Topology-preserving perceptual segmentation using the Combinatorial Pyramid

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

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

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

Tracking objects with the bounded irregular pyramid

En esta tesis se propone un sistema de seguimiento de objetos basados en un nuevo método de representación y localización del objetivo. Se trata de realizar el seguimiento de objetos no rígidos en tiempo real, sin utilizar ningún modelo previo de los objetos a seguir. Para conseguir esto, se propone un nuevo modelo para caracterizar la apariencia del objeto basado en una máscara o template. Este modelo utiliza una nueva estructura piramidal, denominada Bounded Irregular Pyramid (BIP), para representar el target y el template, así como para realizar el proceso de localización del objeto o template matching de forma jerárquica, reduciendo su coste computacional. El sistema de seguimiento propuesto permite realizar el seguimiento de objetos rígidos y no rígidos utilizando una máscara que se actualiza de forma dinámica. Esta máscara incluye información de las máscaras previas, solucionando dos de las causas de fallo más importante se los sistemas de seguimiento: cambios en la apariencia del objetivo y oclusiones del mismo. Además, el sistema permite seguir varios objetos simultáneamente sin un incremento excesivo del coste computacional. Las características previamente comentadas del sistema propuesto lo hacen muy adecuado para su utilización en aplicaciones visuales más complejas, que requieren una respuesta en tiempo real

Affine image region detection and description

This paper describes a novel approach for affine invariant region detection and description. At the detection stage, a hierarchical clustering mechanism is employed to group image pixels into regions. This process is based on the Bounded Irregular Pyramid (BIP) and takes into account a colour contrast measure, internal region descriptors and attributes of their shared boundaries. High-contrasted regions are selected as salient regions. On the other hand, geometrically and photometrically normalized regions are represented by a kernel-based descriptor. The lenght descriptor is reduced by applying Principal Component Analysis (PCA). The protocol proposed by Mikolajczyk et al. has been conducted to compare the proposed approach with other similar methods. Experimental results prove that the performance of our proposal is high in terms of computational consuming and distinguished region detection and description abilities.This work has been partially granted by the Spanish Ministerio de Ciencia e Innovación (MCINN) and FEDER funds, and by Junta de Andalucía, under projects no. TIN2008-06196 and P07-TIC-03106, respectively

Combining regular decimation and dual graph contraction for hierarchical image segmentation

The Bounded Irregular Pyramid (BIP) is a hierarchical structure for image representation whose aim is to combine concepts from regular and irregular pyramids. The data structure is a combination of the simplest regular and irregular structures: the 2 × 2/4 regular one and the simple graph representation. However, simple graphs only take into account adjacency relationships, being unable to correctly encode the topology of the image. This paper proposes a new version of the BIP, where the regular decimation process is now merged with a stochastic graph decimation strategy. Experiments demonstrate that this new irregular pyramid is able to provide qualitative good segmentation results and to preserve the topology of the input image at higher levels of its hierarchy