A novel two-stream saliency image fusion CNN architecture for person re-identification
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Systems"
DOI: 10.1007/s00530-017-0583-4
Abstract: Background interference, which arises from complex environment, is a critical problem for a robust person re-identification (re-ID) system. The background noise may significantly compromise the feature learning and matching process. To reduce the background interference,… read more here.
Keywords: saliency image; image; cnn architecture; person ... See more keywords
A Meta-Heuristic Automatic CNN Architecture Design Approach Based on Ensemble Learning
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3054117
Abstract: Convolutional Neural Networks (CNNs) models achieve a dominant performance on immense domains. There are CNNs that come in numerous topologies of different sizes. This field’s challenge is to design the right CNN architecture for a… read more here.
Keywords: design approach; based ensemble; architecture; design ... See more keywords
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-Based EEG Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3204758
Abstract: Brain–computer interface (BCI) is a technology that allows users to control computers by reflecting their intentions. Electroencephalogram (EEG)–based BCI has been developed because of its potential, however, its decoding performance is still insufficient to apply… read more here.
Keywords: decoding performance; performance; cnn architecture; rethinking cnn ... See more keywords
Memory-Reduced Network Stacking for Edge-Level CNN Architecture With Structured Weight Pruning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Journal on Emerging and Selected Topics in Circuits and Systems"
DOI: 10.1109/jetcas.2019.2952137
Abstract: This paper presents a novel stacking and multi-level indexing scheme for convolutional neural networks (CNNs) used in energy-limited edge-level systems. Basically, the proposed scheme offers multiple accuracy modes by adopting a structured weight pruning method… read more here.
Keywords: edge level; structured weight; cnn architecture; cnn ... See more keywords
A CNN Architecture for Learning Device Activity From MMV
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3091841
Abstract: Device activity detection has been extensively investigated for grant-free massive machine-type communications. Instead of using deep Multi-Layer Perception (MLP) networks, this letter proposes a novel convolutional neural network (CNN) architecture for learning device activity from… read more here.
Keywords: architecture learning; device activity; cnn architecture; device ... See more keywords
Brain Cognition-Inspired Dual-Pathway CNN Architecture for Image Classification.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3237962
Abstract: Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and… read more here.
Keywords: cnn architecture; cnn; dual pathway; image ... See more keywords
A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment
Sign Up to like & getrecommendations! Published in 2024 at "Computational Intelligence"
DOI: 10.1111/coin.12631
Abstract: With the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image… read more here.
Keywords: image; fusion; source; cnn architecture ... See more keywords
Optimized fast GPU implementation of robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction
Sign Up to like & getrecommendations! Published in 2019 at "PLoS ONE"
DOI: 10.1371/journal.pone.0223315
Abstract: Background Robust Artificial-neural-networks for k-space Interpolation (RAKI) is a recently proposed deep-learning-based reconstruction algorithm for parallel imaging. Its main premise is to perform k-space interpolation using convolutional neural networks (CNNs) trained on subject-specific autocalibration signal… read more here.
Keywords: cnn architecture; neural networks; space interpolation; raki ... See more keywords
Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture
Sign Up to like & getrecommendations! Published in 2024 at "Diagnostics"
DOI: 10.3390/diagnostics14151691
Abstract: Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for… read more here.
Keywords: top hat; cnn architecture; hybrid feature; feature ... See more keywords