His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Image retrieval and Analytical chemistry. His Artificial intelligence study combines topics in areas such as Machine learning and Invariant. Hans Burkhardt interconnects Robot and Large field of view in the investigation of issues within Computer vision.
His Pattern recognition research includes elements of Speech recognition and Cluster analysis. Hans Burkhardt interconnects Nearest neighbor search and Search engine indexing in the investigation of issues within Image retrieval. His Analytical chemistry research incorporates elements of Microorganism, Online identification and Streptococcus thermophilus.
Hans Burkhardt mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Invariant and Image retrieval. Artificial intelligence is closely attributed to Algorithm in his study. His Computer vision research includes themes of Robot and Robustness.
His Pattern recognition research incorporates themes from Contextual image classification and Image. Hans Burkhardt has researched Invariant in several fields, including Voxel, Computation, Cognitive neuroscience of visual object recognition and Spherical harmonics. His Image retrieval research integrates issues from Feature and Color image.
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Algorithm. Artificial intelligence and Invariant are commonly linked in his work. In his study, which falls under the umbrella issue of Computer vision, Deblurring, Point spread function, Optical transfer function, Image restoration and Iterative reconstruction is strongly linked to Deconvolution.
His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Anisotropic diffusion, Image retrieval and Grayscale. The Image segmentation, Scale-space segmentation and Markov random field research he does as part of his general Segmentation study is frequently linked to other disciplines of science, such as Force field, therefore creating a link between diverse domains of science. His research integrates issues of Steerable filter, Affine combination, Resolution, Kernel method and Mathematical optimization in his study of Algorithm.
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Image processing and Invariant. He has included themes like Deconvolution and Machine learning in his Artificial intelligence study. His Computer vision study incorporates themes from Algorithm, Codebook and Optical transfer function.
The concepts of his Pattern recognition study are interwoven with issues in Contextual image classification, Partial volume and Grayscale. His Image processing research includes elements of Cluster analysis, Preprocessor, Data mining and Image Quantification. His Invariant study combines topics from a wide range of disciplines, such as Bag of features, Image retrieval, Visual Word, Similarity measure and Computation.
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Application of affine-invariant Fourier descriptors to recognition of 3-D objects
K. Arbter;W.E. Snyder;H. Burkhardt;G. Hirzinger.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1990)
Online handwriting recognition with support vector machines - a kernel approach
C. Bahlmann;B. Haasdonk;H. Burkhardt.
international conference on frontiers in handwriting recognition (2002)
Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations
Petra Rösch;Michaela Harz;Michael Schmitt;Klaus-Dieter Peschke.
Applied and Environmental Microbiology (2005)
Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization
J. Wolf;W. Burgard;H. Burkhardt.
IEEE Transactions on Robotics (2005)
Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions
M. Harz;P. Rösch;K. Peschke;Olaf Ronneberger.
The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping
C. Bahlmann;H. Burkhardt.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Object identification with tactile sensors using bag-of-features
Alexander Schneider;Jurgen Sturm;Cyrill Stachniss;Marco Reisert.
intelligent robots and systems (2009)
Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images.
Da-chuan Cheng;Arno Schmidt-Trucksäss;Kuo-sheng Cheng;Hans Burkhardt.
Computer Methods and Programs in Biomedicine (2002)
Data Analysis, Machine Learning, and Applications
Christine Preisach;Hans Burkhardt;Lars Schmidt-Thieme;Reinhold Decker.
4D phase contrast MRI at 3 T: effect of standard and blood-pool contrast agents on SNR, PC-MRA, and blood flow visualization.
Jelena Bock;Alex Frydrychowicz;Aurélien F. Stalder;Thorsten A. Bley;Thorsten A. Bley.
Magnetic Resonance in Medicine (2010)
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