Communications of the ACM
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Self-Organizing Maps
Using Growing Cell Structures for Surface Reconstruction
SMI '03 Proceedings of the Shape Modeling International 2003
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A neural network for recovering 3D shape from erroneous and few depth maps of shaded images
Pattern Recognition Letters
Growing Neural Gas (GNG): A Soft Competitive Learning Method for 2D Hand Modelling
IEICE - Transactions on Information and Systems
Interactive k-d tree GPU raytracing
Proceedings of the 2007 symposium on Interactive 3D graphics and games
Three-dimensional surface reconstruction using meshing growing neural gas (MGNG)
The Visual Computer: International Journal of Computer Graphics
Pattern Recognition Letters
Amdahl's Law in the Multicore Era
Computer
Validity of the single processor approach to achieving large scale computing capabilities
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Neural Network Implementation Using CUDA and OpenMP
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
An Improved Evolutionary Approach for Egomotion Estimation with a 3D TOF Camera
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Designing efficient sorting algorithms for manycore GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Face Detection Using GPU-Based Convolutional Neural Networks
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Three-dimensional mapping with time-of-flight cameras
Journal of Field Robotics - Three-Dimensional Mapping, Part 2
Efficient simulation of large-scale spiking neural networks using CUDA graphics processors
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Robust on-line model-based object detection from range images
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IEEE Micro
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Unsupervised classification of dynamic obstacles in urban environments
Journal of Field Robotics
Parallelization libraries: Characterizing and reducing overheads
ACM Transactions on Architecture and Code Optimization (TACO)
GPU Computing Gems Emerald Edition
GPU Computing Gems Emerald Edition
Fast image representation with GPU-based growing neural gas
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
IEEE Transactions on Fuzzy Systems
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Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6x compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.