44 research outputs found

    Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model

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    Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de Economía y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional Andalucía Tech

    Topology-preserving perceptual segmentation using the Combinatorial Pyramid

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    Scene understanding and other high-level visual tasks usually rely on segmenting the captured images for dealing with a more efficient mid-level representation. Although this segmentation stage will consider topological constraints for the set of obtained regions (e.g., their internal connectivity), it is typical that the importance of preserving the topological relationships among regions will be not taken into account. Contrary to other similar approaches, this paper presents a bottom-up approach for perceptual segmentation of natural images which preserves the topology of the image. The segmentation algorithm consists of two consecutive stages: firstly, the input image is partitioned into a set of blobs of uniform colour (pre-segmentation stage) and then, using a more complex distance which integrates edge and region descriptors, these blobs are hierarchically merged (perceptual grouping). Both stages are addressed using the Combinatorial Pyramid, a hierarchical structure which can correctly encode relationships among image regions at upper levels. The performance of the proposed approach has been initially evaluated with respect to groundtruth segmentation data using the Berkeley Segmentation Dataset and Benchmark. Although additional descriptors must be added to deal with small regions and textured surfaces, experimental results reveal that the proposed perceptual grouping provides satisfactory scores

    Towards Active Image Segmentation: the Foveal Bounded Irregular Pyramid

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    Presentado en: 2nd workshop on Recognition and Action for Scene Understanding York, Inglaterra August 30, 2013It is well established that the units of attention on human vision are not merely spatial but closely related to perceptual objects. This implies a strong relationship between segmentation and attention processes. This interaction is bi-directional: if the segmentation process constraints attention, the way an image is segmented may depend on the specific question asked to an observer, i.e. what she 'attend' in this sense. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. While much work has recently been focused on computational models for object-based attention, the design and development of multi-resolution structures that can segment the input image according to the focused perceptual unit is largely unexplored. This paper proposes a novel structure for multi-resolution image segmentation that extends the encoding provided by the Bounded Irregular Pyramid. Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image region. Preliminary results obtained from the segmentation of natural images show that the performance of the approach is good in terms of speed and accuracy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    CLARA: Building a Socially Assistive Robot to Interact with Elderly People

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    Although the global population is aging, the proportion of potential caregivers is not keeping pace. It is necessary for society to adapt to this demographic change, and new technologies are a powerful resource for achieving this. New tools and devices can help to ease independent living and alleviate the workload of caregivers. Among them, socially assistive robots (SARs), which assist people with social interactions, are an interesting tool for caregivers thanks to their proactivity, autonomy, interaction capabilities, and adaptability. This article describes the different design and implementation phases of a SAR, the CLARA robot, both from a physical and software point of view, from 2016 to 2022. During this period, the design methodology evolved from traditional approaches based on technical feasibility to user-centered co-creative processes. The cognitive architecture of the robot, CORTEX, keeps its core idea of using an inner representation of the world to enable inter-procedural dialogue between perceptual, reactive, and deliberative modules. However, CORTEX also evolved by incorporating components that use non-functional properties to maximize efficiency through adaptability. The robot has been employed in several projects for different uses in hospitals and retirement homes. This paper describes the main outcomes of the functional and user experience evaluations of these experiments.This work has been partially funded by the EU ECHORD++ project (FP7-ICT-601116), the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 825003 (DIH-HERO SUSTAIN), the RoQME and MiRON Integrated Technical Projects funded, in turn, by the EU RobMoSys project (H20202-732410), the project RTI2018-099522-B-C41, funded by the Gobierno de España and FEDER funds, the AT17-5509-UMA and UMA18-FEDERJA-074 projects funded by the Junta de Andalucía, and the ARMORI (CEIATECH-10) and B1-2021_26 projects funded by the University of Málaga. Partial funding for open access charge: Universidad de Málaga

    Testing a fully autonomous robotic salesman in real scenarios

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    Over the past decades, the number of robots deployed in museums, trade shows and exhibitions have grown steadily. This new application domain has become a key research topic in the robotics community. Therefore, new robots are designed to interact with people in these domains, using natural and intuitive channels. Visual perception and speech processing have to be considered for these robots, as they should be able to detect people in their environment, recognize their degree of accessibility and engage them in social conversations. They also need to safely navigate around dynamic, uncontrolled environments. They must be equipped with planning and learning components, that allow them to adapt to different scenarios. Finally, they must attract the attention of the people, be kind and safe to interact with. In this paper, we describe our experience with Gualzru, a salesman robot endowed with the cognitive architecture RoboCog. This architecture synchronizes all previous processes in a social robot, using a common inner representation as the core of the system. The robot has been tested in crowded, public daily life environments, where it interacted with people that had never seen it before nor had a clue about its functionality. Experimental results presented in this paper demonstrate the capabilities of the robot and its limitations in these real scenarios, and define future improvement actions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    The cognitive architecture of a robotic salesman

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    This paper describes a robotics cognitive architecture for social robots named CORTEX. This architecture integrates di fferent levels of abstraction (from basic geometry to high-level predicates) into a unique Deep Space Representation (DSR) that diff erent agents interface. These agents update the contents of the DSR with new data from the outer world, and execute, plan and design behaviours. The design of CORTEX as an unified deep representation allows to fit both the subsymbolic processing and exibility requirements of robot control. In this paper a first implementation of CORTEX has been integrated into Gualzru, a robotic salesman, and tested in real scenarios. Results show that this cognitive architecture allows this robot to adequately execute its use case, and that it has a promising adaptability to achieve new tasks and be used in new scenarios.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Integrating the users in the design of a robot for making Comprehensive Geriatric Assessments (CGA) to elderly people in care centers

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    Lisboa, (28-31 de agosto 2017)Comprehensive Geriatric Assessment (CGA) is a multidimensional and multidisciplinary diagnostic instrument that helps provide personalized care to the elderly, by evaluating their physical and mental state. In a social and economic context of growing ageing populations, medical experts can save time and effort if provided with interactive tools to efficiently assist them in doing CGAs, managing standardized tests or data collection. Recent research proposes the use of social robots as the central part of these tools. These robots must be able to unfold all functionalities that questionnaires or motion-based tests require, including natural language, face tracking and monitoring, human motion capture and so on. But another issue is the robot's acceptability and trust by the end-users, both patients (elderly people) and clinicians: the robot needs to be able to engage with the patients during the interaction sessions, and must be perceived as a useful and efficient tool by the clinicians. This paper presents the acquisition of new user requirements for CLARC, through participatory and user-centered design approach, to inform the improvement of both interface and interaction. Thirty eight persons (elderly people, caregivers and health professionals) were involved in the design process of CLARC, based on user-centered methods and techniques of Human-Computer Interaction discipline.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116) and the TIN2015-65686-C5-1-R Spanish Ministerio de EconomÍa y Competitividad project and FEDER funds

    Percepts symbols or Action symbols? Generalizing how all modules interact within a software architecture for cognitive robotics

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    Robots require a close coupling of perception and action. Cognitive robots go beyond this to require a further coupling with cognition. From the perspective of robotics, this coupling generally emphasizes a tightly integrated perceptuomotor system, which is then loosely connected to some limited form of cognitive system such as a planner. At the other end, from the perspective of automated planning, the emphasis is on a highly functional system that, taken to its extreme, calls perceptual and motor modules as independent functions. This paper proposes to join both perspectives through a unique representation where the responses of all modules on the software architecture (percepts or actions) are grounded using the same set of symbols. This allows to generalize the signal-to-symbol divide that separates classic perceptuomotor and automated planning systems, being the result a software architecture where all software modules interact using the same tokens.This paper has been partially supported by the Spanish Ministerio de Economía y Competitividad TIN2015-65686-C5 and FEDER funds and by the FP7 EU project ECHORD++ grant 601116 (CLARK project)

    Perceptions or Actions? Grounding How Agents Interact Within a Software Architecture for Cognitive Robotics

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    One of the aims of cognitive robotics is to endow robots with the ability to plan solutions for complex goals and then to enact those plans. Additionally, robots should react properly upon encountering unexpected changes in their environment that are not part of their planned course of actions. This requires a close coupling between deliberative and reactive control flows. From the perspective of robotics, this coupling generally entails a tightly integrated perceptuomotor system, which is then loosely connected to some specific form of deliberative system such as a planner. From the high-level perspective of automated planning, the emphasis is on a highly functional system that, taken to its extreme, calls perceptual and motor modules as services when required. This paper proposes to join the perceptual and acting perspectives via a unique representation where the responses of all software modules in the architecture are generalized using the same set of tokens. The proposed representation integrates symbolic and metric information. The proposed approach has been successfully tested in CLARC, a robot that performs Comprehensive Geriatric Assessments of elderly patients. The robot was favourably appraised in a survey conducted to assess its behaviour. For instance, using a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), patients reported an average of 4.86 when asked if they felt confident during the interaction with the robot. This paper proposes a mechanism for bringing the perceptual and acting perspectives closer within a distributed robotics architecture. The idea is built on top of the blackboard model and scene graphs. The modules in our proposal communicate using a short-term memory, writing the perceptual information they need to share with other agents and accessing the information they need for determining the next goals to address
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