Lane markings are mymargin a key element for Autonomous Driving. The generation of high definition maps and ground-truth data require extensive manual labor. In this paper, we present an efficient and robust method for the offline annotation of lane markings, using low-density LIDAR point clouds and odometry information. The odometry is used to accumulate the scans and to process them using blocks following the trajectory of the vehicle. At each block, candidate lane marking points are detected by generating virtual scan-lines and applying a dynamically optimized filter function to the LIDAR intensity values. The lane markings are tracked block wise, and their width is estimated and classified as either solid or dashed. The results are lists of connected 3D points that represent the different lane markings. The accuracy of the proposed method was tested against manually labeled recordings. A novel evaluation methodology focused on the lateral precision of detections is presented. Moreover, a web user interface was used to load the produced annotations, achieving a reduction of 60% in the annotation time, as compared to a fully manual baseline.

The burgeoning capabilities of Web browsers to exploit full-featured devices can turn the huge pool of social connected users into a powerful network of processing assets. HTML5 and JavaScript stacks support the deployment of social client-side processing infrastructure, while WebGL and WebCL fill the gap to gain full GPU and multi-CPU performance. Mobile Grid and Mobile Cloud Computing solutions leverage smart devices to relieve the processing tasks to be performed by the service infrastructure. Motivated to gain cost-efficiency, a social network service provider can outsource the video analysis to elements of a mobile grid as an infrastructure to complement an elastic cloud service. As long as users access to videos, batch image analysis tasks are dispatched from the server, executed in the background of the client-side hardware, and finally, results are consolidated by the server. This paper presents SaW (Social at Work) to provide a pure Web-based solution as a mobile grid to complement a cloud media service for image analysis on videos.

Remote rehabilitation applications still have limited deployment. The path to achieve greater user acceptance and adherence lies in the provision of solutions in their real-life context. Such acceptance is gained through flexibility provided in terms of location, client device, interaction means, and content. This article presents a universal remote rehabilitation delivery approach supporting the introduced flexibility needs. Furthermore, an implementation of the approach in joint rehabilitation for the elderly is described. The approach has been evaluated in a real scenario within Donostia Hospital. The usability evaluation results show the validity of the approach and the acceptance of the developed human −computer interaction paradigm.

Context categorization is a fundamental pre-requisite for multi-domain multimedia content analysis applications. Most feature extraction methods require prior knowledge to decide if they are suitable for a specific domain and to optimize their input parameters. In this paper, we introduce a new color image context categorization method (DITEC) based on the trace transform. The problem of dimensionality reduction of the obtained trace transform signal is addressed through statistical descriptors of its frequency representation that keep the underlying information. We also analyze the distortions produced by the parameters that determine the sampling of the discrete trace transform. Moreover, Feature Subset Selection (FSS) is applied to both, improve the classification performance and compact the final length of the descriptor that will be provided to the classifier. These extracted features offer a highly discriminant behavior for content categorization without prior knowledge requirements. The method has been experimentally validated through two different datasets.

A force-force controller of a master-slave force-feedback teleoperation system is proposed. This scheme is especially suitable for non-backdriveable manipulators. Forces and torques exerted by the operator and the environment and measured by a six axis force sensor are used to compute in real time a desired trajectory which is tracked by a PD controller. This system has been applied to a master-slave system, in which the master robot is a Stewart platform. The use of virtual springs prevents the platform from running into singularities and from getting close to workspace boundaries. Experimental results the satisfactory performance of control algorithm with virtual springs.

Master-slave teleoperators have to cope with tasks so different such as unconstrained motion and hard contact tasks. If the controller is tuned on one of them, the other will not perform well and may, eventually, lead to an unstable system. In this paper, an adaptive modification of the desired robot behaviour in the orthogonal direction to the contact task is proposed. This strategy makes the system stable in hard contact tasks while easy to control in unconstrained motion. Experimental results obtained with a 2-degree-of-freedom manipulator prove the effectiveness of the method.