and Text Localization Method for Mobile Applications

We propose a low complexity method for sign detection and text localization in natural images. This method is designed for mobile applications (e.g., unmanned or handheld devices) in which computational and energy resources are limited. No prior assumption is made regarding the text size, font, language, or character set. However, the text is assumed to be located on a homogeneous background using a contrasting color. We have deployed our method on a Nokia N800 cellular phone as part of a system for automatic detection and translation of outdoor signs. This handheld device is equipped with a 0.3-megapixel camera capable of acquiring images of outdoor signs that typically contain enough details for the sign to be readable by a human viewer. Our experiments show that the text of these images can be accurately localized within the device in a fraction of a second.

Which Point Configurations are Determined by the Distribution of their Pairwise Distances

In a previous paper we showed that, for any n ≥ m + 2, most sets of n points in ℝm are determined (up to rotations, reflections, translations and relabeling of the points) by the distribution of their pairwise distances. But there are some exceptional point configurations which are not reconstructible from the distribution of distances in the above sense. In this paper, we concentrate on the planar case m = 2 and present a reconstructibility test with running time O(n11). The cases of orientation preserving rigid motions (rotations and translations) and scalings are also discussed.

Hardware-Friendly Descreening

Conventional electrophotographic printers tend to produce Moire¿ artifacts when used for printing images scanned from printed material such as books and magazines. We propose a novel noniterative, nonlinear, and space-variant descreening filter that removes a wide range of Moire¿-causing screen frequencies in a scanned document while preserving image sharpness and edge detail. This filter is inspired by Perona-Maliks anisotropic diffusion equation. The amount of diffusion of the image intensity resulting from applying the filter is governed by an edge intensity estimate that is robust under halftone noise. More precisely, the filter extracts a spatial feature vector comprising local intensity gradients estimated from a local window in a presmoothed version of the noisy input image. Tunable nonlinear polynomial functions of this feature vector are then used to perform one iteration of a discrete diffusion controlled by the intensity gradient. The polynomial functions and feature extraction kernels are selected empirically in order to minimize computation while ensuring robust performance across a wide range of test images on a target imaging platform. The algorithm uses integer arithmetic, mostly relying on low-cost bit-wise shift and addition operations, and uses a strictly sequential architecture to provide a cost-effective and robust descreening solution in practical imaging devices including copiers and multifunction printers. We compare the performance of the proposed algorithm to other descreening solutions and demonstrate that the new algorithm improves quality over the existing methods while reducing computation.

An algorithm for automatic skin smoothing in digital portraits

We describe an automatic method for beautifying digital portraits by smoothing the skin of the face. The method builds on existing face detection and face feature alignment technology to automatically segment the face and neck areas to be smoothed. A smoothing filter is then applied to these areas. The resulting portraits are enhanced in a subtle and natural fashion.

Hardware-friendly descreening

Conventional electrophotographic printers tend to produce Moire artifacts when used for printing images scanned from printed material such as books and magazines. Inspired by anisotropic diffusion, we propose a novel non-iterative, non-linear, and space-variant de- screening filter that removes a wide range of Moire-causing screen frequencies in a scanned document while preserving image sharpness and edge detail. The amount of diffusion of the image intensity resulting from applying the filter is governed by an estimate of the gradient that is robust under halftone noise. More precisely, the filter extracts a spatial feature vector comprising local intensity gradients estimated from a local window in a pre-smoothed version of the noisy input image. Tunable non-linear polynomial functions of this feature vector are then used to perform one iteration of a discrete diffusion controlled by the intensity gradient. We compare the performance of the proposed algorithm to other descreening solutions and demonstrate that the new algorithm improves quality over the existing methods while reducing computation.

Faithful Shape Representation for 2D Gaussian Mixtures

It has been recently discovered that a faithful representation for the shape of some simple distributions can be constructed using invariant statistics [1,2]. In this paper, we consider the more general case of a Gaussian mixture model. We show that the shape of generic Gaussian mixtures can be represented without any loss by the distribution of the distance between two points independently drawn from this mixture. In other words, we show that if their respective distributions of distances are the same, then there exists a rigid transformation mapping one Gaussian mixture onto the other. Our main motivation is the problem of recognizing the shape of an object represented by points given noisy measurements of these points which can be modeled as a Gaussian mixture.

A framework for modeling 3D scenes using pose-free equations

Many applications in computer graphics require detailed 3D digital models of real-world environments. The automatic and semi-automatic modeling of such spaces presents several fundamental challenges. In this work, we present an easy and robust camera-based acquisition approach for the modeling of 3D scenes which is a significant departure from current methods. Our approach uses a novel pose-free formulation for 3D reconstruction. Unlike self-calibration, omitting pose parameters from the acquisition process implies no external calibration data must be computed or provided. This serves to significantly simplify acquisition, to fundamentally improve the robustness and accuracy of the geometric reconstruction given noise in the measurements or error in the initial estimates, and to allow using uncalibrated active correspondence methods to obtain robust data. Aside from freely taking pictures and moving an uncalibrated digital projector, scene acquisition and scene point reconstruction is automatic and requires pictures from only a few viewpoints. We demonstrate how the combination of these benefits has enabled us to acquire several large and detailed models ranging from 0.28 to 2.5 million texture-mapped triangles.