Object Edge Contour Localisation Based on HexBinary Feature Matching

This paper addresses the issue of localising object edge contours in cluttered backgrounds to support robotics tasks such as grasping and manipulation and also to improve the potential perceptual capabilities of robot vision systems. Our approach is based on coarse-to-fine matching of a new recursively constructed hierarchical, dense, edge-localised descriptor, the HexBinary, based on the HexHog descriptor structure first proposed in [1]. Since Binary String image descriptors [2]– [5] require much lower computational resources, but provide similar or even better matching performance than Histogram of Orientated Gradient (HoG) descriptors, we have replaced the HoG base descriptor fields used in HexHog with Binary Strings generated from first and second order polar derivative approximations. The ALOI [6] dataset is used to evaluate the HexBinary descriptors which we demonstrate to achieve a superior performance to that of HexHoG [1] for pose refinement. The validation of our object contour localisation system shows promising results with correctly labelling ~86% of edgel positions and mis-labelling ~3%

A Portable Active Binocular Robot Vision Architecture for Scene Exploration

We present a portable active binocular robot vision archi- tecture that integrates a number of visual behaviours. This vision archi- tecture inherits the abilities of vergence, localisation, recognition and si- multaneous identification of multiple target object instances. To demon- strate the portability of our vision architecture, we carry out qualitative and comparative analysis under two different hardware robotic settings, feature extraction techniques and viewpoints. Our portable active binoc- ular robot vision architecture achieved average recognition rates of 93.5% for fronto-parallel viewpoints and, 83% percentage for anthropomorphic viewpoints, respectively

Interactive Perception based on Gaussian Process Classification Applied to Household Object Recognition & Sorting

No abstract available

Naltrexone-assisted rapid methadone discontinuation : a pilot study

Rapport de Synthèse : Un sevrage lent comme méthode élective pour l'interruption de la méthadone est coûteux en termes de temps, le plus souvent associé à un taux élevé d'abandon. Bien que les méthodes ultrarapides de désintoxication des opiacés aient gagné en popularité récemment, elles sont chères et posent les problèmes spécifiques liés aux patients traités par la méthadone. Méthodologie: ont été inclus dans l'étude dix patients en traitement de substitution avec de la méthadone. La dernière dose de méthadone a été administrée le matin même du jour de l'admission, en préalable à l'hospitalisation. Les médicaments suivants ont été administrés le jour suivant l'admission: ondansetron 36mg, ranitidine 40mg, loperamide 8m., clonazepam 4m., promazine 1OOmg, metoclopramide 70mg, naltrexone 5Omg. L'échelle objective de sevrage des opiacés (Objective Opiate Withdrawal Scale) a été appliquée au deuxième, troisième et quatrième jour d'hospitalisation, deux fois par jour, à 8h00 et 18h00. Un suivi a été réalisé sous la forme d'entretiens téléphoniques pendant une semaine, respectivement six mois après la date de sortie de l'hôpital, faisant suite à la désintoxication. Un autre entretient téléphonique a été réalisé dans les six mois suivant le "post-sevrage", avec pour objectif d'investiguer la continuité du traitément, une éventuelle rechute dans l'abus de drogues et une possible réintroduction de la méthadone. Résultats: nous avons pu déterminer quatre groupes de symptômes, sur la base d'une observation de trois jours d'évolution: 1) Les signes typiques du syndrome de sevrage de retrait des opiacés, symptôme de froid et chaud, pilo-érection, anxiété caractérisée par une intensité initiale élevée et une disparition relativement continue. 2) Hyperactivité neurovégétative caractérisée par une intensité initiale élevée et une rapide disparition. 3) Phénomènes neurovégétatifs dont l'intensité s'est maintenue durant toute la période d'observation. 4) Contractions musculaires, insomnies et anorexie, manque d'appétit, réapparaissant chez certains patients au 2ème et au début du 3ème jour. Conclusions: une procédure courte de désintoxication utilisant une dose unique de naltrexone s'avère être une méthode alternative valable pour un sevrage de la méthadone. Cette méthode semble accélérer et écourter la symptomatologie associée au sevrage. Le cours des symptômes peut être interprété comme biphasique. Une première phase de retrait est éminemment caractérisée par tous les symptômes typiques eux-mêmes et probablement induits par la naltrexone. La seconde phase, pour un plus petit nombre de patients, peut être interprétée comme en corrélation avec une concentration de méthadone en diminution significative ultérieurement

Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, FPFH features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

On the Calibration of Active Binocular and RGBD Vision Systems for Dual-Arm Robots

This paper describes a camera and hand-eye calibration methodology for integrating an active binocular robot head within a dual-arm robot. For this purpose, we derive the forward kinematic model of our active robot head and describe our methodology for calibrating and integrating our robot head. This rigid calibration provides a closedform hand-to-eye solution. We then present an approach for updating dynamically camera external parameters for optimal 3D reconstruction that are the foundation for robotic tasks such as grasping and manipulating rigid and deformable objects. We show from experimental results that our robot head achieves an overall sub millimetre accuracy of less than 0.3 millimetres while recovering the 3D structure of a scene. In addition, we report a comparative study between current RGBD cameras and our active stereo head within two dual-arm robotic testbeds that demonstrates the accuracy and portability of our proposed methodology

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2\% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.Comment: 9 pages, accepted by IROS201

A Biologically Motivated Software Retina for Robotic Sensors Based on Smartphone Technology

A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the phone is so exquisitely integrated having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to children’s toys, such as the Meccanoid robot, employ a smartphone to provide a vision system/control system. Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels. In our research we have been developing biologically motivated foveated vision algorithms, potentially some 100 times more efficient than their conventional counterparts, based on a model of the mammalian retina we have developed. Vision systems based on the foveated architectures found in mammals have the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage, [2,3,4]. We are now at the early stages of investigating how best to port our foveated architecture onto a smartphone platform. To achieve the required levels of performance we propose to optimise our retina model to the ARM processors utilised in smartphones, in conjunction with their integrated GPUs, to provide a foveated smart vision system on a smartphone. Our current goal is to have a foveated system running in real-time to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone which communicates with conventional hardware performing back-end visual classification/interpretation. We believe that, as in Nature, space-variance is the key to achieving the necessary data reduction to be able to implement the complete visual processing chain on the smartphone itself

The intensity of rainfall in mediterranean environments. Extreme values according to the scale of observation

Mediterranean environments are dominated by episodes of torrential rain, whereby the critical parameter is not so much the amount of rain these episodes accumulate, but rather the intensity they can reach. The heavy intensities that extreme events can achieve are critical in the dynamics of soil erosion; those related to triggering of debris-flow and above all in hydrology, as they affect rainfall-runoff conversion processes, runoff and coefficient thresholds and flash floods generation