Computer Vision, Imaging and Computer Graphics – Theory and Applications

This book constitutes thoroughly revised and selected papers from the 10th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2015, held in Berlin, Germany, in March 2015. VISIGRAPP comprises GRAPP, International Conference on Computer Graphics Theory and Applications; IVAPP, International Conference on Information Visualization Theory and Applications; and VISAPP, International Conference on Computer Vision Theory and Applications. The 23 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 529 submissions. The book also contains one invited talk in full-paper length. The regular papers were organized in topical sections named: computer graphics theory and applications; information visualization theory and applications; and computer vision theory and applications.

[1]  Albert Fornells,et al.  A study of the effect of different types of noise on the precision of supervised learning techniques , 2010, Artificial Intelligence Review.

[2]  Jie Zhang,et al.  Instance-based object recognition in 3D point clouds using discriminative shape primitives , 2017, Machine Vision and Applications.

[3]  Antonis A. Argyros,et al.  From Multiple Views to Textured 3D Meshes: A GPU-Powered Approach , 2010, ECCV Workshops.

[4]  J. Navarro-Pedreño.  Numerical Methods for Least Squares Problems , 1996 .

[5]  Peter F. Sturm,et al.  A Factorization Based Algorithm for Multi-Image Projective Structure and Motion , 1996, ECCV.

[6]  Harry Shum,et al.  Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Kenichi Kanatani,et al.  Uncalibrated Factorization Using a Variable Symmetric Affine Camera , 2007, IEICE Trans. Inf. Syst..

[8]  Taghi M. Khoshgoftaar,et al.  Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Stephen J. Cox,et al.  Improving lip-reading performance for robust audiovisual speech recognition using DNNs , 2015, AVSP.

[10]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[11]  Federico Tombari,et al.  Performance Evaluation of 3D Keypoint Detectors , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[12]  J. Pohlmann,et al.  Parallel Analysis: a method for determining significant principal components , 1995 .

[13]  Ákos Pernek,et al.  Metric Reconstruction with Missing Data under Weak Perspective , 2008, BMVC.

[14]  Ákos Pernek,et al.  Weak-perspective structure from motion by fast alternation , 2011, The Visual Computer.

[15]  Federico Tombari,et al.  Learning to Detect Good 3D Keypoints , 2017, International Journal of Computer Vision.

[16]  Kun Li,et al.  Sparse non-rigid registration of 3D shapes , 2015, SGP '15.

[17]  Levente Hajder,et al.  Rapid weak-perspective Structure from Motion with missing data , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[18]  Guanghui Wang,et al.  Quasi-perspective projection with applications to 3D factorization from uncalibrated image sequences , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Benjamin Bustos,et al.  Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes , 2011, The Visual Computer.

[20]  R. Hartley,et al.  PowerFactorization : 3D reconstruction with missing or uncertain data , 2003 .

[21]  Alejandro F. Frangi,et al.  AV@CAR: A Spanish Multichannel Multimodal Corpus for In-Vehicle Automatic Audio-Visual Speech Recognition , 2004, LREC.

[22]  Axel Pinz,et al.  Globally Optimal O(n) Solution to the PnP Problem for General Camera Models , 2008, BMVC.

[23]  Federico Sukno,et al.  Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[24]  Fadi Dornaika,et al.  Object Pose: The Link between Weak Perspective, Paraperspective, and Full Perspective , 1997, International Journal of Computer Vision.

[25]  Barry-John Theobald,et al.  Insights into machine lip reading , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[26]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[28]  M. A. Jenkins,et al.  A three-stage variable-shift iteration for polynomial zeros and its relation to generalized rayleigh iteration , 1970 .

[29]  Valery A. Petrushin.  Hidden Markov Models: Fundamentals and Applications Part 1: Markov Chains and Mixture Models , 2000 .

[30]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[31]  R. Horaud,et al.  Surface feature detection and description with applications to mesh matching , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using orthonormal matrices , 1988 .

[33]  João Paulo Costeira,et al.  Estimating 3D shape from degenerate sequences with missing data , 2009, Comput. Vis. Image Underst..

[34]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[35]  Tetsuya Ogata,et al.  Lipreading using convolutional neural network , 2014, INTERSPEECH.

[36]  Takeo Kanade,et al.  Quasiconvex Optimization for Robust Geometric Reconstruction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Joel A. Hesch,et al.  A Direct Least-Squares (DLS) method for PnP , 2011, 2011 International Conference on Computer Vision.

[38]  Daniel Cremers,et al.  Real-Time Dense Geometry from a Handheld Camera , 2010, DAGM-Symposium.

[39]  Levente Hajder.  W-PnP Method: Optimal Solution for the Weak-Perspective n-Point Problem and Its Application to Structure from Motion , 2017, VISIGRAPP.

[40]  Alejandro F. Frangi,et al.  Active Shape Models with Invariant Optimal Features: Application to Facial Analysis , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[42]  Richard I. Hartley,et al.  Optimal Algorithms in Multiview Geometry , 2007, ACCV.

[43]  Alessio Del Bue,et al.  Bilinear Modeling via Augmented Lagrange Multipliers (BALM) , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Takeo Kanade,et al.  A Paraperspective Factorization Method for Shape and Motion Recovery , 1994, ECCV.

[45]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[46]  M. Verleysen,et al.  Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[47]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  Anneleen Van Assche,et al.  Ensemble Methods for Noise Elimination in Classification Problems , 2003, Multiple Classifier Systems.

[49]  Shimon Whiteson,et al.  LipNet: Sentence-level Lipreading , 2016, ArXiv.

[50]  P. J. Narayanan,et al.  Practical Time Bundle Adjustment for 3D Reconstruction on the GPU , 2010, ECCV Workshops.

[51]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[52]  Andrew W. Fitzgibbon,et al.  Damped Newton algorithms for matrix factorization with missing data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[53]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[54]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[55]  Richard I. Hartley,et al.  Multiple-View Geometry Under the {$L_\infty$}-Norm , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[56]  Takayuki Okatani,et al.  On the Wiberg Algorithm for Matrix Factorization in the Presence of Missing Components , 2007, International Journal of Computer Vision.

[57]  Hongbin Zha,et al.  Unsupervised Random Forest Manifold Alignment for Lipreading , 2013, 2013 IEEE International Conference on Computer Vision.

[58]  Daphna Weinshall,et al.  Linear and incremental acquisition of invariant shape models from image sequences , 1993, 1993 (4th) International Conference on Computer Vision.

[59]  Leonidas J. Guibas,et al.  A concise and provably informative multi-scale signature based on heat diffusion , 2009 .