Optimizing Multi-Feature Queries for Image Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Curve Morphing by Weighted Mean of Strings
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Exploring Connectivity of the Brain's White Matter with Dynamic Queries
IEEE Transactions on Visualization and Computer Graphics
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Bioinformatics
BrainGazer - Visual Queries for Neurobiology Research
IEEE Transactions on Visualization and Computer Graphics
A Bag of Features Approach for 3D Shape Retrieval
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Exploring the bag-of-words method for 3D shape retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
The Neuron Navigator: Exploring the information pathway through the neural maze
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
IEEE Transactions on Information Technology in Biomedicine
Non-rigid 3D object retrieval using topological information guided by conformal factors
The Visual Computer: International Journal of Computer Graphics - 3DOR 2011
A comparison of methods for non-rigid 3D shape retrieval
Pattern Recognition
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Circuit neuroscience tries to solve one of the most challenging questions in biology: How does the brain work? An important step toward an answer to this question is to gather detailed knowledge about the neuronal circuits of the model organism Drosophila melanogaster. Geometric representations of neuronal objects of the Drosophila are acquired using molecular genetic methods, confocal microscopy, nonrigid registration and segmentation. These objects are integrated into a constantly growing common atlas. The comparison of new segmented neuronal objects to already known neuronal structures is a frequent task, which evolves with a growing amount of data into a bottleneck of the knowledge discovery process. Thus, the exploration of the atlas by means of domain specific similarity measures becomes a pressing need. To enable similarity based retrieval of neuronal objects, we defined together with domain experts tailored dissimilarity measures for each of the three typical neuronal structures cell body, projection, and arborization. Moreover, we defined the neuron enhanced similarity for projections and arborizations. According to domain experts, the developed system has big advantages for all tasks, which involve extensive data exploration.