Results 291 to 300 of about 1,980,897 (317)

Pulse-Net: Dynamic Compression of Convolutional Neural Networks

2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019
Convolutional Neural Networks (CNNs) are used in a range of computer vision tasks, with state-of-the-art CNNs such as AlexNet and VGG16 constructed using a large number of parameter and multiply-add operations (MACs). These tasks require high computational power and high energy requirements to run the CNNs, making them unsuitable for deployment on In ...
David Browne, Michael Giering, Steven D. Prestwich   +2 more
openaire   +1 more source

W–net: A Convolutional Neural Network for Retinal Vessel Segmentation

2021
In this paper we propose a method for retinal vessel segmentation based on a multi-stage deep convolutional neural network with short connections. The proposed method is a two-stage application of an improved U–net architecture. In the first stage, a probability score for the vascular structure presence is computed from a set of random patches taken ...
Alan Reyes-Figueroa, Mariano Rivera
openaire   +2 more sources

AMI-Net: Convolution Neural Networks With Affine Moment Invariants

IEEE Signal Processing Letters, 2018
Affine moment invariant (AMI) is a kind of hand-crafted image feature, which is invariant to affine transformations. This property is precisely what the standard convolution neural network (CNN) is difficult to achieve. In this letter, we present a kind of network architecture to introduce AMI into CNN, which is called AMI-Net.
You Hao, Qi Li, Hanlin Mo, He Zhang 0012, Hua Li 0009   +4 more
openaire   +2 more sources

Recognition of traffic signs by convolutional neural nets for self-driving vehicles

Int. J. Knowl. Based Intell. Eng. Syst., 2018
In this paper, a comprehensive Convolutional Neural Network (CNN) based classifier “WAF-LeNet” is proposed and developed to be used in traffic signs recognition and identification as an empowerment of autonomous driving technologies.
W. Farag
semanticscholar   +1 more source

CIASM-Net: A Novel Convolutional Neural Network for Dehazing Image

2020 5th International Conference on Computer and Communication Systems (ICCCS), 2020
When light propagates in the medium such as haze, the image information collected by the imaging sensor is seriously degraded due to the scattering of particles, which greatly limits the application value of the image. In this paper, a novel convolutional neural network model called CIASM-Net is proposed to implement image dehazing.
Wen Qian, Chao Zhou 0007, Dengyin Zhang
openaire   +2 more sources

U-Net Based Convolutional Neural Network for Skeleton Extraction

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019
Skeletonization is a process aimed to extract a line-like object shape representation, skeleton, which is of great interest for optical character recognition, shape-based object matching, recognition, biomedical image analysis, etc.. Existing methods for skeleton extraction are typically based on topological, morphological or distance transform and are
Oleg Panichev, Alona Voloshyna
openaire   +1 more source

Elasto-Net: An HDL Conversion Framework For Convolutional Neural Networks

2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018
Hardware solutions for Convolutional Neural Networks (CNN) have emerged in the recent wake of their success in image classification. Although CNNs are effective in classifying images, they can be highly complex to implement in hardware. CNNs come in many shapes and sizes.
Anaam Ansari, Tokunbo Ogunfunmi
openaire   +1 more source

A Novel Approach for Handwriting Recognition in Malayalam Manuscripts using Contour Detection and Convolutional Neural Nets

International Conference on Advances in Computing, Communications and Informatics, 2018
Neural Networks is a hot area of research for various kind of pattern recognition. Hand writing recognition is a domain coming under the field of pattern recognition which had captured a high research interest during the last 10 years.
Dhanya Sudarsan, Shelbi Joseph
semanticscholar   +1 more source

Network Inversion of Convolutional Neural Nets (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios.
Pirzada Suhail, Amit Sethi
openaire   +1 more source

Optical ReLUs for Holographic Convolutional Neural Nets

Frontiers in Optics + Laser Science 2025 (FiO, LS)
The array of adaptive interconnections needed for multilayer convolutional neural networks can be implemented and learned with all-optical back-propagation using thick Fourier-plane dynamic holographic photorefractive crystals and a phase sensitive bidirectional optical binary ReLU switch.
Kelvin Wagner, Sean McComb, Diego Olaya, Doug McKnight, Tim Slagle   +4 more
openaire   +1 more source