Limits...
Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

Shu N, Gao Z, Chen X, Liu H - PLoS ONE (2015)

Bottom Line: Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information.Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons.The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China.

ABSTRACT
Humans can easily understand other people's actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

No MeSH data available.


Raster plots obtained considering the 1400 spiking neuron cells in two different actions shown at right: walking and handclapping under condition 1 in KTH.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4489578&req=5

pone.0130569.g010: Raster plots obtained considering the 1400 spiking neuron cells in two different actions shown at right: walking and handclapping under condition 1 in KTH.

Mentions: For a spiking neuron, its mean firing rate over time is computed with the average number of spikes inside a temporal window, Eq (32) defined as:𝓣i(t,Δt)=ηi(t-Δt,t)Δt(32)where ηi(t − Δt, t) counts the number of spikes emitted by neuron i inside the glide time window Δt. Fig 9 displays the spike train of a neuron and its mean firing rate map, where Δt = 7. Fig 10 shows raster plots obtained considering the 1400 cells of a given orientation in two different actions: walking and handclapping.


Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

Shu N, Gao Z, Chen X, Liu H - PLoS ONE (2015)

Raster plots obtained considering the 1400 spiking neuron cells in two different actions shown at right: walking and handclapping under condition 1 in KTH.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489578&req=5

pone.0130569.g010: Raster plots obtained considering the 1400 spiking neuron cells in two different actions shown at right: walking and handclapping under condition 1 in KTH.
Mentions: For a spiking neuron, its mean firing rate over time is computed with the average number of spikes inside a temporal window, Eq (32) defined as:𝓣i(t,Δt)=ηi(t-Δt,t)Δt(32)where ηi(t − Δt, t) counts the number of spikes emitted by neuron i inside the glide time window Δt. Fig 9 displays the spike train of a neuron and its mean firing rate map, where Δt = 7. Fig 10 shows raster plots obtained considering the 1400 cells of a given orientation in two different actions: walking and handclapping.

Bottom Line: Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information.Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons.The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

View Article: PubMed Central - PubMed

Affiliation: School of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China.

ABSTRACT
Humans can easily understand other people's actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

No MeSH data available.