Ramon Morros

Multimodal identification and localization of users in a smart environment

Detecting the location and identity of users is a first step in creating context-aware applications for technologically-endowed environments. We propose a system that makes use of motion detection, person tracking, face identification, feature-based identification, audio-based localization, and audio-based identification modules, fusing information with particle filters to obtain robust localization and identification. The data streams are processed with the help of the generic client-server middleware SmartFlow, resulting in a flexible architecture that runs across different platforms.

Audio, video and multimodal person identification in a smart room

In this paper, we address the modality integration issue on the example of a smart room environment aiming at enabling person identification by combining acoustic features and 2D face images. First we introduce the monomodal audio and video identification techniques and then we present the use of combined input speech and face images for person identification. The various sensory modalities, speech and faces, are processed both individually and jointly. Its shown that the multimodal approach results in improved performance in the identification of the participants.

Object matching based on partition information

This paper presents a new technique for object matching that exploits the information about transitions in the image obtained by means of a segmentation approach. Object matching is performed by comparing a transformed version of an object shape model (reference contour) to the contours in the image partition. The comparison is based on a distance map that measures the Euclidean distance between any point in the image to the partition contours. Examples using parametric and non-parametric reference contours are provided to assess the quality of the proposed technique.

Partition tree for a segmentation-based video coding system

In this paper we describe a very low bit rate video coding system that belongs to the class of object based coding systems. The system takes advantage of homogeneity in both gray level and motion, trying to use the most adequate criterion for defining a partition in order to obtain the best compromise between cost and quality. In order to do so, multiple partitions are generated for every image in a hierarchical way, from the coarsest one to the finest one. Then, a decision step will chose which parts of the image need to be coded with regions from one partition or from another. The aim of the paper is to study the features that this multiple partition must have, and propose a way to create it.

A segmentation-based coding system allowing manipulation of objects (SESAME)

We present a coding scheme that achieves, for each image in the sequence, the best segmentation in terms of rate-distortion theory. It is obtained from a set of initial regions and a set of available coding techniques. The segmentation combines spatial and motion criteria. It selects at each area of the image the most adequate criterion for defining a partition in order to obtain the best compromise between cost and quality. In addition, the proposed scheme is very suitable for addressing content-based functionalities.

Histogram computation based on image bitwise decomposition

In this paper, a new method to compute the image histogram is presented, along with the image maximum and minimum values. It is intended for highly parallel architectures such as the ones found in focal plane processors (FPP). This new approach exploits this parallelism relying on the privatization technique to avoid the memory collision problem, while the bin frequency is obtained through image bitwise manipulation. Unlike traditional privatization techniques, our method exhibits a trade-off between processing time and bin size. That is, it can be adapted as a power-of-two bin size histogram and the computation time decreases exponentially as the bin size is reduced on each power of two, allowing high computational flexibility.