Tiny hand gesture recognition without localization via a deep convolutional network

Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually not accessible. However, the location of the hand is not a requirement when the criteria is just the recognition of a gesture to command a consumer electronics device, such as mobiles phones and TVs. In this paper, a deep convolutional neural network is proposed to directly classify hand gestures in images without any segmentation or detection stage that could discard the irrelevant not-hand areas. The designed hand-gesture recognition network can classify seven sorts of hand gestures in a user-independent manner and on real time, achieving an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds

Temporal pyramid Matching of local binary sub-patterns for hand-gesture recognition

Human–computer Interaction systems based on hand-gesture recognition are nowadays of great interest to establish a natural communication between humans and machines. However, the visual recognition of gestures and other human poses remains a challenging problem. In this paper, the original volumetric spatiograms of local binary patterns descriptor has been extended to efficiently and robustly encode the spatial and temporal information of hand gestures. This enhancement mitigates the dimensionality problems of the previous approach, and considers more temporal information to achieve a higher recognition rate. Excellent results have been obtained, outperforming other existing approaches of the state of the art

From traditional multi-stage learning To end-to-end deep learning for computer vision applications

La reaparición de las Redes Neuronales Profundas, esta vez en la era del big data, e implementadas sobre hardware de alto rendimiento que reduce el tiempo de computación, ha cambiado el paradigma del aprendizaje automático, especialmente en el campo de la visión artificial. Mientras que los sistemas tradicionales basados en aprendizaje máquina emplean múltiples etapas y características diseñadas a mano para facilitar el proceso de aprendizaje, las Redes Neuronales Convolucionales aprenden automáticamente las características que maximizan dicho proceso de extremo a extremo, es decir, desde las propias imágenes hasta la salida deseada. El propósito de esta tesis es mostrar cualitativamente la diferencia entre los sistemas multi-etapa que se basan en aprendizaje máquina tradicional y los sistemas de aprendizaje profundo de extremo a extremo, utilizando para ello diferentes aplicaciones como contexto. En primer lugar, se ha desarrollado un sistema de reconocimiento de gestos dinámicos de manos, donde dos de los aspectos clave son descriptores de imagen y video, y el diseño del sistema completo formado por múltiples etapas. Estos descriptores han sido diseñados para lidiar con las dificultades de los sistemas basados en visión, como los cambios de iluminación, las variaciones intra-clase e inter-clase y transformaciones que pueden sufrir los gestos. Las diferentes etapas del sistema resuelven pasos intermedios que son necesarios para aplicar con éxito los descriptores anteriores. Dado que el sistema propuesto de reconocimiento de gestos ha sido pensado para una interfaz hombre-máquina, este comprende etapas de detección y seguimiento para localizar el objeto de interés, y una etapa de reconocimiento para categorizar el gesto realizado. En segundo lugar, se han propuesto varios sistemas basados en aprendizaje profundo, o redes neuronales profundas, para hacer frente a las debilidades presentes en el aprendizaje tradicional. A diferencia del enfoque anterior, estos sistemas no involucran múltiples etapas, ni diseño de características. La arquitectura de estas redes depende de la tarea que se quiere resolver, de su complejidad y de la cantidad de datos disponibles. Siguiendo estas directrices, se han abordado aplicaciones más comunes como la detección de vehículos y el reconocimiento de gestos de la mano, y otras más novedosas en las que la visión puede jugar un papel importante, como las aplicaciones de robótica. ----------------------------------- Abstract -----------------------The renaissance of Deep Neural Networks in the era of big data, along with the use of highperformance hardware that reduces computational time, have changed the paradigm of machine learning, specially in the field of computer vision. Whereas systems based on traditional machine learning rely on multiple stages and hand-crafted features to get the insight of the problem, Convolutional Neural Networks automatically learn the features that maximize the learning accuracy directly from raw images in an end-to-end manner. The purpose of this dissertation is to show the gap between traditional multi-stage learning systems and end-to-end deep learning systems, addressing different applications for a qualitative comparison. First, an expert-knowledge recognition system has been developed to deal with dynamic hand gestures. The key aspects of this system are hand-crafted image and video descriptors, and also the pipeline of the whole system. These descriptors have been designed to face difficulties of visionbased approaches such as illumination changes, intra-class and inter-class variances, and multiple scales. The design of the multiple stages of the system solve intermediate steps that are necessary to successfully apply the previous descriptors. Since the proposed hand-gesture recognition system has been designed for a human-computer interface, it comprises detection and tracking stages to localize the object of interest, and a recognition stage to categorize the performed gesture. Second, DL approaches have been proposed for different computer vision applications. Research efforts have focused on building these types of end-to-end systems to face the weaknesses present in traditional learning. Unlike previous approach, they do not need multiple stages to perform the target task, nor feature engineering. Their architecture designs rely on the task to be solved, its complexity, and the available amount of data. These guidelines have been applied to common vision-based applications such vehicle detection, and hand-gesture recognition, but also to more challenging situations, such as robotics applications

Structured learning via convolutional neural networks for vehicle detection

One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset

Subcutaneous anti-COVID-19 hyperimmune immunoglobulin for prevention of disease in asymptomatic individuals with SARS-CoV-2 infection: a double-blind, placebo-controlled, randomised clinical trialResearch in context

Summary: Background: Anti-COVID-19 hyperimmune immunoglobulin (hIG) can provide standardized and controlled antibody content. Data from controlled clinical trials using hIG for the prevention or treatment of COVID-19 outpatients have not been reported. We assessed the safety and efficacy of subcutaneous anti-COVID-19 hyperimmune immunoglobulin 20% (C19-IG20%) compared to placebo in preventing development of symptomatic COVID-19 in asymptomatic individuals with SARS-CoV-2 infection. Methods: We did a multicentre, randomized, double-blind, placebo-controlled trial, in asymptomatic unvaccinated adults (≥18 years of age) with confirmed SARS-CoV-2 infection within 5 days between April 28 and December 27, 2021. Participants were randomly assigned (1:1:1) to receive a blinded subcutaneous infusion of 10 mL with 1 g or 2 g of C19-IG20%, or an equivalent volume of saline as placebo. The primary endpoint was the proportion of participants who remained asymptomatic through day 14 after infusion. Secondary endpoints included the proportion of individuals who required oxygen supplementation, any medically attended visit, hospitalisation, or ICU, and viral load reduction and viral clearance in nasopharyngeal swabs. Safety was assessed as the proportion of patients with adverse events. The trial was terminated early due to a lack of potential benefit in the target population in a planned interim analysis conducted in December 2021. ClinicalTrials.gov registry: NCT04847141. Findings: 461 individuals (mean age 39.6 years [SD 12.8]) were randomized and received the intervention within a mean of 3.1 (SD 1.27) days from a positive SARS-CoV-2 test. In the prespecified modified intention-to-treat analysis that included only participants who received a subcutaneous infusion, the primary outcome occurred in 59.9% (91/152) of participants receiving 1 g C19-IG20%, 64.7% (99/153) receiving 2 g, and 63.5% (99/156) receiving placebo (difference in proportions 1 g C19-IG20% vs. placebo, −3.6%; 95% CI -14.6% to 7.3%, p = 0.53; 2 g C19-IG20% vs placebo, 1.1%; −9.6% to 11.9%, p = 0.85). None of the secondary clinical efficacy endpoints or virological endpoints were significantly different between study groups. Adverse event rate was similar between groups, and no severe or life-threatening adverse events related to investigational product infusion were reported. Interpretation: Our findings suggested that administration of subcutaneous human hyperimmune immunoglobulin C19-IG20% to asymptomatic individuals with SARS-CoV-2 infection was safe but did not prevent development of symptomatic COVID-19. Funding: Grifols