J.A. Rodrı́guez

Pyramid segmentation algorithms revisited

The main goal of this work is to compare pyramidal structures proposed to solve segmentation tasks. Segmentation algorithms based on regular and irregular pyramids are described, together with the data structures and decimation procedures which encode and manage the information in the pyramid. In order to compare the different segmentation algorithms, we have employed three types of quality measurements: the shift variance measure, the F function and the Q function.

A SURVEY OF VISION-BASED ARCHITECTURES FOR ROBOT LEARNING BY IMITATION

Learning by imitation is a natural and intuitive way to teach social robots new behaviors. While these learning systems can use different sensory inputs, vision is often their main or even their only source of input data. However, while many vision-based robot learning by imitation (RLbI) architectures have been proposed in the last decade, they may be difficult to compare due to the absence of a common, structured description. The first contribution of this survey is the definition of a set of standard components that can be used to describe any RLbI architecture. Once these components have been defined, the second contribution of the survey is an analysis of how different vision-based architectures implement and connect them. This bottom–up, structural analysis of architectures allows to compare different solutions, highlighting their main advantages and drawbacks, from a more flexible perspective than the comparison of monolithic systems.

Fast gesture recognition based on a two-level representation

Towards developing an interface for human-robot interaction, this paper proposes a two-level approach to recognise gestures which are composed of trajectories followed by different body parts. In a first level, individual trajectories are described by a set of key-points. These points are chosen as the corners of the curvature function associated to the trajectory, which will be estimated using and adaptive, non-iterative scheme. This adaptive representation allows removing noise while preserving detail in curvature at different scales. In a second level, gestures are characterised through global properties of the trajectories that compose them. Gesture recognition is performed using a confidence value that integrates both levels. Experimental results show that the performance of the proposed method is high in terms of computational cost and memory consumption, and gesture recognition ability.

Bounded irregular pyramid: a new structure for color image segmentation

This paper presents a new segmentation technique for color images. It relies on building an irregular pyramid into a regular one, presenting only nodes associated to homogeneous color regions. Hence, the size of the irregular pyramid is bounded. Segmentation is performed by rearranging the set of links among pyramid nodes. Unlike other hierarchical methods based on relinking procedures, our algorithm does not operate in an iterative way and it preserves region connectivity.

Real-time object tracking using bounded irregular pyramids

Target representation and localization is a central component in visual object tracking. In this paper a new approach for target representation and localization is presented. This approach tackles two of the most important causes of failure in object tracking: changes of object appearance and occlusions. We propose a modified template matching approach which does not require a priori learning of object views. This method allows to track non-rigid objects in real-time by employing a weighted template which is dynamically updated, and a hierarchical framework that integrates all the components of the tracker. Our hierarchical tracker allows tracking of multiple objects with low increase of computational time. The capability of the tracking system to handle occlusions and target distortions is demonstrated for several video sequences.

Real-time template-based tracking of non-rigid objects using bounded irregular pyramids

In object tracking, change of objects aspect is the most important cause of failure. This paper proposes a modified template matching approach to solve this problem without a priori learning of object views. This method permits to track non-rigid objects in real-time by employing a weighted template, which is dynamically updated, and a hierarchical framework that integrates all the components of the tracker. The capability of the tracking system to handle partial occlusions and target distortions is demonstrated for several video sequences.

Robot learning of upper-body human motion by active imitation

This paper presents a general architecture that allows a humanoid robot to imitate upper-body movements of a human demonstrator. This architecture integrates a mechanism to memorize novel behaviours executed by a human demonstrator, with a module to recognize and generate its own interpretation of already observed behaviours. Our imitator includes three biologically plausible components: i) an attention mechanism to autonomously extract relevant information from the visual input; ii) a supra-modal representation of the motion of observed body parts to map visual and motor domains; and iii) an active imitation module which involves the motor systems in the behaviour recognition process. Experimental results with a real humanoid robot demonstrate the ability of the proposed architecture to acquire novel behaviours and to recognize and reproduce previously memorized ones

A multiresolution spatiotemporal motion segmentation technique for video sequences based on pyramidal structures

This paper presents a new spatiotemporal segmentation technique for video sequences. It relies on building adaptively interlinked pyramids over consecutive frames. Pyramids are interlinked to keep a relationship between the regions in the frames. Its performance is good in real-world conditions because it does not depend on image constraints.

Color-based video segmentation using interlinked irregular pyramids

This paper presents a new segmentation technique for video sequences. It relies on building irregular pyramids based on its homogeneity over consecutive frames. Pyramids are interlinked to keep a relationship between the regions in the frames. Virtual nodes are considered to improve matching between low resolution levels of the pyramids. Its performance is good in real-world conditions because it does not depend on image constrains.

Data-Driven Multiresolution Camera Using the Foveal Adaptive Pyramid

There exist image processing applications, such as tracking or pattern recognition, that are not necessarily precise enough to maintain the same resolution across the whole image sensor. In fact, they must only keep it as high as possible in a relatively small region, but covering a wide field of view. This is the aim of foveal vision systems. Briefly, they propose to sense a large field of view at a spatially-variant resolution: one relatively small region, the fovea, is mapped at a high resolution, while the rest of the image is captured at a lower resolution. In these systems, this fovea must be moved, from one region of interest to another one, to scan a visual scene. It is interesting that the part of the scene that is covered by the fovea should not be merely spatial, but closely related to perceptual objects. Segmentation and attention are then intimately tied together: while the segmentation process is responsible for extracting perceptively-coherent entities from the scene (proto-objects), attention can guide segmentation. From this loop, the concept of foveal attention arises. This work proposes a hardware system for mapping a uniformly-sampled sensor to a space-variant one. Furthermore, this mapping is tied with a software-based, foveal attention mechanism that takes as input the stream of generated foveal images. The whole hardware/software architecture has been designed to be embedded within an all programmable system on chip (AP SoC). Our results show the flexibility of the data port for exchanging information between the mapping and attention parts of the architecture and the good performance rates of the mapping procedure. Experimental evaluation also demonstrates that the segmentation method and the attention model provide results comparable to other more computationally-expensive algorithms.