A new framework of human interaction recognition based on multiple stage probability fusion

Visual-based human interactive behavior recognition is a challenging research topic in computer vision. There exist some important problems in the current interaction recognition algorithms, such as very complex feature representation and inaccurate feature extraction induced by wrong human body segmentation. In order to solve these problems, a novel human interaction recognition method based on multiple stage probability fusion is proposed in this paper. According to the human body’s contact in interaction as a cut-off point, the process of the interaction can be divided into three stages: start stage, execution stage and end stage. Two persons’ motions are respectively extracted and recognizes in the start stage and the finish stage when there is no contact between those persons. The two persons’ motion is extracted as a whole and recognized in the execution stage. In the recognition process, the final recognition results are obtained by the weighted fusing these probabilities in different stages. The proposed method not only simplifies the extraction and representation of features, but also avoids the wrong feature extraction caused by occlusion. Experiment results on the UT-interaction dataset demonstrated that the proposed method results in a better performance than other recent interaction recognition methods

A new framework of human interaction recognition based on multiple stage probability fusion

Visual-based human interactive behavior recognition is a challenging research topic in computer vision. There exist some important problems in the current interaction recognition algorithms, such as very complex feature representation and inaccurate feature extraction induced by wrong human body segmentation. In order to solve these problems, a novel human interaction recognition method based on multiple stage probability fusion is proposed in this paper. According to the human body’s contact in interaction as a cut-off point, the process of the interaction can be divided into three stages: start stage, execution stage and end stage. Two persons’ motions are respectively extracted and recognizes in the start stage and the finish stage when there is no contact between those persons. The two persons’ motion is extracted as a whole and recognized in the execution stage. In the recognition process, the final recognition results are obtained by the weighted fusing these probabilities in different stages. The proposed method not only simplifies the extraction and representation of features, but also avoids the wrong feature extraction caused by occlusion. Experiment results on the UT-interaction dataset demonstrated that the proposed method results in a better performance than other recent interaction recognition methods

The progenitors of type Ia supernovae in the semidetached binaries with red giant donors

Context. The companions of the exploding carbon-oxygen white dwarfs (CO WDs) for producing type Ia supernovae (SNe Ia) are still not conclusively confirmed. A red-giant (RG) star has been suggested to be the mass donor of the exploding WD, named as the symbiotic channel. However, previous studies on the this channel gave a relatively low rate of SNe Ia. Aims. We aim to systematically investigate the parameter space, Galactic rates and delay time distributions of SNe Ia from the symbiotic channel by employing a revised mass-transfer prescription. Methods. We adopted an integrated mass-transfer prescription to calculate the mass-transfer process from a RG star onto the WD. In this prescription, the mass-transfer rate varies with the local material states. Results. We evolved a large number of WD+RG systems, and found that the parameter space of WD+RG systems for producing SNe Ia is significantly enlarged. This channel could produce SNe Ia with intermediate and old ages, contributing to at most 5% of all SNe Ia in the Galaxy. Our model increases the SN Ia rate from this channel by a factor of 5. We suggest that the symbiotic systems RS Oph and T CrB are strong candidates for the progenitors of SNe Ia.Comment: 8 pages, 6 figure

Screening of alginate lyase-excreting microorganisms from the surface of brown algae

Additional file 1: Figure S1. Gram staining of the 12 alginate lyase-excreting bacterial strains

Synergic effect within n-type inorganic–p-type organic nano-hybrids in gas sensors

This paper describes the exploration of a synergic effect within n-type inorganic–p-type organic nanohybrids in gas sensors. One-dimensional (1D) n-type SnO2–p-type PPy composite nanofibers were prepared by combining the electrospinning and polymerization techniques, and taken as models to explore the synergic effect during the sensing measurement. Outstanding sensing performances, such as large responses and low detection limits (20 ppb for ammonia) were obtained. A plausible mechanism for the synergic effect was established by introducing p–n junction theory to the systems. Moreover, interfacial metal (Ag) nanoparticles were introduced into the n-type SnO2–p-type PPy nano-hybrids to further supplement and verify our theory. The generality of this mechanism was further verified using TiO2–PPy and TiO2–Au–PPy nano-hybrids. We believe that our results can construct a powerful platform to better understand the relationship between the microstructures and their gas sensing performances