Deep Networks Based Energy Models for Object Recognition from Multimodality Images

[1]  Ang Li,et al.  Comprehensive autoencoder for prostate recognition on MR images , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[2]  Yaozong Gao,et al.  Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching , 2016, IEEE Transactions on Medical Imaging.

[3]  Zhenfeng Zhang,et al.  Superpixel-Based Segmentation for 3D Prostate MR Images , 2016, IEEE Transactions on Medical Imaging.

[4]  Xuelong Li,et al.  Two-Stage Learning to Predict Human Eye Fixations via SDAEs , 2016, IEEE Transactions on Cybernetics.

[5]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.

[6]  Haisheng Li,et al.  Saliency detection using two-stage scoring , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[7]  Nikos Komodakis,et al.  HARF: Hierarchy-Associated Rich Features for Salient Object Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Xuelong Li,et al.  DISC: Deep Image Saliency Computing via Progressive Representation Learning , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Joachim M. Buhmann,et al.  Visual Saliency Based Active Learning for Prostate MRI Segmentation , 2015, MLMI.

[10]  Pengfei Shi,et al.  Salient object detection using normalized cut and geodesics , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[11]  Feng Wu,et al.  Background Prior-Based Salient Object Detection via Deep Reconstruction Residual , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Weiwei Du,et al.  Graph-based prostate extraction in T2-weighted images for prostate cancer detection , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[13]  R. Venkatesh Babu,et al.  Salient object detection via objectness measure , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[14]  Xiaogang Wang,et al.  Saliency detection by multi-context deep learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  David Dagan Feng,et al.  Robust saliency detection via regularized random walks ranking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Huchuan Lu,et al.  Salient object detection via bootstrap learning , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Huchuan Lu,et al.  Deep networks for saliency detection via local estimation and global search , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Huchuan Lu,et al.  Saliency detection via Cellular Automata , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Xiangyu Zhu,et al.  Object detection by labeling superpixels , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Wei Liu,et al.  Saliency propagation from simple to difficult , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Jesper Carl,et al.  The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images. , 2015, Medical physics.

[22]  Christian Szegedy,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[23]  Bingbing Liu,et al.  Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images , 2015, IEEE Transactions on Medical Imaging.

[24]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[25]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[26]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Zhang Xiong,et al.  Autoencoder-Based Collaborative Filtering , 2014, ICONIP.

[28]  Ling Shao,et al.  Action recognition by spatio-temporal oriented energies , 2014, Inf. Sci..

[29]  Rynson W. H. Lau,et al.  Saliency Detection with Flash and No-flash Image Pairs , 2014, ECCV.

[30]  Luc Van Gool,et al.  Face Detection without Bells and Whistles , 2014, ECCV.

[31]  Daniel Rueckert,et al.  Hybrid Decision Forests for Prostate Segmentation in Multi-channel MR Images , 2014, 2014 22nd International Conference on Pattern Recognition.

[32]  Ang Li,et al.  Medical image segmentation based on Dirichlet energies and priors , 2014 .

[33]  Sidong Liu,et al.  Early diagnosis of Alzheimer's disease with deep learning , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[34]  Yaozong Gao,et al.  Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning. , 2014, Medical physics.

[35]  James M. Rehg,et al.  The Secrets of Salient Object Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Joachim M. Buhmann,et al.  Prostate MRI Segmentation Using Learned Semantic Knowledge and Graph Cuts , 2014, IEEE Transactions on Biomedical Engineering.

[37]  Florian Jung,et al.  Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge , 2014, Medical Image Anal..

[38]  A. Fenster,et al.  Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images , 2014, IEEE Transactions on Medical Imaging.

[39]  Ang Li,et al.  Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker , 2013, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[40]  Sim Heng Ong,et al.  Automatic 3D Prostate MR Image Segmentation Using Graph Cuts and Level Sets with Shape Prior , 2013, PCM.

[41]  Huchuan Lu,et al.  Saliency Detection via Absorbing Markov Chain , 2013, 2013 IEEE International Conference on Computer Vision.

[42]  David Vázquez,et al.  Random Forests of Local Experts for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[43]  Huchuan Lu,et al.  Saliency Detection via Dense and Sparse Reconstruction , 2013, 2013 IEEE International Conference on Computer Vision.

[44]  Huazhong Shu,et al.  Prostate segmentation on T2 MRI using Optimal Surface Detection , 2013 .

[45]  Shu Liao,et al.  Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation , 2013, MICCAI.

[46]  Gernot A. Fink,et al.  Bag-of-features representations using spatial visual vocabularies for object classification , 2013, 2013 IEEE International Conference on Image Processing.

[47]  Desire Sidibé,et al.  A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images , 2013, Medical Image Anal..

[48]  David Dagan Feng,et al.  Lung image patch classification with automatic feature learning , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[49]  Jingdong Wang,et al.  Salient Object Detection: A Discriminative Regional Feature Integration Approach , 2013, International Journal of Computer Vision.

[50]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[51]  Li Xu,et al.  Hierarchical Saliency Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[52]  James V. Miller,et al.  Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[53]  Dwarikanath Mahapatra,et al.  Graph cut based automatic prostate segmentation using learned semantic information , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[54]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[55]  Razvan Pascanu,et al.  Theano: new features and speed improvements , 2012, ArXiv.

[56]  Desire Sidibé,et al.  Graph cut energy minimization in a probabilistic learning framework for 3D prostate segmentation in MRI , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[57]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[58]  Jian Sun,et al.  Geodesic Saliency Using Background Priors , 2012, ECCV.

[59]  Desire Sidibé,et al.  A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images , 2012, Comput. Methods Programs Biomed..

[60]  Ferdinand van der Heijden,et al.  Prostate MR image segmentation using 3D active appearance models , 2012 .

[61]  Josien P. W. Pluim,et al.  Patient Specific Prostate Segmentation in 3-D Magnetic Resonance Images , 2012, IEEE Transactions on Medical Imaging.

[62]  Yong Yin,et al.  Automated PET-guided liver segmentation from low-contrast CT volumes using probabilistic atlas , 2012, Comput. Methods Programs Biomed..

[63]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[64]  Ali Borji,et al.  Boosting bottom-up and top-down visual features for saliency estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[65]  Ying Wu,et al.  A unified approach to salient object detection via low rank matrix recovery , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[66]  Jimei Yang,et al.  Top-down visual saliency via joint CRF and dictionary learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[67]  A. Jemal,et al.  International variation in prostate cancer incidence and mortality rates. , 2012, European urology.

[68]  Anant Madabhushi,et al.  Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation , 2012, IEEE Transactions on Medical Imaging.

[69]  Carole Lartizien,et al.  Computer-Aided Staging of Lymphoma Patients With FDG PET/CT Imaging Based on Textural Information , 2012, IEEE Journal of Biomedical and Health Informatics.

[70]  Emmanouil Moschidis,et al.  Automatic differential segmentation of the prostate in 3-D MRI using Random Forest classification and graph-cuts optimization , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[71]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[72]  R. Basri,et al.  Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[73]  Chao Lu,et al.  An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy , 2011, Medical Image Anal..

[74]  Leo Grady,et al.  Facilitating 3D Spectroscopic Imaging through Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized via Boosted Classifiers , 2011, Prostate Cancer Imaging.

[75]  Wei Li,et al.  Learning Image Context for Segmentation of Prostate in CT-Guided Radiotherapy , 2011, MICCAI.

[76]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[77]  N. Mitra,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[78]  R. Lenkinski,et al.  Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI. , 2011, Academic radiology.

[79]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[80]  Anant Madabhushi,et al.  A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation , 2011, Medical Image Anal..

[81]  Deepu Rajan,et al.  Random Walks on Graphs for Salient Object Detection in Images , 2010, IEEE Transactions on Image Processing.

[82]  Masoom A. Haider,et al.  Prostate Cancer Segmentation Using Multispectral Random Walks , 2010, Prostate Cancer Imaging.

[83]  Esa Rahtu,et al.  Segmenting Salient Objects from Images and Videos , 2010, ECCV.

[84]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[85]  Wen Gao,et al.  Measuring visual saliency by Site Entropy Rate , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[86]  João Carreira,et al.  Constrained parametric min-cuts for automatic object segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[87]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[88]  Jocelyne Troccaz,et al.  Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model. , 2010, Medical physics.

[89]  Shi-Min Hu,et al.  Sketch2Photo: internet image montage , 2009, ACM Trans. Graph..

[90]  Xiaodong Wu,et al.  Optimal Graph Search Segmentation Using Arc-Weighted Graph for Simultaneous Surface Detection of Bladder and Prostate , 2009, MICCAI.

[91]  Qianjin Feng,et al.  Segmenting CT Prostate Images Using Population and Patient-Specific Statistics for Radiotherapy , 2009, ISBI.

[92]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[93]  N. Vasconcelos,et al.  Discriminant Saliency, the Detection of Suspicious Coincidences, and Applications to Visual Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[94]  Olivier Colot,et al.  Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI , 2009, International Journal of Computer Assisted Radiology and Surgery.

[95]  Hanqing Lu,et al.  Saliency Cuts: An automatic approach to object segmentation , 2008, 2008 19th International Conference on Pattern Recognition.

[96]  Gabriel Thomas,et al.  Semi automatic MRI prostate segmentation based on wavelet multiscale products , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[97]  Dorin Comaniciu,et al.  Simultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography , 2008, ECCV.

[98]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[99]  Jocelyne Troccaz,et al.  Atlas-based prostate segmentation using an hybrid registration , 2008, International Journal of Computer Assisted Radiology and Surgery.

[100]  Dorin Comaniciu,et al.  3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[101]  Dorin Comaniciu,et al.  Accurate polyp segmentation for 3D CT colongraphy using multi-staged probabilistic binary learning and compositional model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[102]  Dorin Comaniciu,et al.  Hierarchical, learning-based automatic liver segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[103]  Jagath Samarabandu,et al.  Prostate Segmentation from 2-D Ultrasound Images Using Graph Cuts and Domain Knowledge , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[104]  Ariel Shamir,et al.  Seam Carving for Content-Aware Image Resizing , 2007, ACM Trans. Graph..

[105]  Nanning Zheng,et al.  Learning to Detect A Salient Object , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[106]  Reyer Zwiggelaar,et al.  A hybrid ASM approach for sparse volumetric data segmentation , 2007, Pattern Recognition and Image Analysis.

[107]  Amjad Zaim,et al.  An Energy-Based Segmentation of Prostate from Ultrasouind Images using Dot-Pattern Select Cells , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[108]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[109]  Aaron Fenster,et al.  Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D , 2006, Comput. Methods Programs Biomed..

[110]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[111]  Marie-Pierre Jolly,et al.  Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images , 2006, International Journal of Computer Vision.

[112]  G. Thomas,et al.  Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[113]  N.N. Kachouie,et al.  An Elliptical Level Set Method for Automatic TRUS Prostate Image Segmentation , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[114]  Payel Ghosh,et al.  Segmentation of medical images using a genetic algorithm , 2006, GECCO.

[115]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[116]  Dinggang Shen,et al.  Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method , 2006, IEEE Transactions on Medical Imaging.

[117]  Shehrzad A. Qureshi Embedded Image Processing on the TMS320C6000™ DSP: Examples in Code Composer Studio™ and MATLAB , 2005 .

[118]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[119]  Yongmin Kim,et al.  Parametric shape modeling using deformable superellipses for prostate segmentation , 2004, IEEE Transactions on Medical Imaging.

[120]  Xiaoli Tang,et al.  Geometric-model-based segmentation of the prostate and surrounding structures for image-guided radiotherapy , 2004, IS&T/SPIE Electronic Imaging.

[121]  Bernhard Schölkopf,et al.  Ranking on Data Manifolds , 2003, NIPS.

[122]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[123]  Irfan A. Essa,et al.  Graphcut textures: image and video synthesis using graph cuts , 2003, ACM Trans. Graph..

[124]  Reyer Zwiggelaar,et al.  Semi-automatic Segmentation of the Prostate , 2003, IbPRIA.

[125]  Mingyue Ding,et al.  Prostate segmentation in 3D US images using the cardinal-spline-based discrete dynamic contour , 2003, SPIE Medical Imaging.

[126]  Dinggang Shen,et al.  Segmentation of prostate boundaries from ultrasound images using statistical shape model , 2003, IEEE Transactions on Medical Imaging.

[127]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[128]  Keck Voon Ling,et al.  3D Prostate Surface Detection from Ultrasound Images Based on Level Set Method , 2002, MICCAI.

[129]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[130]  Hanif M. Ladak,et al.  Prostate segmentation from 2D ultrasound images , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[131]  Jerry L Prince,et al.  Image Segmentation Using Deformable Models , 2000 .

[132]  Olga Veksler,et al.  Image segmentation by nested cuts , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[133]  Mariano Alcañiz Raya,et al.  Outlining of the prostate using snakes with shape restrictions based on the wavelet transform , 1999, Pattern Recognit..

[134]  Daniel P. Huttenlocher,et al.  A new Bayesian framework for object recognition , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[135]  C. Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[136]  Davi Geiger,et al.  Segmentation by grouping junctions , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[137]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[138]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[139]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[140]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[141]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[142]  B. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[143]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[144]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[145]  Ang Li,et al.  Adaptive background search and foreground estimation for saliency detection via comprehensive autoencoder , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[146]  G. Saranya,et al.  Lung Nodule Classification Using Deep Features in Ct Images , 2016 .

[147]  John Folkesson,et al.  Relational Approaches for Joint Object Classification and Scene Similarity Measurement in Indoor Environments , 2014, AAAI Spring Symposia.

[148]  M. Kirschner,et al.  Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model , 2012 .

[149]  Soumya Ghose,et al.  A Random Forest Based Classification Approach to Prostate Segmentation in MRI , 2012 .

[150]  Nan Wang,et al.  An analysis of Gaussian-binary restricted Boltzmann machines for natural images , 2012, ESANN.

[151]  D. Shen,et al.  Prostate segmentation by sparse representation based classification. , 2012, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[152]  Razvan Pascanu,et al.  Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.

[153]  Jianchao Zeng,et al.  Segmentation of prostate ultrasound images using an improved snakes model , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[154]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[155]  Demetri Terzopoulos,et al.  On Matching Deformable Models to Images , 1987, Topical Meeting on Machine Vision.