μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.This work has been supported by grant University of Alicante projects GRE11-01 and grant Valencian Government GV/2013/005

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.This work was supported in part by the University of Alicante, Valencian Government and Spanish government under grants GRE11-01, GV/2013/005 and DPI2013-40534-R

Adjustable compression method for still JPEG images

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods

A collaborative working model for enhancing the learning process of science & engineering students

Science and engineering education are mostly based on content assimilation and development of skills. However, to adequately prepare students for today's world, it is also necessary to stimulate critical thinking and make them reflect on how to improve current practices using new tools and technologies. In this line, the main motivation of this research consists in exploring ways supported by technology to enhance the learning process of students and to better prepare them to face the challenges of today's world. To this end, the purpose of this work is to design an innovative learning project based on collaborative work among students, and research its impact in achieving better learning outcomes, generating of collective intelligence and further motivation. The proposed collaborative working model is based on peer review assessment methodology implemented through a learning web-platform. Thus, students were encouraged to peer review their classmates' works. They had to make comments, suggest improvements, and assess final assignments. Teaching staff managed and supervised the whole process. Students were selected from computer science engineering at the University of Alicante (Spain). Results suggested greater content assimilation and enhanced learning in several scientific skills. The students' final grade exceeded what any student could produce individually, but we cannot conclude that real collective intelligence was generated. Learning methodologies based on the possibilities of Information and Communication Technologies (ICT) provide new ways to transmit and manage knowledge in higher education. Collaborating in peer assessment enhances the students' motivation and promotes the active learning. In addition, this method can be very helpful and time saving for instructors in the management of large groups

Remote Web Server of Digital Signal Processors Cards and its Applications in Education

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Architecture for automatic recognition of group activities using local motions and context

Currently, the ability to automatically detect human behavior in image sequences is one of the most important challenges in the area of computer vision. Within this broad field of knowledge, the recognition of activities of people groups in public areas is receiving special attention due to its importance in many aspects including safety and security. This paper proposes a generic computer vision architecture with the ability to learn and recognize different group activities using mainly the local group’s movements. Specifically, a multi-stream deep learning architecture is proposed whose two main streams correspond to a representation based on a descriptor capable of representing the trajectory information of a sequence of images as a collection of local movements that occur in specific regions of the scene. Additional information (e.g. location, time, etc.) to strengthen the classification of activities by including it as additional streams. The proposed architecture is capable of classifying in a robust way different activities of a group as well to deal with the one-class problems. Moreover, the use of a simple descriptor that transforms a sequence of color images into a sequence of two-image streams can reduce the curse of dimensionality using a deep learning approach. The generic deep learning architecture has been evaluated with different datasets outperforming the state-of-the-art approaches providing an efficient architecture for single and multi-class classification problems

Publishing open data considering quality criteria

Los datos abiertos son considerados un mecanismo de democratización en el acceso a la información generada por organizaciones del sector público y para el desarrollo de servicios digitales generados por el sector infomediario. Sin embargo, esta tendencia ha presentado algunas barreras que van desde la calidad insuficiente de los datos publicados hasta la falta de mantenimiento de los portales donde se publican. Esta investigación realiza un análisis del estado de la cuestión en el ámbito de los datos abiertos, así como de los estándares internacionales y buenas prácticas de calidad de datos con el fin de proponer un marco de referencia que posibilite la publicación de datos abiertos con un nivel de calidad adecuado. El marco de referencia fue validado utilizando un caso de estudio mediante la metodología de investigación en acción.Open data is considered a mechanism of democratization in the access to information generated by public sector organizations and for the development of digital services generated by the infomediary sector. However, this trend has presented some barriers ranging from the insufficient quality of the data published to the lack of maintenance of the portals where they are published. This research carries out an analysis of the state of the issue in the field of open data, as well as of international standards and best practices in data quality in order to propose a frame of reference that enables the publication of open data with an adequate level of quality. The framework of reference was validated using a case study using action research methodology

La Arquitectura de Computadores en la Universidad de Alicante

Siguiendo el objetivo de estas jornadas para dar a conocer distintos métodos de trabajo dentro de la enseñanza de la Informática en las universidades españolas, presentamos el proyecto docente aplicado en la asignatura de Arquitectura de Computadores, impartida dentro del plan de estudios de Ingeniería Informática en la Universidad de Alicante, así como las necesidades que los alumnos que cursan estos estudios tienen en relación con la materia específica que se aborda

Entornos para prácticas de control y comunicaciones en asignaturas de informática industrial y domótica

En este artículo se presentan entornos para prácticas docentes en asignaturas relacionadas con las comunicaciones y el control en contextos industriales y domésticos. Estos entornos representan situaciones comunes en la sociedad de la información, donde el acceso a los servicios será posible desde cualquier lugar y en cualquier momento. Con estos requerimientos se diseña la arquitectura del sistema siendo conscientes de las necesidades didácticas y económicas de las prácticas de laboratorio. A partir de este diseño general se instancian dos paneles: industrial y domótico. Las conclusiones que se han extraído de la experiencia en las aulas están motivando el desarrollo de nuevos entornos