David Marimon

Evaluation of Mobile Augmented Reality Applications for Tourism Destinations

Every city contains interesting places and stories to be discovered. Mobile Augmented Reality provides the means to enrich tourists through precise and tailored information about the surroundings of the area they are visiting. MobiAR is an AR platform based on Android, which assists users who need tourist information about a city. When users observe reality through the MobiAR application via their mobile devices, they can experience events that took place at their location through multimedia content, and can access useful information to plan their routes in the city. This paper describes the MobiAR platform and presents the evaluation process that has been applied to the MobiAR application, in order to gather the opinion of real users.

A master-slave approach for object detection and matching with fixed and mobile cameras

Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, such variability is addressed by using a large training data. However, only objects present in the training data can be detected. This paper introduces a vision-based system to address such problem. A master-slave approach is presented where a mobile camera (the slave) can match any object detected by a fixed camera (the master). Features extracted by the master camera are used to detect the object of interest in the slave camera without the use of any training data. A single observation is enough regardless of the changes in illumination, viewpoint, color distribution and image quality. A coarse to fine description of the object is presented built upon image statistics robust to partial occlusions. Qualitative and quantitative results are presented in an indoor and an outdoor urban scene.

DARTs: Efficient scale-space extraction of DAISY keypoints

Winder et al. [15, 14] have recently shown the superiority of the DAISY descriptor [12] in comparison to other widely extended descriptors such as SIFT [8] and SURF [1]. Motivated by those results, we present a novel algorithm that extracts viewpoint and illumination invariant keypoints and describes them with a particular implementation of a DAISY-like layout. We demonstrate how to efficiently compute the scale-space and re-use this information for the descriptor. Comparison to similar approaches such as SIFT and SURF show higher precision vs recall performance of the proposed method. Moreover, we dramatically reduce the computational cost by a factor of 6x and 3x, respectively. We also prove the use of the proposed method for computer vision applications.

A global probabilistic framework for the foreground, background and shadow classification task

Over the years, many works have been published on the two-dimensional foreground segmentation task, describing different methods that treat to extract that part of the scene containing active entities. In most of the cases, the stochastic background process for each pixel is modeled first, and then the foreground pixels are classified as an exception to the model or using maximum a posteriori (MAP) or maximum likelihood (ML). The shadow is usually removed in a later stage and salt and pepper noise is treated with connected component analysis or mathematical morphology. In this paper, we propose a global method that classifies each pixel by finding the best possible class (foreground, background, shadow) examining the image globally. A Markov Random Field is used to represent the dependencies between all the pixels and classes and the global optimal solution is approximated with the Belief Propagation algorithm. The method can extend most local methods and increase their accuracy. In addition, this approach brings a probabilistic justification of the classification problem and it avoids the use of additional post-processing techniques.

Large-scale visual search based on voting in reduced pose space with application to mobile search and video collections

We present a novel visual search system that deals with scalability, is fast enough for commercial applications, and addresses limitations present in current visual search engines. Most scalable visual search approaches rely on local features, the Bag of Visual Words representation, and a ranking mechanism based on some vector space model [1, 2]. However, since in those methods the initial rankings do not take into account any spatial information, they are not well suited to identify multiple small objects “buried” within complex scenes. To alleviate this limitation we propose to perform the initial ranking using clustering of matches in a limited pose space. We also describe its smooth integration with Soft Assignment of Visual Words and RANSAC-inspired spatial consistency verification. We demonstrate that our system addresses the problem and show the use of the method in several commercially attractive applications.

Fast non-uniform filtering with Symmetric Weighted Integral Images

Non-uniform filters are frequently used in many image processing applications to describe regions or to detect specific features. However, non-uniform filtering is a computationally complex task. This paper presents a method to perform fast non-uniform filtering using a reduced number of memory accesses. The idea is based on integral images which are commonly used for box or Haar wavelet filtering. The disadvantage of those filters for several applications is their uniform shape. We describe a method to build Symmetric Weighted Integral Images that are tailored for a variety of kernels and the process to perform fast filtering with them. We show a relevant speedup when compared to Kernel Integral Images and large when compared to conventional non-uniform filtering by reducing the computational complexity.

[DEMO] High volume offline image recognition

We show a prototype of an offline image recognition engine, running on a tablet with Intel®AtomTM processor, searching within less than 250ms through thousands (5000+) of images. Moreover, the prototype still offers the advanced capabilities of recognising real world 3D objects, until now reserved only for cloud solutions. Until now image search within large collections of images could be performed only in the cloud, requiring mobile devices to have Internet connectivity. However, for many use cases the connectivity requirement is impractical, e.g. many museums have no network coverage, or do not want their visitors incurring expensive roaming charges. Existing commercial solutions are very limited in terms of searched collections sizes, often imposing a maximum limit of <100 reference images. Moreover, adding images typically affects the recognition speed and increases RAM requirements.