Deep Networks Based Energy Models for Object Recognition from Multimodality Images
暂无分享,去创建一个
[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.