CHENG Li

Transduction on Directed Graphs via Absorbing Random Walks

In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.Different from existing research efforts that either only deal with undirected graphs or circumvent directionality by means of symmetrization, we propose a novel random walk approach on directed graphs using absorbing Markov chains, which can be regarded as maximizing the accumulated expected number of visits from the unlabeled transient states.

type: 
Journal Paper
journal: 
IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 99, 2017, doi:10.1109/TPAMI.2017.2730871
pubmed: 
28809671
Url: 
http://ieeexplore.ieee.org/document/8008851/
Impact Factor: 
8.329

Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration

Understanding the mechanisms of collective cell migration is crucial for cancer metastasis, wound healing and many developmental processes. Imaging a migrating cluster in vivo is feasible, but the quantification of individual cell behaviours remains challenging. We have developed an image analysis toolkit, CCMToolKit, to quantify the Drosophila border cell system. In addition to chaotic motion, previous studies reported that the migrating cells are able to migrate in a highly coordinated pattern.

type: 
Journal Paper
journal: 
Nature Communications, 2017 Apr 4;8:14905. doi: 10.1038/ncomms14905
pubmed: 
28374738
Url: 
https://www.nature.com/articles/ncomms14905
Impact Factor: 
12.124
Date of acceptance: 
2017-02-10

Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-free and More Effective

Accurate and infrastructure-free indoor positioning can be very useful in a variety of applications. However, most

type: 
Journal Paper
journal: 
IEEE Transactions on Medical Imaging, Issue 99, 2016, doi: 10.1109/TMI.2016.2623357
Impact Factor: 
3.756

Segment 2D and 3D Filaments by Learning Sructured and Contextual Features

We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this

type: 
Journal Paper
journal: 
IEEE Transactions on Medical Imaging, Issue 99, 2016, doi: 10.1109/TMI.2016.2623357
Impact Factor: 
2.536

Action Recognition in Still Images With Minimum Annotation Efforts

We focus on the problem of still image-based human action recognition, which essentially involves making prediction by analyzing human poses and their interaction with objects in the scene. Besides image-level action labels (e.g., riding, phoning), during both training and testing stages, existing works usually require additional input of human bounding boxes to facilitate the characterization of the underlying human–object interactions. We argue that this additional input requirement might severely discourage potential applications and is not very necessary.

type: 
Journal Paper
journal: 
IEEE Transactions on Image Processing, Vol. 25, Issue 11, Nov 2016, Pg 5479-5490, doi: 10.1109/TIP.2016.2605305
Url: 
http://ieeexplore.ieee.org/document/7558119/
Impact Factor: 
3.735

Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups

Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a uni ed paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and sh, as well as on human hand. On
these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial

type: 
Journal Paper
journal: 
International Journal of Computer Vision, 2016
Impact Factor: 
4.207

Pose Estimation from Line Correspondences: A Complete Analysis and A Series of Solutions

In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are threefold: (1) We provide a complete 3D configuration analysis for P3L, which includes the well-known P3P problem as well as several existing analyses as special cases.

type: 
Journal Paper
journal: 
IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 99, 2016, doi: 10.1109/TPAMI.2016.2582162
Url: 
http://ieeexplore.ieee.org/document/7494617/
Impact Factor: 
6.077

NeuronCyto II: An Automatic and Quantitative Solution for Crossover Neural Cells in High Throughput Screening

Microscopy is a fundamental technology driving new biological discoveries. Today microscopy allows a large number of images to be acquired using, for example, High Throughput Screening (HTS) and 4D imaging. It is essential to be able to interrogate these images and extract quantitative information in an automated fashion. In the context of neurobiology, it is important to automatically quantify the morphology of neurons in terms of neurite number, length, branching and complexity, etc.

type: 
Journal Paper
journal: 
Cytometry A, Vol. 89, Issue 8, Aug 2016, Pg 747-754 doi: 10.1002/cyto.a.22872
pubmed: 
27233092
Url: 
https://www.ncbi.nlm.nih.gov/pubmed/27233092
Impact Factor: 
3.066

Incremental Regularized Least Squares for Dimensionality Reduction of LargeScale Data

Over the past few decades, much attention has been drawn to large-scale incremental data analysis, where researchers are faced with huge amount of highdimensional data acquired incrementally. In such a case, conventional algorithms that compute the result from scratch whenever a new sample comes are highly inef-

type: 
Conference Paper/Poster
journal: 
SIAM Journal on Scientific Computing 2016
Impact Factor: 
1.850

Robust Modelling and Analysis of Vascular Geometries from Biomedical Images

In this paper, a robust computational framework is proposed for the modelling and analysis of vascular geometries from biomedical images. The approach consists of the segmentation of vascular geometries using an active contour model and the extraction of geometric features. A robust image feature is derived based on geometric interactions between the active contour model and the image object boundaries. The derived image feature uses voxel interactions across the image domain, and gives a coherent representation of the vessel shapes in the image.

type: 
Conference Paper/Poster
journal: 
Proceedings of the IASTED International Conference in Biomedical Engineering (BioMed) 2016, 832-024. February 2016
Url: 
http://www.actapress.com/PaperInfo.aspx?paperId=456221
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