2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to pattern recognition and statistical machine learning theory
Joachim M. Buhmann spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Cluster analysis and Image segmentation. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision. The Pattern recognition study combines topics in areas such as Contextual image classification, Embedding and Pairwise comparison.
His work on Image texture as part of general Segmentation research is often related to Associative property, thus linking different fields of science. His Cluster analysis research is multidisciplinary, incorporating perspectives in Data mining and Dimensionality reduction. Joachim M. Buhmann has researched Image segmentation in several fields, including Pixel, Optimization problem, Histogram and Active appearance model.
Joachim M. Buhmann mainly focuses on Artificial intelligence, Pattern recognition, Cluster analysis, Segmentation and Computer vision. While the research belongs to areas of Artificial intelligence, Joachim M. Buhmann spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Inference. His Pattern recognition study integrates concerns from other disciplines, such as Contextual image classification, Histogram, Feature and Image processing.
In most of his Cluster analysis studies, his work intersects topics such as Data mining. He studies Computer vision, namely Pixel. His Correlation clustering research integrates issues from Clustering high-dimensional data and Fuzzy clustering.
Joachim M. Buhmann mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Algorithm and Artificial neural network. His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His biological study spans a wide range of topics, including Sampling, Entropy and Softmax function.
His studies in Pattern recognition integrate themes in fields like Supervised learning, Random forest, Feature and Feature. His work carried out in the field of Algorithm brings together such families of science as Bayesian probability, Probabilistic logic, Gaussian process and Robustness. In his research on the topic of Artificial neural network, Spinal surgery, Stenosis and Magnetic resonance imaging is strongly related with Surgical planning.
Joachim M. Buhmann mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Machine learning. Joachim M. Buhmann undertakes multidisciplinary studies into Artificial intelligence and Connectomics in his work. His studies deal with areas such as Contextual image classification and Feature as well as Pattern recognition.
The concepts of his Segmentation study are interwoven with issues in Multiplexing and Tissue sections. Many of his research projects under Computer vision are closely connected to Sparse matrix with Sparse matrix, tying the diverse disciplines of science together. His work deals with themes such as Classifier and Active learning, which intersect with Machine learning.
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Distortion invariant object recognition in the dynamic link architecture
M. Lades;J.C. Vorbruggen;J. Buhmann;J. Lange.
IEEE Transactions on Computers (1993)
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Charlotte Giesen;Hao A O Wang;Denis Schapiro;Nevena Zivanovic.
Nature Methods (2014)
The Balanced Accuracy and Its Posterior Distribution
Kay Henning Brodersen;Cheng Soon Ong;Klaas Enno Stephan;Joachim M. Buhmann.
international conference on pattern recognition (2010)
Pairwise data clustering by deterministic annealing
T. Hofmann;J.M. Buhmann.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
Stability-based validation of clustering solutions
Tilman Lange;Volker Roth;Mikio L. Braun;Joachim M. Buhmann.
Neural Computation (2004)
Empirical evaluation of dissimilarity measures for color and texture
J. Puzicha;J.M. Buhmann;Y. Rubner;C. Tomasi.
international conference on computer vision (1999)
Empirical Evaluation of Dissimilarity Measures for Color and Texture
Yossi Rubner;Jan Puzicha;Carlo Tomasi;Joachim M Buhmann.
Computer Vision and Image Understanding (2001)
Non-parametric similarity measures for unsupervised texture segmentation and image retrieval
J. Puzicha;T. Hofmann;J.M. Buhmann.
computer vision and pattern recognition (1997)
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry
Lukas Reiter;Manfred Claassen;Sabine P. Schrimpf;Marko Jovanovic.
Molecular & Cellular Proteomics (2009)
Topology free hidden Markov models: application to background modeling
B. Stenger;V. Ramesh;N. Paragios;F. Coetzee.
international conference on computer vision (2001)
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