Index for buhm

Buhmann, J. Co Author Listing * Regularizing Phase Based Stereo

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