Bias reduction for stereo based motion estimation with applications to large scale visual odometry

2009 ◽  
Author(s):  
Gijs Dubbelman ◽  
Frans C.A. Groen
Keyword(s):  
Motion Estimation ◽  
Large Scale ◽  
Visual Odometry ◽  
Bias Reduction

scholarly journals sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network

2021 ◽  
Vol 11 (10) ◽  
pp. 4678
Author(s):  
Chao Chen ◽  
Weiyu Guo ◽  
Chenfei Ma ◽  
Yongkui Yang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Control ◽  
Large Scale ◽  
Human Motion ◽  
Kernel Size ◽  
Continuous Motion ◽  
Convolutional Network ◽  
Continuous Estimation ◽  
Discrete Motion ◽  
Precision Rate

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel’s size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN’s problem: that it is difficult to fully extract the sEMG’s temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%.

Visual Odometry Based on 3D-3D and 3D-2D Motion Estimation Method

2018 ◽  
Author(s):  
Kai Cao ◽  
Xuemeng Yang ◽  
Song Gao ◽  
Chaobo Chen ◽  
Jiaoru Huang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Estimation Method ◽  
Visual Odometry

scholarly journals Study retention as bias reduction in a hard-to-reach population

2016 ◽  
Vol 113 (20) ◽  
pp. 5477-5485 ◽  
Author(s):  
Bruce Western ◽  
Anthony Braga ◽  
David Hureau ◽  
Catherine Sirois
Keyword(s):  
Large Scale ◽  
Bias Reduction ◽  
Study Participation ◽  
Marginal Populations ◽  
Yield Data ◽  
Social Insecurity ◽  
Hard To Reach Populations ◽  
Data Collections ◽  
Information Change

Collecting data from hard-to-reach populations is a key challenge for research on poverty and other forms of extreme disadvantage. With data from the Boston Reentry Study (BRS), we document the extreme marginality of released prisoners and the related difficulties of study retention and analysis. Analysis of the BRS data yields three findings. First, released prisoners show high levels of “contact insecurity,” correlated with social insecurity, in which residential addresses and contact information change frequently. Second, strategies for data collection are available to sustain very high rates of study participation. Third, survey nonresponse in highly marginal populations is strongly nonignorable, closely related to social and economic vulnerability. The BRS response rate of 94% over a 1-y follow-up period allows analysis of hypothetically high nonresponse rates. In this setting, nonresponse attenuates regression estimates in analyses of housing insecurity, drug use, and unemployment. These results suggest that in the analysis of very poor and disadvantaged populations, methods that maximize study participation reduce bias and yield data that can usefully supplement large-scale household or administrative data collections.

scholarly journals FIREWORKS: a bottom-up approach to integrative coessentiality network analysis

2020 ◽  
Vol 4 (2) ◽  
pp. e202000882
Author(s):  
David R Amici ◽  
Jasen M Jackson ◽  
Mihai I Truica ◽  
Roger S Smith ◽  
Sarki A Abdulkadir ◽  
...  
Keyword(s):  
Large Scale ◽  
Gene Interaction ◽  
Bias Reduction ◽  
Functional Relationships ◽  
Accessibility Barriers ◽  
Common Effect ◽  
Human Genes ◽  
Case Vignettes ◽  
Systematic Biases ◽  
Context Specific

Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug–gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of “undruggable” proteins, and context-specific rewiring of genetic networks.

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