RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Análisis multitemporal del cambio de uso del suelo, en el Paisaje Terrestre Protegido Miraflor Moropotente Nicaragua, 1993-2011

El análisis multitemporal permite detectar cambios entre diferentes fechas de referencia, deduciendo la evolución del medio natural o las repercusiones de la acción humana sobre el medio. El propósito del estudio fue evaluar el cambio de uso del suelo en el Paisaje Terrestre Miraflor Moropotente en el período 1993-2011, a través de imágenes satelitales, a fin de determinar el estado de fragmentación del paisaje. Los cambios de usos de suelo fueron derivados de la clasificación de tres imágenes Landsat TM, con una resolución espacial de 30 metros tomadas en febrero de 1993, abril de 2000 y enero 2011. Se realizó una verificación en campo para la identificación de coberturas de suelo y la corroboración en las imágenes satelitales. La fragmentación se realizó con el cálculo de métricas e índices de fragmentación a nivel del paisaje. Los principales resultados muestran que los cambios de uso de suelo están determinados por la degradación antrópica, principalmente en la conversión de la vegetación nativa a espacios agrícolas y la expansión de la ganadería. El crecimiento demográfico y los monocultivos van ejerciendo presión sobre el bosque, transformando zonas de vocación forestal a cultivos agrícolas. Los cambios de cobertura han significado un paisaje fragmentado con diferentes grados de perturbación, que conllevan a una disminución de la superficie de hábitats naturales, reducción del tamaño de los fragmentos y aislamientos de los mismos.

Human-computer interaction for optical music recognition tasks

The need to digitise music scores has led to the development of Optical Music Recognition (OMR) tools. Unfortunately, the performance of these systems is still far from providing acceptable results. This situation forces the user to be involved in the process due to the need of correcting the mistakes made during recognition. However, this correction is performed over the output of the system, so these interventions are not exploited to improve the performance of the recognition. This work sets the scenario in which human and machine interact to accurately complete the OMR task with the least possible effort for the user.