Buhmann, J.
* 1996: Regularizing Phase Based Stereo
Buhmann, J.M.
* 1993: Distortion Invariant Object Recognition in the Dynamic Link Architecture
* 1993: Sensory Segmentation with Coupled Neural Oscillators
* 1994: maximum entropy approach to pairwise data clustering, A
* 1996: Deterministic Annealing Framework for Unsupervised Texture Segmentation, A
* 1996: Unsupervised Segmentation of Textured Images by Pairwise Data Clustering
* 1997: Multiscale Annealing for Real-Time Unsupervised Texture Segmentation
* 1997: Non-Parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
* 1997: Optimization Approach to Unsupervised Hierarchical Texture Segmentation, An
* 1997: Pairwise Data Clustering by Deterministic Annealing
* 1997: Pairwise Data Clustering by Deterministic Annealing
* 1997: Region-Based Motion Compensated 3D-Wavelet Transform Coding of Video
* 1997: Video-Coding by Region-Based Motion Compensation and Spatio-Temporal Wavelet Transform
* 1998: Multiscale Annealing for Real-Time Unsupervised Texture Segmentation
* 1998: On Spatial Quantization of Color Images
* 1998: Unsupervised Texture Segmentation in a Deterministic Annealing Framework
* 1999: Empirical Evaluation of Dissimilarity Measures for Color and Texture
* 1999: Histogram Clustering for Unsupervised Image Segmentation
* 1999: Histogram clustering for unsupervised segmentation and image retrieval
* 1999: Multiscale Annealing for Grouping and Unsupervised Texture Segmentation
* 2000: Active Learning for Hierarchical Pairwise Data Clustering
* 2000: Data Visualization by Multidimensional Scaling: A Deterministic Annealing Approach
* 2000: Feature Selection for Support Vector Machines
* 2000: On Learning Texture Edge Detectors
* 2000: On Spatial Quantization of Color Images
* 2000: theory of proximity based clustering: structure detection by optimization, A
* 2001: Contextual Classification by Entropy-Based Polygonization
* 2001: Empirical Evaluation of Dissimilarity Measures for Color and Texture
* 2001: Path Based Pairwise Data Clustering with Application to Texture Segmentation
* 2001: Perceptual Grouping by Path Based Clustering
* 2001: Topology Free Hidden Markov Models: Application to Background Modeling
* 2002: Combined color and texture segmentation by parametric distributional clustering
* 2002: Data Resampling for Path Based Clustering
* 2002: Parametric Distributional Clustering for Image Segmentation
* 2002: Selforganized Clustering of Mixture Models for Combined Color and Texture Segmentation
* 2003: Bagging for path-based clustering
* 2003: minimum entropy approach to adaptive image polygonization, A
* 2003: New Distance Measure for Probabilistic Shape Modeling, A
* 2003: Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
* 2003: Path-based clustering for grouping of smooth curves and texture segmentation
* 2004: Landscape of clustering algorithms
* 2004: Shape constrained image segmentation by parametric distributional clustering
* 2005: Learning with Constrained and Unlabelled Data
* 2005: Object Categorization by Compositional Graphical Models
* 2006: Dense Stereo by Triangular Meshing and Cross Validation
* 2006: Learning Compositional Categorization Models
* 2006: Learning Top-Down Grouping of Compositional Hierarchies for Recognition
* 2006: Model Order Selection and Cue Combination for Image Segmentation
* 2006: Model Selection in Kernel Methods Based on a Spectral Analysis of Label Information
* 2006: On the information and representation of non-Euclidean pairwise data
* 2006: Probabilistic De Novo Peptide Sequencing with Doubly Charged Ions
* 2006: Smooth Image Segmentation by Nonparametric Bayesian Inference
* 2007: Bayesian Order-Adaptive Clustering for Video Segmentation
* 2007: Compositional Object Recognition, Segmentation, and Tracking in Video
* 2007: Learning the Compositional Nature of Visual Objects
* 2007: Regularized Data Fusion Improves Image Segmentation
* 2007: Robust Image Segmentation Using Resampling and Shape Constraints
* 2008: Automatic Detection of Learnability under Unreliable and Sparse User Feedback
* 2008: Nonparametric Bayesian Image Segmentation
* 2008: Probabilistic image registration and anomaly detection by nonlinear warping
* 2008: Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma
* 2009: Inter-active learning of randomized tree ensembles for object detection
* 2009: Randomized Tree Ensembles for Object Detection in Computational Pathology
* 2009: Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
* 2010: Balanced Accuracy and Its Posterior Distribution, The
* 2010: Binormal Assumption on Precision-Recall Curves, The
* 2010: Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
* 2010: Learning the Compositional Nature of Visual Object Categories for Recognition
* 2010: Neuron geometry extraction by perceptual grouping in ssTEM images
* 2010: Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning
* 2011: Agnostic Domain Adaptation
* 2011: Weakly supervised semantic segmentation with a multi-image model
* 2012: Active learning for semantic segmentation with expected change
* 2012: Cardiac LV and RV Segmentation Using Mutual Context Information
* 2012: Learning Dictionaries With Bounded Self-Coherence
* 2012: Weakly supervised structured output learning for semantic segmentation
* 2013: Approximate Sorting
* 2013: Automatic Detection and Segmentation of Crohn's Disease Tissues From Abdominal MRI
* 2014: Convolutional Decision Trees for Feature Learning and Segmentation
* 2015: Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images
* 2015: Transformation-Invariant Convolutional Jungles
* 2015: Visual Saliency Based Active Learning for Prostate MRI Segmentation
* 2016: TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
* 2017: Model Selection for Gaussian Process Regression
* 2018: Wheel Defect Detection With Machine Learning
* 2019: Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography
* 2019: Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression
* 2020: Instance Segmentation for the Quantification of Microplastic Fiber Images
* 2021: Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography
Includes: Buhmann, J.M. Buhmann, J.M.[Joachim M.]
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