Search Results for author: Ba-Tuong Vo

Found 9 papers, 1 papers with code

Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering

no code implementations29 Nov 2022 Changbeom Shim, Ba-Tuong Vo, Ba-Ngu Vo, Jonah Ong, Diluka Moratuwage

Generalized Labeled Multi-Bernoulli (GLMB) densities arise in a host of multi-object system applications analogous to Gaussians in single-object filtering.

Tracking Cells and their Lineages via Labeled Random Finite Sets

1 code implementation22 Apr 2021 Tran Thien Dat Nguyen, Ba-Ngu Vo, Ba-Tuong Vo, Du Yong Kim, Yu Suk Choi

Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide.

How Trustworthy are Performance Evaluations for Basic Vision Tasks?

no code implementations8 Aug 2020 Tran Thien Dat Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Ba-Tuong Vo, Silvio Savarese, Ian Reid

This paper examines performance evaluation criteria for basic vision tasks involving sets of objects namely, object detection, instance-level segmentation and multi-object tracking.

Multi-Object Tracking object-detection +1

Model-Based Multiple Instance Learning

no code implementations7 Mar 2017 Ba-Ngu Vo, Dinh Phung, Quang N. Tran, Ba-Tuong Vo

While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning.

Clustering Decision Making +3

Clustering For Point Pattern Data

no code implementations8 Feb 2017 Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo

However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources.


Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering

no code implementations18 Nov 2016 Du Yong Kim, Ba-Ngu Vo, Ba-Tuong Vo

Furthermore the labeled random finite set framework enables the incorporation of prior knowledge that mis-detections of long tracks which occur in the middle of the scene are likely to be due to occlusions.

Management Multi-Object Tracking +1

A Generalized Labeled Multi-Bernoulli Filter Implementation using Gibbs Sampling

no code implementations2 Jun 2015 Hung Gia Hoang, Ba-Tuong Vo, Ba-Ngu Vo

This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step.

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