Period Estimation in Astronomical Time Series Using Slotted Correntropy

In this letter, we propose a method for period estimation in light curves from periodic variable stars using correntropy. Light curves are astronomical time series of stellar brightness over time, and are characterized as being noisy and unevenly sampled. We propose to use slotted time lags in order to estimate correntropy directly from irregularly sampled time series. A new information theoretic metric is proposed for discriminating among the peaks of the correntropy spectral density. The slotted correntropy method outperformed slotted correlation, string length, VarTools (Lomb-Scargle periodogram and Analysis of Variance), and SigSpec applications on a set of light curves drawn from the MACHO survey

Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks

Recognising human actions in untrimmed videos is an important challenging task. An effective three-dimensional (3D) motion representation and a powerful learning model are two key factors influencing recognition performance. In this study, the authors introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process. By normalising the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the colour-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. They then design and train different deep convolutional neural networks based on the residual network architecture on the obtained image-based representations to learn 3D motion features and classify them into classes. Their proposed method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches while requiring less computation for training and prediction

Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks

This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP)We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, how to construct a robust representation that easily captures the spatial-temporal evolutions of motions from skeleton sequences. Second, how to design D-CNNs capable of learning discriminative features from the new representation in a effective manner. To address these tasks, a skeleton-based representation, namely, SPMF (Skeleton Pose-Motion Feature) is proposed. The SPMFs are built from two of the most important properties of a human action: postures and their motions. Therefore, they are able to effectively represent complex actions. For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs. Our method is evaluated on two challenging datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated that the proposed method surpasses state-of-the-art methods whilst requiring less computation

Video-based human action recognition using deep learning: a review

Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this led.This work was supported by the Cen-tre d'Etudes et d'Expertise sur les Risques, l'environnement la mobilité et l'aménagement (CEREMA) and the UC3M Conex-Marie Curie Program.No publicad

Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

This paper has been presented at 8th International Conference of Pattern Recognition Systems (ICPRS 2017).Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a lot of progresses, recognizing actions in a unknown video is still a challenging task in computer vision. Recently, deep learning algorithms has proved its great potential in many vision-related recognition tasks. In this paper, we propose the use of Deep Residual Neural Networks (ResNets) to learn and recognize human action from skeleton data provided by Kinect sensor. Firstly, the body joint coordinates are transformed into 3D-arrays and saved in RGB images space. Five different deep learning models based on ResNet have been designed to extract image features and classify them into classes. Experiments are conducted on two public video datasets for human action recognition containing various challenges. The results show that our method achieves the state-of-the-art performance comparing with existing approachesThis work was supported by the Cerema Research Center and Universidad Carlos III de Madrid. Sergio A. Velastin has received funding from the European Unions Seventh Framework Programme for Research, Technological Development and demonstration under grant agreement No 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Exploiting deep residual networks for human action recognition from skeletal data

The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset

Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks

Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeletonbased representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.This research was carried out at the Cerema Research Center (CEREMA) and Toulouse Institute of Computer Science Research (IRIT), Toulouse, France. Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for Research, Technological Development and demonstration under grant agreement N. 600371, el Ministerio de Economia, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Gelatin-methacryloyl hydrogels containing turnip mosaic virus for fabrication of nanostructured materials for tissue engineering

16 Pág.Current tissue engineering techniques frequently rely on hydrogels to support cell growth, as these materials strongly mimic the extracellular matrix. However, hydrogels often need ad hoc customization to generate specific tissue constructs. One popular strategy for hydrogel functionalization is to add nanoparticles to them. Here, we present a plant viral nanoparticle the turnip mosaic virus (TuMV), as a promising additive for gelatin methacryloyl (GelMA) hydrogels for the engineering of mammalian tissues. TuMV is a flexuous, elongated, tubular protein nanoparticle (700-750 nm long and 12-15 nm wide) and is incapable of infecting mammalian cells. These flexuous nanoparticles spontaneously form entangled nanomeshes in aqueous environments, and we hypothesized that this nanomesh structure could serve as a nanoscaffold for cells. Human fibroblasts loaded into GelMA-TuMV hydrogels exhibited similar metabolic activity to that of cells loaded in pristine GelMA hydrogels. However, cells cultured in GelMA-TuMV formed clusters and assumed an elongated morphology in contrast to the homogeneous and confluent cultures seen on GelMA surfaces, suggesting that the nanoscaffold material per se did not favor cell adhesion. We also covalently conjugated TuMV particles with epidermal growth factor (EGF) using a straightforward reaction scheme based on a Staudinger reaction. BJ cells cultured on the functionalized scaffolds increased their confluency by approximately 30% compared to growth with unconjugated EGF. We also provide examples of the use of GelMA-TuMV hydrogels in different biofabrication scenarios, include casting, flow-based-manufacture of filaments, and bioprinting. We envision TuMV as a versatile nanobiomaterial that can be useful for tissue engineering.EV-L, AIF-S, MJ-LZ, and JAT-N acknowledge funding from scholarships provided by CONACyT (Consejo Nacional de Ciencia y Tecnología, México). EV-L acknowledges the Nuevo Leon Institute for Innovation and Technology Transference for a PhD student grant (No. 459134, CVU 360539). GT-dS and MMA acknowledge the institutional funding received from Tecnológico de Monterrey (Grant 002EICIS01). MMA, GT-dS, and IG-G acknowledge funding provided by CONACyT (Consejo Nacional de Ciencia y Tecnología, México) through several grants (SNI 26048, SNI 256730, and SNI 313028). FP acknowledge the funding received from RTA 2015-00017 from INIA and EU Arimnet-2 Grant Agreement No. 618127. The CBGP was granted “Severo Ochoa” Distinctions of Excellence by the Spanish Ministry of Science and Innovation (SEV-2016-0672 and CEX 2020-000999-S)Peer reviewe