Publications

[1] Andrew Du, Bo Chen, Tat-Jun Chin, Yee Wei Law, Michele Sasdelli, Ramesh Rajasegaran, and Dillon Campbell. Physical adversarial attacks on an aerial imagery object detector. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2022. [ bib | pdf ]
[2] Bo Chen, Tat-Jun Chin, and Marius Klimavicius. Occlusion-invariant object pose estimation with holistic representation. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2022. [ bib | pdf ]
[3] Ragav Sachdeva, Ravi Hammond, James Bockman, Alec Arthur, Brandon Smart, Dustin Craggs, Anh-Dzung Doan, Thomas Rowntree, Elijah Schutz, Adrian Orenstein, Andy Yu, Tat-Jun Chin, and Ian Reid. Autonomy and perception for space mining. In International Conference on Robotics and Automation (ICRA), 2022. [ bib | pdf ]
[4] Daqi Liu, Alvaro Parra, Yasir Latif, Bo Chen, Tat-Jun Chin, and Ian Reid. Asynchronous optimisation for event-based visual odometry. In International Conference on Robotics and Automation (ICRA), 2022. [ bib | pdf ]
[5] Anh-Dzung Doan, Michele Sasdelli, David Suter, and Tat-Jun Chin. A hybrid quantum-classical algorithm for robust fitting. In Computer Vision and Pattern Recognition (CVPR), 2022. [ bib | pdf ]
[6] Erchuan Zhang, Ruwan Tennakoon, David Suter, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, and Syed Zulqarnain Gilani. Maximum consensus by weighted influences of monotone boolean functions. In Computer Vision and Pattern Recognition (CVPR)], 2022. [ bib | pdf ]
[7] Huu Le, Tat-Jun Chin, Anders Eriksson, Thanh-Toan Do, and David Suter. Deterministic approximate methods for maximum consensus robust fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(3):842--857, 2021. [ bib | pdf ]
[8] Ang-Dzung Doan, Yasir Latif, Tat-Jun Chin, and Ian Reid. HM4: Hidden Markov model with memory management for visual place recognition. IEEE Robotics and Automation Letters, 6(1):167--174, 2021. [ bib | pdf | project page ]
[9] Anders Eriksson, Carl Olsson, Fredric Kahl, and Tat-Jun Chin. Rotation averaging with the chordal distance: global minimizers and strong duality. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(1):256--268, 2021. [ bib | DOI ]
[10] Daqi Liu, Álvaro Parra, and Tat-Jun Chin. Spatiotemporal registration for event-based visual odometry. In Computer Vision and Pattern Recognition (CVPR), 2021. [ bib | pdf | project page ]
[11] Álvaro Parra, Shin-Fang Chng, Tat-Jun Chin, Anders Eriksson, and Ian Reid. Rotation coordinate descend for fast globally optimal rotation averaging. In Computer Vision and Pattern Recognition (CVPR), 2021. [ bib | pdf | project page ]
[12] Ruwan Tennakoon, David Suter, Erchuan Zhang, Tat-Jun Chin, and Alireza Bab-hadiashar. Consensus maximisation using influences of monotone boolean functions. In Computer Vision and Pattern Recognition (CVPR), 2021. [ bib | pdf ]
[13] Anh-Dzung Doan, Daniyar Turmukhambetov, Yasir Latif, Tat-Jun Chin, and Soohyun Bae. Learning to predict repeatability of interest points. In International Conference on Robotics and Automation (ICRA), 2021. [ bib ]
[14] Bo Chen, Daqi Liu, Tat-Jun Chin, Mark Rutten, Dawa Derksen, Marcus Märtens, Moritz von Looz, Gurvan Lecuyer, and Dario Izzo. Spot the GEO satellites: From dataset to Kelvins SpotGEO Challenge. In CVPR Workshop (CVPRW) on AI4Space, 2021. [ bib | pdf ]
[15] Dung Anh Hoang, Bo Chen, and Tat-Jun Chin. A spacecraft dataset for detection, segmentation and parts recognition. In CVPR Workshop (CVPRW) on AI4Space, 2021. [ bib | pdf ]
[16] Michele Sasdelli and Tat-Jun Chin. Quantum annealing formulation for binary neural networks. In Digital Image Computing: Techniques and Applications (DICTA), 2021. [ bib | award | pdf ]
[17] Samya Bagchi and Tat-Jun Chin. Event-based star tracking via multiresolution progressive Hough Transforms. In Winter Conference on Applications of Computer Vision (WACV), 2020. [ bib | pdf ]
[18] Daqi Liu, Bo Chen, Tat-Jun Chin, and Mark Rutten. Topological sweep for multi-target detection of geostationary space objects. IEEE Transactions on Signal Processing, 68:5166--5177, 2020. [ bib | pdf ]
[19] Ang-Dzung Doan, Yasir Latif, Tat-Jun Chin, Shin-Fang Ch'ng, Thanh-Toan Do, and Ian Reid. Visual localization under appearance change: Filtering approaches. Neural Computing and Applications, 2020. [ bib | pdf | project page ]
[20] Tat-Jun Chin, Zhipeng Cai, and Frank Neumann. Robust fitting in computer vision: Easy or hard? International Journal of Computer Vision (IJCV), 128:575--587, 2020. [ bib | DOI ]
[21] Tat-Jun Chin, David Suter, Shin-Fang Ch'ng, and James Quach. Quantum robust fitting. In Asian Conference on Computer Vision (ACCV), 2020. [ bib | pdf ]
[22] Bo Chen, Álvaro Parra, Jiewei Cao, Nan Li, and Tat-Jun Chin. End-to-end learnable geometric vision by backpropagating PnP optimization. In Computer Vision and Pattern Recognition (CVPR), 2020. [ bib | pdf | project page ]
[23] Shin-Fang Ch'ng, Naoya Sogi, Pulak Purkait, Tat-Jun Chin, and Kazuhiro Fukui. Resolving marker pose ambiguity by robust rotation averaging with clique constraints. In International Conference on Robotics and Automation (ICRA), 2020. [ bib | pdf ]
[24] Yasir Latif, Ang-Dzung Doan, Tat-Jun Chin, and Ian Reid. SPRINT: Subgraph place recognition for intelligent transportation. In International Conference on Robotics and Automation (ICRA), 2020. [ bib | DOI ]
[25] Pulak Purkait, Tat-Jun Chin, and Ian Reid. NeuRoRA: Neural robust rotation averaging. In European Conference on Computer Vision (ECCV), 2020. [ bib | pdf ]
[26] Daqi Liu, Álvaro Parra, and Tat-Jun Chin. Globally optimal contrast maximisation for event-based motion estimation. In Computer Vision and Pattern Recognition (CVPR), 2020. [ bib | pdf | project page ]
[27] Álvaro Parra, Tat-Jun Chin, Anders Eriksson, and Ian Reid. Visual SLAM: Why bundle adjust? In International Conference on Robotics and Automation (ICRA), 2019. [ bib | pdf | project page ]
[28] Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin, Yu Liu, Thanh-Toan Do, and Ian Reid. Scalable place recognition under appearance change for autonomous driving. In International Conference on Computer Vision (ICCV), 2019. [ bib | pdf | project page ]
[29] Tat-Jun Chin, Samya Bagchi, Anders Eriksson, and Andre van Schaik. Star tracking using an event camera. In CVPR Workshop (CVPRW) on Event-based Vision and Smart Cameras, 2019. [ bib | award | pdf | project page ]
[30] Bo Chen, Jiewei Cao, Álvaro Parra Bustos, and Tat-Jun Chin. Satellite pose estimation with deep landmark regression and nonlinear pose refinement. In ICCV Workshop (ICCVW) on Recovering 6D Object Pose, 2019. [ bib | pdf | project page ]
[31] Zhipeng Cai, Tat-Jun Chin, and Vladlen Koltun. Consensus maximization tree search revisited. In International Conference on Computer Vision (ICCV), 2019. [ bib | pdf ]
[32] Huan Do, Tat-Jun Chin, Nick Moretti, Moriba K. Jah, and Matthew Tetlow. Robust foreground segmentation and image registration for optical detection of GEO objects. Advances in Space Research (ASR), 64(3):733--746, 2019. [ bib | pdf | project page ]
[33] Zhipeng Cai, Tat-Jun Chin, Álvaro Parra Bustos, and Konrad Schindler. Practical optimal registration of terrestrial lidar scan pairs. ISPRS Journal of Photogrammetry and Remote Sensing, 147:118--131, 2019. [ bib | pdf | project page ]
[34] Ang-Dzung Doan, Yasir Latif, Thanh-Toan Do, Yu Liu, Shin-Fang Ch'ng, Tat-Jun Chin, and Ian Reid. Visual localization under appearance change: A filtering approach. In Digital Image Computing: Techniques and Applications (DICTA), 2019. [ bib | award | pdf | project page ]
[35] Qianggong Zhang, Tat-Jun Chin, and Huu Le. A fast resection-intersection method for the known rotation problem. In Computer Vision and Pattern Recognition (CVPR), 2018. [ bib | pdf ]
[36] Cosimo Rubino, Alessio del Bue, and Tat-Jun Chin. Practical motion segmentation for urban street view scenes. In International Conference on Robotics and Automation (ICRA), 2018. [ bib | DOI ]
[37] Huu Le, Anders Eriksson, Michael Milford, Thanh-Toan Do, Tat-Jun Chin, and David Suter. Non-smooth m-estimator for maximum consensus estimation. In British Machine Vision Conference (BMVC), 2018. [ bib | award | pdf ]
[38] Anders Eriksson, Fredrik Kahl, Carl Olsson, and Tat-Jun Chin. Rotation averaging and strong duality. In Computer Vision and Pattern Recognition (CVPR), 2018. [ bib | pdf ]
[39] Tat-Jun Chin, Zhipeng Cai, and Frank Neumann. Robust fitting in computer vision: easy or hard? In European Conference on Computer Vision (ECCV), 2018. [ bib | award | slides | pdf ]
[40] Zhipeng Cai, Tat-Jun Chin, Huu Le, and David Suter. Deterministic consensus maximization with biconvex programming. In European Conference on Computer Vision (ECCV), 2018. [ bib | slides | pdf | project page ]
[41] Qianggong Zhang and Tat-Jun Chin. Coresets for triangulation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40(9):2095--2108, 2018. [ bib | pdf ]
[42] Álvaro Parra Bustos and Tat-Jun Chin. Guaranteed outlier removal for point cloud registration with correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40(12):2868--2882, 2018. [ bib | pdf ]
[43] Tat-Jun Chin and David Suter. The maximum consensus problem: recent algorithmic advances. Synthesis Lectures on Computer Vision. Morgan and Claypool Publishers, San Rafael, CA, U.S.A., 2017. [ bib | project page ]
[44] Qianggong Zhang, Tat-Jun Chin, and David Suter. Quasiconvex plane sweep for triangulation with outliers. In International Conference on Computer Vision (ICCV), 2017. [ bib | pdf ]
[45] Huu Le, Tat-Jun Chin, and David Suter. An exact penalty method for locally convergent maximum consensus. In Computer Vision and Pattern Recognition (CVPR), 2017. [ bib | pdf ]
[46] Huu Le, Tat-Jun Chin, and David Suter. RATSAC - random tree sampling for maximum consensus estimation. In Digital Image Computing: Techniques and Applications (DICTA), 2017. [ bib | award | pdf ]
[47] Alireza Khosravian, Tat-Jun Chin, Ian Reid, and Robert Mahony. A discrete-time attitude observer on so(3) for vision and gps fusion. In International Conference on Robotics and Automation (ICRA), 2017. [ bib | pdf ]
[48] Alireza Khosravian, Tat-Jun Chin, and Ian Reid. A branch-and-bound algorithm for checkerboard extraction in camera-laser calibration. In International Conference on Robotics and Automation (ICRA), 2017. [ bib | pdf ]
[49] Pulak Purkait, Tat-Jun Chin, Alireza Sadri, and David Suter. Clustering with hypergraphs: the case for large hyperedges. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 39(9):1697--1711, 2017. [ bib | DOI | project page ]
[50] Tat-Jun Chin, Pulak Purkait, Anders Eriksson, and David Suter. Efficient globally optimal consensus maximisation with tree search. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 39(4):758--772, 2017. [ bib | DOI ]
[51] Tat-Jun Chin, Huan Do, Nick Moretti, and Matthew Tetlow. Robust geometric algorithms for space object detection. In IAA Symposium on Space Debris, International Astronautical Congress (IAC), 2017. [ bib | pdf ]
[52] Trung Pham, Hamid Rezatofighi, Ian Reid, and Tat-Jun Chin. Efficient point process inference for large-scale object detection. In Computer Vision and Pattern Recognition (CVPR), 2016. [ bib | pdf ]
[53] Ryan Marker, Tat-Jun Chin, and Garry Newsam. Eficient geometric matching with polar bounds for aligning star field images. In Australasian Conference on Robotics and Automation (ACRA), 2016. [ bib | pdf ]
[54] Huu Le, Tat-Jun Chin, and David Suter. Conformal surface alignment with optimal möbius search. In Computer Vision and Pattern Recognition (CVPR), 2016. [ bib | pdf ]
[55] Anders Eriksson, John Bastian, Tat-Jun Chin, and Mats Isaksson. A consensus-based framework for distributed bundle adjustment. In Computer Vision and Pattern Recognition (CVPR), 2016. [ bib ]
[56] Tat-Jun Chin, Yang Heng Kee, Anders Eriksson, and Frank Neumann. Guaranteed outlier removal with mixed integer linear programs. In Computer Vision and Pattern Recognition (CVPR), 2016. [ bib | pdf ]
[57] Álvaro Parra Bustos, Tat-Jun Chin, Anders Eriksson, Hongdong Li, and David Suter. Fast rotation search with stereographic projections for 3d registration. IEEE Transactions on Pattern Analysis Machine Intelligence (TPAMI), 38(11):2227--2240, 2016. [ bib | DOI | project page ]
[58] Taotao Lai, Hanzi Wang, Yan Yan, Tat-Jun Chin, and Wan-Lei Zhao. Motion segmentation via sparsity constraint. IEEE Transactions on Intelligent Transportation Systems (ITS), 18(4):973--983, 2016. [ bib | DOI ]
[59] Álvaro Parra and Tat-Jun Chin. Guaranteed outlier removal for rotation search. In International Conference on Computer Vision (ICCV), 2015. [ bib | pdf | project page ]
[60] William X. Liu and Tat-Jun Chin. Smooth globally warp locally: video stabilisation using homography fields. In Digital Image Computing: Techniques and Applications (DICTA), 2015. [ bib | award | demo | code | pdf ]
[61] Anders Eriksson, Mats Isaksson, and Tat-Jun Chin. High-breakdown bundle adjustment. In Winter Conference on Applications of Computer Vision (WACV), 2015. [ bib | DOI ]
[62] Anders Eriksson, Trung Pham, Tat-Jun Chin, and Ian Reid. The k-support norm and convex envelopes of cardinality and rank. In Computer Vision and Pattern Recognition (CVPR), 2015. [ bib | pdf ]
[63] Tat-Jun Chin, Pulak Purkait, Anders Eriksson, and David Suter. Efficient globally optimal consensus maximisation with tree search. In Computer Vision and Pattern Recognition (CVPR), 2015. [ bib | award | slides | code | pdf ]
[64] Hanno Ackermann, Björn Scheuermann, Tat-Jun Chin, and Bodo Rosenhahn. Randomly walking can get you lost: graph segmentation with unknown edge weights. In International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2015. [ bib | DOI ]
[65] Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, and David Suter. Clustering with hypergraphs: the case for large hyperedges. In European Conference on Computer Vision (ECCV), 2014. [ bib | pdf | project page ]
[66] Álvaro Parra, Tat-Jun Chin, and David Suter. Fast rotation search with stereographic projections for 3d registration. In Computer Vision and Pattern Recognition (CVPR), 2014. [ bib | pdf | project page ]
[67] Tat-Jun Chin, Álvaro Parra, Michael S. Brown, and David Suter. Fast rotation search for real-time interactive point cloud registration. In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3DG), 2014. [ bib | demo | pdf ]
[68] Bineng Zhong, Hongxun Yao, Sheng Chen, Rongrong Ji, Tat-Jun Chin, and Hanzi Wang. Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition (PR), 47(3):1395--1410, 2014. [ bib | DOI ]
[69] Julio Zaragoza, Tat-Jun Chin, Quoc-Huy Tran, Michael S. Brown, and David Suter. As-projective-as-possible image stitching with moving dlt. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 36(7):1285--1298, 2014. [ bib | pdf | project page ]
[70] Jin Yu, Anders Eriksson, Tat-Jun Chin, and David Suter. An adversarial optimization approach to efficient outlier removal. Journal of Mathematical Imaging and Vision (JMIV), 48(3):451--466, 2014. [ bib | DOI | code ]
[71] Quoc-Huy Tran, Tat-Jun Chin, Wojciech Chojnacki, and David Suter. Sampling minimal subsets with large spans for robust estimation. International Journal of Computer Vision (IJCV), 106(1):93--112, 2014. [ bib | pdf ]
[72] Trung Pham, Tat-Jun Chin, Konrad Schindler, and David Suter. Interacting geometric priors for robust multi-model fitting. IEEE Transactions on Image Processing (TIP), 23(10):4601--4610, 2014. [ bib | project page ]
[73] Trung Pham, Tat-Jun Chin, Jin Yu, and David Suter. The random cluster model for robust geometric fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 36(8):1658--1671, 2014. [ bib | project page ]
[74] Julio Zaragoza, Tat-Jun Chin, Michael S. Brown, and David Suter. As-projective-as-possible image stitching with moving dlt. In Computer Vision and Pattern Recognition (CVPR), 2013. [ bib | pdf | project page ]
[75] Thuraiappah Sathyan, Tat-Jun Chin, David Suter, and Mark Hedley. Improved wireless tracking using radio frequency and video sensors. In International Conference on Information Fusion (FUSION), 2013. [ bib | DOI ]
[76] William X. Liu, Tat-Jun Chin, Gustavo Carneiro, and David Suter. Point correspondence validation under unknown radial distortion. In Digital Image Computing: Techniques and Applications (DICTA), 2013. [ bib | DOI ]
[77] Gustavo Carneiro, Zhibin Liao, and Tat-Jun Chin. Closed-loop deep vision. In Digital Image Computing: Techniques and Applications (DICTA), 2013. [ bib | pdf ]
[78] Hoi Sim Wong, Tat-Jun Chin, Jin Yu, and David Suter. A simultaneous sample-and-filter strategy for robust multi-structure model fitting. Computer Vision and Image Understanding (CVIU), 117(12):1755--1769, 2013. [ bib | DOI ]
[79] Hoi Sim Wong, Tat-Jun Chin, Jin Yu, and David Suter. Mode seeking over permutations for rapid geometric model fitting. Pattern Recognition (PR), 46(1):257--271, 2013. [ bib | DOI ]
[80] Thuraiappah Sathyan, Tat-Jun Chin, Sanjeev Arulampalam, and David Suter. A multiple hypothesis tracker for multitarget tracking with multiple simultaneous measurements. J. Sel. Topics Signal Processing, 7(3):448--460, 2013. [ bib | DOI ]
[81] Junhong Gao, Yu Li, Tat-Jun Chin, and Michael S. Brown. Seam driven image stitching. In Annual Conference of the European Association for Computer Graphics (Eurographics), 2013. [ bib | pdf ]
[82] Xue Zhou, Xi Li, Tat-Jun Chin, and David Suter. Superpixel-driven level set tracking. In International Conference on Image Processing (ICIP), 2012. [ bib ]
[83] Xue Zhou, Xi Li, Tat-Jun Chin, and David Suter. Adaptive human silhouette reconstruction based on the exploration of temporal information. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012. [ bib ]
[84] Quoc-Huy Tran, Tat-Jun Chin, Gustavo Carneiro, Michael S. Brown, and David Suter. In defence of RANSAC for outlier rejection in deformable registration. In European Conference on Computer Vision (ECCV), 2012. [ bib | code | pdf ]
[85] Tao Qin, Bineng Zhong, Tat-Jun Chin, and Hanzi Wang. Matting-driven online learning of Hough forests for object tracking. In International Conference on Pattern Recognition (ICPR), 2012. [ bib ]
[86] Trung Pham, Tat-Jun Chin, Jin Yu, and David Suter. The random cluster model for robust geometric fitting. In Computer Vision and Pattern Recognition (CVPR), 2012. [ bib | project page ]
[87] Hanzi Wang, Tat-Jun Chin, and David Suter. Simultaneously fitting and segmenting multiple-structure data with outliers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(6):1177--1192, 2012. [ bib | DOI ]
[88] Tat-Jun Chin, Jin Yu, and David Suter. Accelerated hypothesis generation for multistructure data via preference analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(4):625--638, 2012. [ bib | rejoinder | bug fix | code | pdf ]
[89] Jin Yu, Anders Eriksson, Tat-Jun Chin, and David Suter. An adversarial optimization approach to efficient outlier removal. In International Conference on Computer Vision (ICCV), 2011. [ bib | code | pdf ]
[90] Jin Yu, Tat-Jun Chin, and David Suter. A global optimization approach to robust multi-model fitting. In Computer Vision and Pattern Recognition (CVPR), 2011. [ bib | pdf ]
[91] Hoi Sim Wong, Tat-Jun Chin, Jin Yu, and David Suter. Dynamic and hierarchical multi-structure geometric model fitting. In International Conference on Computer Vision (ICCV), 2011. [ bib | pdf ]
[92] Trung Pham, Tat-Jun Chin, Jin Yu, and David Suter. Simultaneous sampling and multi-structure fitting with adaptive reversible jump MCMC. In Advances in Neural Information Processing Systems (NIPS), 2011. [ bib | pdf ]
[93] Tat-Jun Chin, David Suter, and Hanzi Wang. Boosting histograms of descriptor distances for scalable multiclass specific scene recognition. Image Vision Computing (IVC), 29(4):241--250, 2011. [ bib | DOI ]
[94] Hoi Sim Wong, Tat-Jun Chin, Jin Yu, and David Suter. Efficient multi-structure robust fitting with incremental top-k lists comparison. In Asian Conference on Computer Vision (ACCV), 2010. [ bib | pdf ]
[95] Hanzi Wang, Tat-Jun Chin, and David Suter. Visual localization and segmentation based on foreground/background modeling. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010. [ bib | pdf ]
[96] Tat-Jun Chin, Jin Yu, and David Suter. Accelerated hypothesis generation for multi-structure robust fitting. In European Conference on Computer Vision (ECCV), 2010. [ bib | bug fix | code | pdf ]
[97] Tat-Jun Chin, David Suter, and Hanzi Wang. Multi-structure model selection via kernel optimisation. In Computer Vision and Pattern Recognition (CVPR), 2010. [ bib | pdf ]
[98] Tat-Jun Chin and David Suter. Keypoint induced distance profiles for visual recognition. In Computer Vision and Pattern Recognition (CVPR), 2009. [ bib | pdf ]
[99] Tat-Jun Chin, Hanzi Wang, and David Suter. The ordered residual kernel for robust motion subspace clustering. In Advances in Neural Information Processing Systems (NIPS), 2009. [ bib | pdf ]
[100] Tat-Jun Chin, Hanzi Wang, and David Suter. Robust fitting of multiple structures: The statistical learning approach. In International Conference on Computer Vision (ICCV), 2009. [ bib | code | pdf ]
[101] Tat-Jun Chin, Yilun You, Celine Coutrix, Joo-Hwee Lim, Jean-Pierre Chevallet, and Laurence Nigay. Mobile phone-based mixed reality: the snap2play game. The Visual Computer, 25(1):25--37, 2009. [ bib ]
[102] Rami Albatal, Philip Mulhem, Yves Chiaramella, and Tat-Jun Chin. Comparing image segmentation algorithms for content based image retrieval systems. In Singaporean-French IPAL Symposium (SinFra), 2009. [ bib ]
[103] Yilun You, Tat-Jun Chin, Joo-Hwee Lim, Jean-Pierre Chevallet, Celine Coutrix, and Laurence Nigay. Deploying and evaluating a mixed reality mobile treasure hunt: Snap2Play. In Mobile Human Computer Interaction, 2008. [ bib ]
[104] Tat-Jun Chin, Hanlin Goh, and Joo-Hwee Lim. Using densely recorded scenes for place recognition. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008. [ bib | pdf ]
[105] Tat-Jun Chin, Y. You, Celine Coutrix, Joo-Hwee Lim, Jean-Pierre Chevallet, and Laurence Nigay. Snap2Play: a mixed-reality game based on scene identification. In International Conference on Multimedia Modeling (MMM), 2008. [ bib ]
[106] Tat-Jun Chin, Hanlin Goh, and Ngan-Meng Tan. Exact integral images at generic angles for 2D barcode detection. In International Conference on Pattern Recognition (ICPR), 2008. [ bib ]
[107] Tat-Jun Chin and David Suter. Out-of-sample extrapolation of learned manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 30(9):1547--1556, 2008. [ bib | pdf ]
[108] Tat-Jun Chin, Hanlin Goh, and Joo-Hwee Lim. Boosting descriptors condensed from video sequences for place recognition. In CVPR Workshop (CVPRW) on Visual Localization for Mobile Platforms (VLMP), 2008. [ bib ]
[109] Tat-Jun Chin, Liang Wang, Konrad Schindler, and David Suter. Extrapolating learned manifolds for human activity recognition. In International Conference on Image Processing (ICIP), 2007. [ bib | pdf ]
[110] Tat-Jun Chin and David Suter. Incremental kernel principal component analysis. IEEE Transactions on Image Processing (TIP), 16(6):1662--1674, 2007. [ bib | code | pdf ]
[111] Nathan Faggian, Andrew Paplinski, and Tat-Jun Chin. Face recognition from video using active appearance model segmentation. In International Conference on Pattern Recognition (ICPR), 2006. [ bib ]
[112] Tat-Jun Chin and David Suter. A new distance criterion for face recognition using image sets. In Asian Conference on Computer Vision (ACCV), 2006. [ bib ]
[113] Tat-Jun Chin, Konrad Schindler, and David Suter. Incremental kernel SVD for face recognition with image sets. In International Conference on Automatic Face and Gesture Recognition (FG), 2006. [ bib ]
[114] Tat-Jun Chin and David Suter. Incremental kernel PCA for efficient non-linear feature extraction. In British Machine Vision Conference (BMVC), 2006. [ bib ]
[115] Tat-Jun Chin and David Suter. Improving the speed of kernel PCA on large scale datasets. In International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2006. [ bib ]
[116] Therdsak Tangkuampien and Tat-Jun Chin. Locally linear embedding for markerless human motion capture using multiple cameras. In Digital Image Computing: Techniques and Applications (DICTA), 2005. [ bib ]
[117] Tat-Jun Chin, James U, Konrad Schindler, and David Suter. Face recognition from video by matching image sets. In Digital Image Computing: Techniques and Applications (DICTA), 2005. [ bib ]