Results 61 to 70 of about 54,400 (268)
Advanced Robotics Research, EarlyView.Embedded flexible sensing technologies advance underwater soft robotics, yet most systems still suffer from hysteresis and limited perceptiveness. Instead, vision‐based tactile sensors provide reliable and rapid feedback essential for complex underwater tasks.
Qiyi Zhang +5 more
wiley +1 more source
SensorsConvolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception ...
Ravi Raj, Andrzej Kos
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CNN+CNN: Convolutional Decoders for Image Captioning
, 2018 Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field.
Wang, Qingzhong, Chan, Antoni B.
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Advanced Robotics Research, EarlyView.Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley +1 more source
Brain Sciences, 2020 Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks.
Yibo Cui +5 more
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, 2016 Ted Turner launched Cable News Network (CNN), the world’s first twenty-four-hour news channel, in 1980. Broadcast network journalists and media pundits initially dismissed CNN as the “Chicken Noodle Network,” pointing to its poor production values and small audience share.
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Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling
Advanced Robotics Research, EarlyView.A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang +5 more
wiley +1 more source
2017 IEEE International Conference on Computer Vision (ICCV), 2017 The deep Convolutional Neural Network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following basic principles such as increasing the depth and constructing highway connections, researchers have manually designed a lot of fixed network structures and verified their effectiveness. In this paper, we discuss the possibility of
Xie, Lingxi, Yuille, Alan
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Advanced Theory and Simulations, EarlyView.This study highlights the potential of deep learning, particularly Convolutional Neural Networks (CNNs), for predicting the photovoltaic performance of organic solar cells. By leveraging 2D images representing donor/acceptor molecular pairs, the model accurately estimates key performance indicators proving that this image‐based approach offers a fast ...
Khoukha Khoussa +2 more
wiley +1 more source
P-CNN: Percept-CNN for semantic segmentation
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & VisualizationThe task of image segmentation remains a fundamental challenge, in the field of computer vision. Convolutional Neural Networks (CNNs) have achieved significant success in this field, yet there are some limitations in the conventional approach. The process of accurate, pixel-wise image annotation is time-consuming, as well as requires more human effort.
Deepak Hegde, G. N. Balaji
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