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Displaying 1-14 out of 14 total
Adaptive Object Tracking Based on an Effective Appearance Filter
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hanzi Wang, David Suter, Konrad Schindler, Chunhua Shen
Issue Date:September 2007
pp. 1661-1667
We propose a similarity measure based on a Spatial-color Mixture of Gaussians (SMOG) appearance model for particle filters. This improves on the popular similarity measure based on color histograms because it considers not only the colors in a region but a...
 
{\cal U}Boost: Boosting with the Universum
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Chunhua Shen,Peng Wang,Fumin Shen,Hanzi Wang
Issue Date:April 2012
pp. 825-832
It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that ...
 
Robust Modular Linear Regression Based Classification for Face Recognition with Occlusion
Found in: 2013 Seventh International Conference on Image and Graphics (ICIG)
By Guanglu Liu,Yan Yan,Hanzi Wang
Issue Date:July 2013
pp. 509-514
Face recognition with occlusion is a challenging problem. Recently, the modular representation based method, i.e., modular linear regression based classification (MLRC) was proposed to deal with this problem. However, MLRC just simply combines the individu...
 
Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Xi Li,A. Dick, Chunhua Shen,A. van den Hengel, Hanzi Wang
Issue Date:April 2013
pp. 863-881
Visual tracking usually requires an object appearance model that is robust to changing illumination, pose, and other factors encountered in video. Many recent trackers utilize appearance samples in previous frames to form the bases upon which the object ap...
 
Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hanzi Wang,Tat-Jun Chin,David Suter
Issue Date:June 2012
pp. 1177-1192
We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Orde...
 
Graph mode-based contextual kernels for robust SVM tracking
Found in: Computer Vision, IEEE International Conference on
By Xi Li,Anthony Dick,Hanzi Wang,Chunhua Shen,Anton van den Hengel
Issue Date:November 2011
pp. 1156-1163
Visual tracking has been typically solved as a binary classification problem. Most existing trackers only consider the pairwise interactions between samples, and thereby ignore the higher-order contextual interactions, which may lead to the sensitivity to ...
 
Multi-structure model selection via kernel optimisation
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Tat-Jun Chin, David Suter, Hanzi Wang
Issue Date:June 2010
pp. 3586-3593
Our goal is to fit the multiple instances (or structures) of a generic model existing in data. Here we propose a novel model selection scheme to estimate the number of genuine structures present. In contrast to conventional model selection approaches, our ...
 
Rank Aggregation Based Text Feature Selection
Found in: Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
By Ou Wu, Haiqiang Zuo, Mingliang Zhu, Weiming Hu, Jun Gao, Hanzi Wang
Issue Date:September 2009
pp. 165-172
Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is...
 
A Generalized Kernel Consensus-Based Robust Estimator
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hanzi Wang, Daniel Mirota, Gregory D. Hager
Issue Date:January 2010
pp. 178-184
In this paper, we present a new Adaptive-Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANdom SAmple Consensus (RANSAC), Adaptive Scale Sample Consensus (ASSC), and Maximum ...
 
Robust motion estimation and structure recovery from endoscopic image sequences with an Adaptive Scale Kernel Consensus estimator
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Hanzi Wang, Daniel Mirota, Masaru Ishii, Gregory D. Hager
Issue Date:June 2008
pp. 1-7
To correctly estimate the camera motion parameters and reconstruct the structure of the surrounding tissues from endoscopic image sequences, we need not only to deal with outliers (e.g., mismatches), which may involve more than 50% of the data, but also to...
 
Background Subtraction Based on a Robust Consensus Method
Found in: Pattern Recognition, International Conference on
By Hanzi Wang, David Suter
Issue Date:August 2006
pp. 223-226
Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects ...
 
Efficient Visual Tracking by Probabilistic Fusion of Multiple Cues
Found in: Pattern Recognition, International Conference on
By Hanzi Wang, David Suter
Issue Date:August 2006
pp. 892-895
It has been shown that integrating multiple cues will increase the reliability and robustness of a vision system in situations that no single cue is reliable. In this paper, we propose a method by fusing multiple cues (i.e., the color cue and the edge cue)...
 
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hanzi Wang, David Suter
Issue Date:November 2004
pp. 1459-1474
Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator for the scale of the noise in the (inlier) data. Indeed, we propose two novel robust t...
 
Variable Bandwidth QMDPE and Its Application in Robust Optical Flow Estimation
Found in: Computer Vision, IEEE International Conference on
By Hanzi Wang, David Suter
Issue Date:October 2003
pp. 178
Robust estimators, such as Least Median of Squared (LMedS) Residuals, M-estimators, the Least Trimmed Squares (LTS) etc., have been employed to estimate optical flow from image sequences in recent years. However, these robust estimators have a breakdown po...
 
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