Discriminative Learning with Scale Decomposition for Person Detection

Person detection,which can locate the person regions in the image,continues to be a hot research topic in both computer vision and signal processing communities.However,detecting person at small scale remains a challenging problem due to the lack of discriminative details in the typical image at small scale.In this paper,we propose a decomposition mapping method which contains two subnets:encoder subnet and decoder subnet.Encoder subnet can exploit decomposition transformation for person regions from big scale to small scale.Decoder subnet reverses the process of the encoder subnet.We add deconvolution network to the decoder subnet to make up for the lost information and a discriminative mapping has been restructured to transform the person regions from the small scale to the big scale.Therefore,person-regions and background-regions can then be separated according to their decomposition positions in the new scale space.The proposed approach is evaluated on two challenging person datasets:Caltech dataset and the KITTI dataset.Compared with SAF R-CNN,the miss rate has been optimized by 3.96%on Caltech person dataset and the mean average precision has been optimized by 1.76%on KITTI person dataset.

Property A_(UB) of Metric Spaces under Decompositions of Finite Depth

Property AUB is the notion in metric geometry which has applications in higher index problems.In this paper,the permanence property of property AUB of metric spaces under large scale decompositions of finite depth is proved.

Adaptive Fault Tolerant Control of Multi-time-scale Singularly Perturbed Systems

This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. First, the full order system will be controlled using v-dependent control law. The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena. Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness. A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent. The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators. The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small. A numerical example is provided to illustrate the proposed method.

Property A and Uniform Embeddability of Metric Spaces Under Decompositions of Finite Depth

Property A and uniform embeddability are notions of metric geometry which imply the coarse Baum-Connes conjecture and the Novikov conjecture.In this paper,the authors prove the permanence properties of property A and uniform embeddability of metric spaces under large scale decompositions of finite depth.