A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.

Enriching the integration of semantic resources based on WordNet

In this paper we present the enrichment of the Integration of Semantic Resources based in WordNet (ISR-WN Enriched). This new proposal improves the previous one where several semantic resources such as SUMO, WordNet Domains and WordNet Affects were related, adding other semantic resources such as Semantic Classes and SentiWordNet. Firstly, the paper describes the architecture of this proposal explaining the particularities of each integrated resource. After that, we analyze some problems related to the mappings of different versions and how we solve them. Moreover, we show the advantages that this kind of tool can provide to different applications of Natural Language Processing. Related to that question, we can demonstrate that the integration of semantic resources allows acquiring a multidimensional vision in the analysis of natural language.

3D model based map building

Comunicación presentada en el IX Workshop de Agentes Físicos (WAF'2008), Vigo, 11-12 septiembre 2008.

Proinsulin slows retinal degeneration and vision loss in the P23H rat model of retinitis pigmentosa

Proinsulin has been characterized as a neuroprotective molecule. In this work we assess the therapeutic potential of proinsulin on photoreceptor degeneration, synaptic connectivity, and functional activity of the retina in the transgenic P23H rat, an animal model of autosomal dominant retinitis pigmentosa (RP). P23H homozygous rats received an intramuscular injection of an adeno-associated viral vector serotype 1 (AAV1) expressing human proinsulin (hPi+) or AAV1-null vector (hPi−) at P20. Levels of hPi in serum were determined by enzyme-linked immunosorbent assay (ELISA), and visual function was evaluated by electroretinographic (ERG) recording at P30, P60, P90, and P120. Preservation of retinal structure was assessed by immunohistochemistry at P120. Human proinsulin was detected in serum from rats injected with hPi+ at all times tested, with average hPi levels ranging from 1.1 nM (P30) to 1.4 nM (P120). ERG recordings showed an amelioration of vision loss in hPi+ animals. The scotopic b-waves were significantly higher in hPi+ animals than in control rats at P90 and P120. This attenuation of visual deterioration correlated with a delay in photoreceptor degeneration and the preservation of retinal cytoarchitecture. hPi+ animals had 48.7% more photoreceptors than control animals. Presynaptic and postsynaptic elements, as well as the synaptic contacts between photoreceptors and bipolar or horizontal cells, were preserved in hPi+ P23H rats. Furthermore, in hPi+ rat retinas the number of rod bipolar cell bodies was greater than in control rats. Our data demonstrate that hPi expression preserves cone and rod structure and function, together with their contacts with postsynaptic neurons, in the P23H rat. These data strongly support the further development of proinsulin-based therapy to counteract retinitis pigmentosa.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

Screening Subclinical Keratoconus With Placido-Based Corneal Indices

Purpose. To assess in a sample of normal, keratoconic, and keratoconus (KC) suspect eyes the performance of a set of new topographic indices computed directly from the digitized images of the Placido rings. Methods. This comparative study was composed of a total of 124 eyes of 106 patients from the ophthalmic clinics Vissum Alicante and Vissum Almería (Spain) divided into three groups: control group (50 eyes), KC group (50 eyes), and KC suspect group (24 eyes). In all cases, a comprehensive examination was performed, including the corneal topography with a Placidobased CSO topography system. Clinical outcomes were compared among groups, along with the discriminating performance of the proposed irregularity indices. Results. Significant differences at level 0.05 were found on the values of the indices among groups by means of Mann-Whitney-Wilcoxon nonparametric test and Fisher exact test. Additional statistical methods, such as receiver operating characteristic analysis and K-fold cross validation, confirmed the capability of the indices to discriminate between the three groups. Conclusions. Direct analysis of the digitized images of the Placido mires projected on the cornea is a valid and effective tool for detection of corneal irregularities. Although based only on the data from the anterior surface of the cornea, the new indices performed well even when applied to the KC suspect eyes. They have the advantage of simplicity of calculation combined with high sensitivity in corneal irregularity detection and thus can be used as supplementary criteria for diagnosing and grading KC that can be added to the current keratometric classifications.

A Performance Evaluation of Surface Normals-Based Descriptors for Recognition of Objects using CAD-Models

This paper describes a study and analysis of surface normal-base descriptors for 3D object recognition. Specifically, we evaluate the behaviour of descriptors in the recognition process using virtual models of objects created from CAD software. Later, we test them in real scenes using synthetic objects created with a 3D printer from the virtual models. In both cases, the same virtual models are used on the matching process to find similarity. The difference between both experiments is in the type of views used in the tests. Our analysis evaluates three subjects: the effectiveness of 3D descriptors depending on the viewpoint of camera, the geometry complexity of the model and the runtime used to do the recognition process and the success rate to recognize a view of object among the models saved in the database.

Active Visual Features Based on Events to Guide Robot Manipulators in Tracking Tasks

Traditional visual servoing systems do not deal with the topic of moving objects tracking. When these systems are employed to track a moving object, depending on the object velocity, visual features can go out of the image, causing the fail of the tracking task. This occurs specially when the object and the robot are both stopped and then the object starts the movement. In this work, we have employed a retina camera based on Address Event Representation (AER) in order to use events as input in the visual servoing system. The events launched by the camera indicate a pixel movement. Event visual information is processed only at the moment it occurs, reducing the response time of visual servoing systems when they are used to track moving objects.

A reliable and valid questionnaire was developed to measure computer vision syndrome at the workplace

Objectives: To design and validate a questionnaire to measure visual symptoms related to exposure to computers in the workplace. Study Design and Setting: Our computer vision syndrome questionnaire (CVS-Q) was based on a literature review and validated through discussion with experts and performance of a pretest, pilot test, and retest. Content validity was evaluated by occupational health, optometry, and ophthalmology experts. Rasch analysis was used in the psychometric evaluation of the questionnaire. Criterion validity was determined by calculating the sensitivity and specificity, receiver operator characteristic curve, and cutoff point. Testeretest repeatability was tested using the intraclass correlation coefficient (ICC) and concordance by Cohen’s kappa (k). Results: The CVS-Q was developed with wide consensus among experts and was well accepted by the target group. It assesses the frequency and intensity of 16 symptoms using a single rating scale (symptom severity) that fits the Rasch rating scale model well. The questionnaire has sensitivity and specificity over 70% and achieved good testeretest repeatability both for the scores obtained [ICC 5 0.802; 95% confidence interval (CI): 0.673, 0.884] and CVS classification (k 5 0.612; 95% CI: 0.384, 0.839). Conclusion: The CVS-Q has acceptable psychometric properties, making it a valid and reliable tool to control the visual health of computer workers, and can potentially be used in clinical trials and outcome research.

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 three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.