The Experts below are selected from a list of 20238 Experts worldwide ranked by ideXlab platform
Sauro Succi - One of the best experts on this subject based on the ideXlab platform.
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a fast and efficient deep learning procedure for Tracking droplet motion in dense microfluidic emulsions
Philosophical Transactions of the Royal Society A, 2021Co-Authors: Mihir Durve, Fabio Bonaccorso, Andrea Montessori, Marco Lauricella, Adriano Tiribocchi, Sauro SucciAbstract:We present a deep learning-based Object detection and Object Tracking Algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict th...
Mihir Durve - One of the best experts on this subject based on the ideXlab platform.
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a fast and efficient deep learning procedure for Tracking droplet motion in dense microfluidic emulsions
Philosophical Transactions of the Royal Society A, 2021Co-Authors: Mihir Durve, Fabio Bonaccorso, Andrea Montessori, Marco Lauricella, Adriano Tiribocchi, Sauro SucciAbstract:We present a deep learning-based Object detection and Object Tracking Algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict th...
Fabio Bonaccorso - One of the best experts on this subject based on the ideXlab platform.
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a fast and efficient deep learning procedure for Tracking droplet motion in dense microfluidic emulsions
Philosophical Transactions of the Royal Society A, 2021Co-Authors: Mihir Durve, Fabio Bonaccorso, Andrea Montessori, Marco Lauricella, Adriano Tiribocchi, Sauro SucciAbstract:We present a deep learning-based Object detection and Object Tracking Algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict th...
Bradley J. Nelson - One of the best experts on this subject based on the ideXlab platform.
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a deformable Object Tracking Algorithm based on the boundary element method that is robust to occlusions and spurious edges
International Journal of Computer Vision, 2008Co-Authors: Michael A. Greminger, Bradley J. NelsonAbstract:The manipulation of deformable Objects is an important problem in robotics and arises in many applications including biomanipulation, microassembly, and robotic surgery. For some applications, the robotic manipulator itself may be deformable. Vision-based deformable Object Tracking can provide feedback for these applications. Computer vision is a logical sensing choice for Tracking deformable Objects because the large amount of data that is collected by a vision system allows many points within the deformable Object to be tracked simultaneously. This article introduces a template based deformable Object Tracking Algorithm, based on the boundary element method, that is able to track a wide range of deformable Objects. The robustness of this Algorithm to occlusions and to spurious edges in the source image is also demonstrated. A robust error measure is used to handle the problem of occlusion and an improved edge detector based on the Canny edge operator is used to suppress spurious edges. This article concludes by quantifying the performance increase provided by the robust error measure and the robust edge detector. The performance of the Algorithm is also demonstrated through the Tracking of a sequence of cardiac MRI images.
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boundary element deformable Object Tracking with equilibrium constraints
International Conference on Robotics and Automation, 2004Co-Authors: Michael A. Greminger, Bradley J. NelsonAbstract:This paper presents a deformable Object Tracking Algorithm based on the boundary element method (BEM). BEM differs from the finite element method (FEM) in that only the boundary of the Object needs to be meshed for BEM. FEM requires that the interior of the Object is meshed in addition to its boundary. This feature of BEM makes it attractive for computer vision problems. We present a deformable template that uses BEM to model deformations. This deformable template is registered to an image using an energy minimization approach. The BEM Tracking Algorithm presented in this paper constraints the Tracking results to satisfy the condition of static equilibrium. This increases the robustness of the Tracking results and enhances the usefulness of the forces obtained from the Tracking procedure. We demonstrate the Tracking performance of this Algorithm for Objects with linear and non-linear elastic properties. In addition, the results of Tracking the deformations of a cell are presented.
Minghsuan Yang - One of the best experts on this subject based on the ideXlab platform.
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robust Object Tracking via sparse collaborative appearance model
IEEE Transactions on Image Processing, 2014Co-Authors: Wei Zhong, Minghsuan YangAbstract:In this paper, we propose a robust Object Tracking Algorithm based on a sparse collaborative model that exploits both holistic templates and local representations to account for drastic appearance changes. Within the proposed collaborative appearance model, we develop a sparse discriminative classifier (SDC) and sparse generative model (SGM) for Object Tracking. In the SDC module, we present a classifier that separates the foreground Object from the background based on holistic templates. In the SGM module, we propose a histogram-based method that takes the spatial information of each local patch into consideration. The update scheme considers both the most recent observations and original templates, thereby enabling the proposed Algorithm to deal with appearance changes effectively and alleviate the Tracking drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art Algorithms.
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Object Tracking via partial least squares analysis
IEEE Transactions on Image Processing, 2012Co-Authors: Qing Wang, Feng Chen, Wenli Xu, Minghsuan YangAbstract:We propose an Object Tracking Algorithm that learns a set of appearance models for adaptive discriminative Object representation. In this paper, Object Tracking is posed as a binary classification problem in which the correlation of Object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As Object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust Tracking. The proposed Algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the Tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed Tracking Algorithm.
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robust Object Tracking via sparsity based collaborative model
Computer Vision and Pattern Recognition, 2012Co-Authors: Wei Zhong, Huchuan Lu, Minghsuan YangAbstract:In this paper we propose a robust Object Tracking Algorithm using a collaborative model. As the main challenge for Object Tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art Algorithms.
