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Displaying 1-19 out of 19 total
Incremental Kernel SVD for Face Recognition with Image Sets
Found in: Automatic Face and Gesture Recognition, IEEE International Conference on
By Tat-Jun Chin, Konrad Schindler, David Suter
Issue Date:April 2006
pp. 461-466
Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such kernel methods include Kernel PCA, Kernel DA, Kernel SVD and Kernel QR. Sinc...
 
Face Recognition From Video using Active Appearance Model Segmentation
Found in: Pattern Recognition, International Conference on
By Nathan Faggian, Andrew Paplinski, Tat-Jun Chin
Issue Date:August 2006
pp. 287-290
Face recognition from video can be improved if good face segmentation of the subject under test is achieved. Many video based face recognition rely on simple background modeling and coarse alignment strategies for segmentation. This work presents a face re...
 
Face Recognition from Video by Matching Image Sets
Found in: Digital Image Computing: Techniques and Applications
By Tat-Jun Chin, James U, Konrad Schindler, David Suter
Issue Date:December 2005
pp. 28
As opposed to still-image based paradigms, video-based face recognition involves identifying a person from a video input. In video-based approaches, face detection and tracking are performed together with recognition, as usually the background is included ...
 
Locally Linear Embedding for Markerless Human Motion Capture Using Multiple Cameras
Found in: Digital Image Computing: Techniques and Applications
By Therdsak Tangkuampien, Tat-Jun Chin
Issue Date:December 2005
pp. 72
We investigate the possibility of applying non-linear manifold learning techniques to aid in markerless human motion capturing. We hypothesize that the set of segmented binary images (in a constrained environment) of a person in all possible poses lie on a...
 
The Random Cluster Model for robust geometric fitting
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Trung Thanh Pham, Tat-Jun Chin, Jin Yu,D. Suter
Issue Date:June 2012
pp. 710-717
Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal or elemental subsets of the data, and hypothesize the geometric model from the selected subsets. While tak...
 
Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Tat-Jun Chin, Jin Yu,D. Suter
Issue Date:April 2012
pp. 625-638
Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new appr...
 
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...
 
An adversarial optimization approach to efficient outlier removal
Found in: Computer Vision, IEEE International Conference on
By Jin Yu,Anders Eriksson, Tat-Jun Chin,David Suter
Issue Date:November 2011
pp. 399-406
This paper proposes a novel adversarial optimization approach to efficient outlier removal in computer vision. We characterize the outlier removal problem as a game that involves two players of conflicting interests, namely, optimizer and outlier. Such an ...
 
Dynamic and hierarchical multi-structure geometric model fitting
Found in: Computer Vision, IEEE International Conference on
By Hoi Sim Wong,Tat-Jun Chin,Jin Yu,David Suter
Issue Date:November 2011
pp. 1044-1051
The ability to generate good model hypotheses is instrumental to accurate and robust geometric model fitting. We present a novel dynamic hypothesis generation algorithm for robust fitting of multiple structures. Underpinning our method is a fast guided sam...
 
A global optimization approach to robust multi-model fitting
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jin Yu, Tat-Jun Chin,D. Suter
Issue Date:June 2011
pp. 2041-2048
We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of geometric structures in vision data. Our objective function enforces both the fidelity of a model to the data and the similarity between its associated inliers. Departi...
 
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 ...
 
Keypoint induced distance profiles for visual recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Tat-Jun Chin, D. Suter
Issue Date:June 2009
pp. 1239-1246
We show that histograms of keypoint descriptor distances can make useful features for visual recognition. Descriptor distances are often exhaustively computed between sets of keypoints, but besides finding the k-smallest distances the structure of the dist...
 
Boosting descriptors condensed from video sequences for place recognition
Found in: Computer Vision and Pattern Recognition Workshop
By Tat-Jun Chin, Hanlin Goh, Joo-Hwee Lim
Issue Date:June 2008
pp. 1-8
We investigate the task of efficiently training classifiers to build a robust place recognition system. We advocate an approach which involves densely capturing the facades of buildings and landmarks with video recordings to greedily accumulate as much vis...
 
Out-of-Sample Extrapolation of Learned Manifolds
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Tat-Jun Chin, David Suter
Issue Date:September 2008
pp. 1547-1556
We investigate the problem of extrapolating the embedding of a manifold learned from finite samples to novel out-of-sample data. We concentrate on the manifold learning method called Maximum Variance Unfolding (MVU) for which the extrapolation problem is s...
 
Improving the Speed of Kernel PCA on Large Scale Datasets
Found in: Advanced Video and Signal Based Surveillance, IEEE Conference on
By Tat-Jun Chin, David Suter
Issue Date:November 2006
pp. 41
This paper concerns making large scale Kernel Principal Component Analysis (KPCA) feasible on regular hardware. The KPCA has been proven a useful non-linear feature extractor in several computer vision applications. The standard computation method for KPCA...
 
The Random Cluster Model for Robust Geometric Fitting
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Trung T. Pham,Tat-Jun Chin,Jin Yu,David Suter
Issue Date:August 2014
pp. 1658-1671
Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal subsets of the data, and hypothesize the geometric models from the selected subsets. While taking minimal ...
 
As-Projective-As-Possible Image Stitching with Moving DLT
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Julio Zaragoza,Tat-Jun Chin,Quoc-Huy Tran,Michael S. Brown,David Suter
Issue Date:July 2014
pp. 1-1
The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”. The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumption...
 
As-Projective-As-Possible Image Stitching with Moving DLT
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Julio Zaragoza,Tat-Jun Chin,Michael S. Brown,David Suter
Issue Date:June 2013
pp. 2339-2346
We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projecti...
 
Fast rotation search for real-time interactive point cloud registration
Found in: Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D '14)
By Álvaro Parra Bustos, David Suter, Michael S. Brown, Tat-Jun Chin
Issue Date:March 2014
pp. 55-62
Our goal is the registration of multiple 3D point clouds obtained from LIDAR scans of underground mines. Such a capability is crucial to the surveying and planning operations in mining. Often, the point clouds only partially overlap and initial alignment i...
     
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