In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robòtica i Informàtica Industrial within the project "Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)". Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated Function Described Graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.

This paper presents three tools developed within the framework of the project EDINSOST2-SDG, aimed at embedding and assessing the Education for Sustainable Development (ESD) in Engineering curricula. ESD is promoted through the introduction into engineering curricula of learning outcomes related to sustainability and, specifically, to the Sustainable Development Goals (SDG). The first tool, the “Engineering Sustainability Map”, contains ESD-related learning outcomes that any engineering student should have acquired upon completion of their studies. These learning outcomes are described according to four sustainability competencies: (1) Critical contextualization of knowledge, (2) Sustainable use of resources, (3) Participation in community processes, and (4) Application of ethical principles. The second tool, the “Sustainability Presence Map” of a degree, shows the percentage of the presence in the curriculum of each sustainability competency. The calculation of the presence of each competency is based on the effective integration of the related learning outcomes into a specific curriculum. Respective data are provided by teachers responsible for the coordination of the different subjects of the degree, collected by means of a questionnaire. The third tool presented is a questionnaire aimed at measuring the level of ESD that students perceive they have acquired through each competency. The comparison of data resulting from the Sustainability Presence Map with the data from the student questionnaire is the first step that allows the effectiveness of embedding ESD in a degree to be determined, a proper learning assessment will confirm such effectiveness. The three tools presented in this work have undergone a validation process and are currently being used in a set of engineering degrees related to the EDINSOST2-SDG project. The results of the application of these tools are part of the future research work of the authors.

It is unclear whether Hidden Markov Models (HMMs) or Dynamic Time Warping (DTW) techniques are more appropriate for gesture recognition. In this paper, we compare both methods using different criteria, with the objective of determining the one with better performance. For this purpose we have created a set of recorded gestures. The dataset used includes many samples of ten different gestures, with their corresponding ground truth obtained with a kinect. The dataset is made public for benchmarking purposes. The results show that DTW gives higher performance than HMMs, and strongly support the use of DTW.

This paper presents a tracking algorithm for automatic instrument localization in robotically assisted laparoscopic surgery. We present a simple and robust system that does not need the presence of artificial marks, or special colours to distinguish the instruments. So, the system enables the robot to track the usual instruments used in laparoscopic operations.Since the instruments are normally the most structured objects in laparoscopic scenes, the algorithm uses the Hough transform to detect straight lines in the scene. In order to distinguish among different instruments or other structured elements present in the scene, motion information is also used.We give in this paper a detailed description of all stages of the system.

ARTICLE INFO ABSTRACTIn this paper we present a new algorithm for filtering a grey-level image using as attribute the number of holes of its connected components. Our approach is based on the max-tree data structure, that makes it possible to implement an attribute filtering of the image with linear computational cost.To determine the number of holes, we present a set of diverse pixel patterns. These patterns are designed in a way that the number of holes can be computed recursively, this means that the calculations done for the components of the image can be inherited by their parent nodes of the max-tree. Since we do not need to re-calculate the attribute data for all connected components of the image, the computation time devoted to the attribute computation remains linear.