Results 191 to 200 of about 98,687 (237)

Nonlinear predictive vector quantisation with recurrent neural nets

Neural Networks for Signal Processing III - Proceedings of the 1993 IEEE-SP Workshop, 2002
The nonlinear prediction capability of neural nets is applied to the design of improved predictive speech coders. Performance evaluations and comparisons with linear predictive speech coding are presented. These tests show the applicability of nonlinear prediction to speech coding and the improvement in coding performance. >
L. Wu, M. Niranjan, F. Fallside
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Motion analysis with recurrent neural nets

1994
218zVisual tasks such as the interpretation of cell images (Psarrou and Buxton, 1993) and the recognition of moving vehicles require to track objects along their trajectory and to predict their future position in their environment. It was noted that objects move purposely in an environment and effective prediction on their trajectories can be achieved ...
A. Psarrou, H. Buxton
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RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation

IEEE Journal of Biomedical and Health Informatics, 2019
The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velocity that needs to be adapted for each segmentation task. The Convolutional Neural Networks (CNN) address this issue by learning robust features in a supervised end-to-end manner.
Arunava Chakravarty, Jayanthi Sivaswamy
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Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)

NAECON 2018 - IEEE National Aerospace and Electronics Conference, 2018
Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology.
Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari   +3 more
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GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation

Medical Image Analysis, 2019
Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks.
T. Wollmann, M. Gunkel, I. Chung, H. Erfle, K. Rippe, K. Rohr   +5 more
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A Novel Physics-Informed Recurrent Neural Network Approach for State Estimation of Autonomous Platforms

IEEE International Joint Conference on Neural Network
State estimation of autonomous platforms is a crucial element in the design and test of perception algorithms. The nonlinear nature of autonomous platforms makes hard to design accurate state estimation algorithms without using linearization techniques ...
Adolfo Perrusquía, Weisi Guo
semanticscholar   +1 more source

A Novel Technique for Query Plan Representation Based on Graph Neural Nets

International Conference on Data Warehousing and Knowledge Discovery
Learning representations for query plans play a pivotal role in machine learning-based query optimizers of database management systems. To this end, particular model architectures are proposed in the literature to transform the tree-structured query ...
Baoming Chang, Amin Kamali, Verena Kantere   +2 more
semanticscholar   +1 more source

A recurrent neural net approach to one-step ahead control problems

IEEE Transactions on Systems, Man, and Cybernetics, 1994
In this paper, we present a recurrent neural net technique to provide control actions for nonlinear dynamic systems. In most current neural net control approaches, two nets are usually required. One acts as a system emulator, and the other one is a controller network.
P.P.C. Yip, null Yoh-Han Pao
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Learning temporal sequences in recurrent self-organising neural nets

1997
The learning of temporal sequences is an extremely important component of human and animal behaviour. As well as the motor control involved in routine behaviour such as walking, running, talking, tool use and so on, humans have an apparently remarkable capacity for learning (and subsequently reproducing) temporal sequences. A new connectionist model of
Garry Briscoe, Terry Caelli
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Schema generation in recurrent neural nets for intercepting a moving target

Biological Cybernetics, 2010
The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load.
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