The satellite-based Global Positioning System (GPS) provides robust localization on smartphones outdoors. In indoor environments, however, no system is close to achieving a similar level of ubiquity, with existing solutions offering different trade-offs in terms of accuracy, robustness and cost. In this paper, we develop a multi-modal positioning system, targeted at smartphones, which aims to get the best out of each of its constituent modalities. More precisely, we combine Bluetooth low energy (BLE) beacons, round-trip-time (RTT) enabled WiFi access points and the smartphone's inertial measurement unit (IMU) to provide a cheap robust localization system that, unlike fingerprinting methods, requires no pre-training. To do this, we use a probabilistic algorithm based on a conditional random field (CRF). We show how to incorporate sparse visual information to improve the accuracy of our system, using pose estimation from pre-scanned visual landmarks, to calibrate the system online. Our method achieves an accuracy of around 2 meters on two realistic datasets, outperforming other distance-based localization approaches. We also compare our approach with an ultra-wideband (UWB) system. While we do not match the performance of UWB, our system is cheap, smartphone compatible and provides satisfactory performance for many applications.

This paper addresses a key issue in the problem of reconstructing a panoramic view from several pictures taken with a hand-held camera, namely the estimation of some ill-posed parameters using an external constraint. For many practical reasons, a panoramic reconstruction has to be performed in several independent steps, resulting in a set of different measurements of the same reality. For example, the focal length can be estimated with each pair of overlapping images. The idea is to introduce some a priori knowledge about the world by means of a constraint on the parameter set. In the former example, the constraint would impose equality on all the focal length estimates. This paper describes the appropriate correction that needs to be applied to the parameters in order to obtain a coherent result. It also suggests a way to evaluate if a constraint is plausible given a set of initial estimates. The basic idea behind the method is to modify the parameters without significantly changing the overlapping part of the images. The method is evaluated using two different experimental setups. The first aims at improving the quality of a full panoramic image. The second measures independently the positions in space of two planes using two pictures. The latter experiment shows that the two computed motions can be considered as a single one with two different planes in space.

Digital terrains are generally large files and need to be simplified to be rendered efficiently. We propose to build an adaptive embedded triangulation based on a binary tree structure to generate multiple levels of details. We present a O(nlogn) decimation algorithm and a O(nlogn) refinement algorithm, where n is the number of elevation points. We compare them in a rate-distortion (RD) framework. The algorithms are based on an improved version of the optimal tree pruning algorithm G-BFOS allowing one to deal with constrained tree structures and non-monotonic tree functionals.

Theory and experimentation are both complementary in sciences. Where the former builds up a formal framework, the latter helps humans to develop their intuition. Hard-printed books are great supports for theorems and formulas repositories, but stay desperately static when providing examples. This paper presents some directions towards a framework for digital publishing, distance learning and computer-aided teaching. The interactive digital signal processing (DSP) book is built in this framework and provides a concrete example to the reader. The book, reachable at http://lcavwww.epfl.ch/DSPBook, is used as a digital support for a course taught by our lab to a third year undergraduate class in electrical engineering.

The vast majority of current Internet traffic is generated by web browsing applications. Proxy caching, which allows some of the most popular web objects to be cached at intermediate nodes within the network, has been shown to provide substantial performance improvements. In this paper we argue that image-specific caching strategies are desirable and will result in improved performance over approaches treating all objects alike. We propose that Soft Caching, where an image can be cached at one of a set of levels of resolutions, can benefit the overall performance when combined with cache management strategies that estimate, for each object, both the bandwidth to the server where the object is stored and the appropriate resolution level demanded by the user. We formalize the cache management problem under these conditions and describe an experimental system to test these techniques.

An explosion of on-line image and video data in digital form is already well underway. With the exponential rise in interactive information exploration and dissemination through the World-Wide Web (WWW), the major inhibitors of rapid access to on-line video data are costs and management of capture and storage, lack of real-time delivery, and nonavailability of content-based intelligent search and indexing techniques. The solutions for capture, storage, and delivery may be on the horizon or a little beyond. However, even with rapid delivery, the lack of efficient authoring and querying tools for visual content-based indexing may still inhibit as widespread a use of video information as that of text and traditional tabular data is currently. In order to be able to nonlinearly browse and index into videos through visual content, it is necessary to develop authoring tools that can automatically separate moving objects and significant components of the scene, and represent these in a compact form. Given that video data comes in torrents-almost a megabyte every 30th of a second-it will be highly inefficient to search for objects and scenes in every frame of a video. In this paper, we present techniques to automatically derive compact representations of scenes and objects from the motion information. Image motion is a significant cue in videos for the separation of scenes into their significant components and for the separation of moving objects. Motion analysis is useful in capturing the visual content of videos for indexing and browsing in two different ways. First, separation of the static scene from moving objects can be accomplished by employing dominant 2D/3D motion estimation methods. Alternatively, if the goal is to be able to represent the fixed scene too as a composition of significant structures and objects, then simultaneous multiple motion methods might be more appropriate. In either case, view-based summarized representations of the scene can be created by video compositing/mosaicing based on the estimated motions. We present robust algorithms for both kinds of representations: 1) dominant motion estimation based techniques which exploit a fairly common occurrence in videos that a mostly fixed background (scene) is imaged with or without independently moving objects, and 2) simultaneous multiple motion estimation and representation of motion video using layered representations. Ample examples of the representations achieved by each method are included in the paper.

Representing and modeling the motion and spatial support of multiple objects and surfaces from motion video sequences is an important intermediate step towards dynamic image understanding. One such representation, called layered representation, has recently been proposed. Although a number of algorithms have been developed for computing these representations, there has not been a consolidated effort into developing a precise mathematical formulation of the problem. This paper presents one such formulation based on maximum likelihood estimation (MLE) of mixture models and the minimum description length (MDL) encoding principle. The three major issues in layered motion representation are: (i) how many motion models adequately describe image motion, (ii) what are the motion model parameters, and (iii) what is the spatial support layer for each motion model.>

It is fairly common in video sequences that a mostly fixed background (scene) is imaged with or without objects. The dominant background changes in the image plane mostly due to camera operations and motion (zoom, pan, tilt, track etc.). We address the problem of computation of the dominant image transformation over time and demonstrate how this can be effectively used for efficient video representation through video mosaicing and image registration. We formulate the problem of dominant component estimation as that of model based robust estimation using M estimators with direct, multi resolution methods. In addition to 2D affine and plane projective models, that have been used in the past for describing image motion using direct methods, we also employ a true 3D model of motion and scene structure imaged with uncalibrated cameras. This model parameterizes the image motion as that due to a planar component and a parallax component. For rigid 3D scenes imaged under camera motion only, least squares (LS) methods with the plane and parallax parameterization are also presented. Furthermore, in the context of robust estimation, in contrast with previous approaches for similar problems, our algorithm employs an automatic computation of a scale parameter that is crucial in rejecting the non dominant components as outliers.>

The major inhibitors of rapid access to online video data are costs and management of capture and storage, lack of high-speed real-time delivery and non-availability of content and context based intelligent search and indexing techniques. The solutions for capture, storage and delivery maybe on the horizon, however the lack of visual content based indexing of video and image information may still inhibit as widespread a use of this information modality as that of text or tabular data is currently. We present techniques for compact visual representation of video data that will be useful for visual content based presentation and indexing. Video data comes in torrents-almost a megabyte every 30th of a second-but also affords the exploitation of relatively smoothly changing information over time. The techniques presented exploit the motion information across video frames to represent the underlying scene in a compact visual form as it is seen across many slowly varying frames in a video. Two classes of techniques are presented: (i) dominant motion estimation based techniques which exploit a fairly common occurrence in videos that a mostly fixed background (scene) is imaged with or without independently moving objects, and (ii) simultaneous multiple motion estimation and representation of motion video using layered representations.

Region based coding schemes are among the most promising compression techniques for very low bit-rates. They consist of image segmentation, contour and texture coding. In this paper, a new shape representation for segmented images based on the geodesic morphological skeleton is presented. It is used for the coding of contour prediction residues obtained after motion compensation based on temporally modeled motion vectors. A non-reversible pre-skeletonization filter removes contour noise. A reversible post-skeletonization filter allows progressive transmission of contour information. It is based on the spatial distribution of the skeleton points, which are found to be close to known contours.>

This paper presents a hierarchical motion estimation method considering both a large spatial and temporal support. The problem of motion estimation is formulated as a time-varying parameter estimation problem. The main advantage of the proposed algorithm is its ability to constrain the computation of the flow field spatially and temporally at the same time, by defining each motion parameter as a linear combination of some orthogonal time functions. The number of time basis varies according to the complexity of the movement and is determined automatically by means of a multiresolution approach and of statistical tests. We also present two different applications which underline the validity of the chosen approach.