Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision.Computer vision is one of the most active research fields that lies at the intersection of dee...Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision.Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision.This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shapemoving objects while accommodating the shift and scale invariances that the object may encounter.The first part uses the Maximum Average Correlation Height(MACH)filter for object recognition and determines the bounding box coordinates.In case the correlation based MACH filter fails,the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e.,affine scale invariant feature transform(ASIFT).ASIFT is used to accommodate object shift and scale object variations.ASIFT extracts certain features from the object of interest,providing invariance in up to six affine parameters,namely translation(two parameters),zoom,rotation and two camera axis orientations.However,in this paper,only the shift and scale invariances are used.The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient(APG)technique to periodically update the coordinates of the object encapsulated in the bounding box.At the end,a comparison of the proposed algorithm with other stateof-the-art tracking algorithms has been presented,which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.展开更多
In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing ...In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.展开更多
The receptor for activated C kinase 1(RACK1)is a protein that plays a crucial role in various signaling pathways and is involved in the pathogenesis of Alzheimer's disease(AD),a prevalent neurodegenerative disease...The receptor for activated C kinase 1(RACK1)is a protein that plays a crucial role in various signaling pathways and is involved in the pathogenesis of Alzheimer's disease(AD),a prevalent neurodegenerative disease.RACK1 is highly expressed in neuronal cells of the central nervous system and regulates the pathogenesis of AD.Specifically,RACK1 is involved in regulation of the amyloid-β precursor protein processing through α-or β-secretase by binding to different protein kinase C isoforms.Additionally,RACK1 promotes synaptogenesis and synaptic plasticity by inhibiting N-methyl-D-aspartate receptors and activating gamma-aminobutyric acid A receptors,thereby preventing neuronal excitotoxicity.RACK1 also assembles inflammasomes that are involved in various neuroinflammatory pathways,such as nuclear factor-kappa B,tumor necrosis factor-alpha,and NOD-like receptor family pyrin domain-containing 3 pathways.The potential to design therapeutics that block amyloid-β accumulation and inflammation or precisely regulate synaptic plasticity represents an attractive therapeutic strategy,in which RACK1 is a potential target.In this review,we summarize the contribution of RACK1 to the pathogenesis of AD and its potential as a therapeutic target.展开更多
基金This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)and the Soonchunhyang University Research Fund.
文摘Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision.Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision.This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shapemoving objects while accommodating the shift and scale invariances that the object may encounter.The first part uses the Maximum Average Correlation Height(MACH)filter for object recognition and determines the bounding box coordinates.In case the correlation based MACH filter fails,the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e.,affine scale invariant feature transform(ASIFT).ASIFT is used to accommodate object shift and scale object variations.ASIFT extracts certain features from the object of interest,providing invariance in up to six affine parameters,namely translation(two parameters),zoom,rotation and two camera axis orientations.However,in this paper,only the shift and scale invariances are used.The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient(APG)technique to periodically update the coordinates of the object encapsulated in the bounding box.At the end,a comparison of the proposed algorithm with other stateof-the-art tracking algorithms has been presented,which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.
文摘In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density （MM-GM-PHD） filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association （IMMJPDA） filter in a relatively dense clutter environment.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82071395)the Natural Science Foundation of Chongqing(Grant Nos.cstc2021ycjh-bgzxm0186,cstc2020jcyj-zdxmX0004,and cstc2021jcyj-bsh0023)the CQMU Program for Youth Innovation in Future Medicine(Grant No.W0044).
文摘The receptor for activated C kinase 1(RACK1)is a protein that plays a crucial role in various signaling pathways and is involved in the pathogenesis of Alzheimer's disease(AD),a prevalent neurodegenerative disease.RACK1 is highly expressed in neuronal cells of the central nervous system and regulates the pathogenesis of AD.Specifically,RACK1 is involved in regulation of the amyloid-β precursor protein processing through α-or β-secretase by binding to different protein kinase C isoforms.Additionally,RACK1 promotes synaptogenesis and synaptic plasticity by inhibiting N-methyl-D-aspartate receptors and activating gamma-aminobutyric acid A receptors,thereby preventing neuronal excitotoxicity.RACK1 also assembles inflammasomes that are involved in various neuroinflammatory pathways,such as nuclear factor-kappa B,tumor necrosis factor-alpha,and NOD-like receptor family pyrin domain-containing 3 pathways.The potential to design therapeutics that block amyloid-β accumulation and inflammation or precisely regulate synaptic plasticity represents an attractive therapeutic strategy,in which RACK1 is a potential target.In this review,we summarize the contribution of RACK1 to the pathogenesis of AD and its potential as a therapeutic target.
