Cargando…

Depth in convolutional neural networks solves scene segmentation

Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural...

Descripción completa

Detalles Bibliográficos
Autores principales:	Seijdel, Noor, Tsakmakidis, Nikos, de Haan, Edward H. F., Bohte, Sander M., Scholte, H. Steven
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2020
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406083/ https://www.ncbi.nlm.nih.gov/pubmed/32706770 http://dx.doi.org/10.1371/journal.pcbi.1008022

Ejemplares similares

Low-level image statistics in natural scenes influence perceptual decision-making
por: Seijdel, Noor, et al.
Publicado: (2020)

Scene complexity modulates degree of feedback activity during object detection in natural scenes
por: Groen, Iris I. A., et al.
Publicado: (2018)

Sparse Computation in Adaptive Spiking Neural Networks
por: Zambrano, Davide, et al.
Publicado: (2019)

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks
por: Sörensen, Lynn K. A., et al.
Publicado: (2023)

Editorial: Artificial Neural Networks as Models of Neural Information Processing
por: van Gerven, Marcel, et al.
Publicado: (2017)

The contribution of object identity and configuration to scene representation in convolutional neural networks
por: Tang, Kevin, et al.
Publicado: (2022)

An Improved Convolutional Neural Network-Based Scene Image Recognition Method
por: Wang, Pinhe, et al.
Publicado: (2022)

Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes
por: Liu, Sheng, et al.
Publicado: (2013)

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification
por: Yu, Donghang, et al.
Publicado: (2020)

Multi-Object Detection in Security Screening Scene Based on Convolutional Neural Network
por: Sun, Fan, et al.
Publicado: (2022)

Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks
por: Daou, Andrea, et al.
Publicado: (2023)

Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks
por: Zheng, Yajing, et al.
Publicado: (2021)

Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification
por: Gopalapillai, Radhakrishnan, et al.
Publicado: (2021)

Convolutional neural networks for brain tumour segmentation
por: Bhandari, Abhishta, et al.
Publicado: (2020)

Sunflower seeds classification based on sparse convolutional neural networks in multi-objective scene
por: Jin, Xiaowei, et al.
Publicado: (2022)

Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling
por: Lee, Hongjae, et al.
Publicado: (2021)

Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network
por: Lin, Xiao, et al.
Publicado: (2019)

Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network
por: Ye, Mengqi, et al.
Publicado: (2021)

Plasticity versus chronicity: Stable performance on category fluency 40 years post‐onset
por: de Haan, Edward H. F., et al.
Publicado: (2019)

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging
por: Valvano, Gabriele, et al.
Publicado: (2019)

Convolutional neural network for automated mass segmentation in mammography
por: Abdelhafiz, Dina, et al.
Publicado: (2020)

Analysis of Convolutional Neural Network Segmentation Algorithm of Adenomyoma
por: Jin, Yi, et al.
Publicado: (2022)

Region Convolutional Neural Network for Brain Tumor Segmentation
por: Pitchai, R., et al.
Publicado: (2022)

Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation
por: Nanni, Loris, et al.
Publicado: (2023)

Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks
por: Stember, J. N., et al.
Publicado: (2019)

Automatic worm detection to solve overlapping problems using a convolutional neural network
por: Mori, Shinichiro, et al.
Publicado: (2022)

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks †
por: Wang, Huai-Mu, et al.
Publicado: (2021)

Depth Estimation from Light Field Geometry Using Convolutional Neural Networks †
por: Han, Lei, et al.
Publicado: (2021)

Retracted: Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network
por: Intelligence and Neuroscience, Computational
Publicado: (2023)

Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images
por: Chen, Chen, et al.
Publicado: (2020)

Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification
por: Zhang, Jianming, et al.
Publicado: (2020)

Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?
por: De Cesarei, Andrea, et al.
Publicado: (2021)

Knowledge distillation with ensembles of convolutional neural networks for medical image segmentation
por: Noothout, Julia M. H., et al.
Publicado: (2022)

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model
por: Sörensen, Lynn K. A., et al.
Publicado: (2022)

Automated Training of Deep Convolutional Neural Networks for Cell Segmentation
por: Sadanandan, Sajith Kecheril, et al.
Publicado: (2017)

Fully automatic wound segmentation with deep convolutional neural networks
por: Wang, Chuanbo, et al.
Publicado: (2020)

Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation
por: Luan, Shunyao, et al.
Publicado: (2021)

Accurate Tumor Segmentation via Octave Convolution Neural Network
por: Wang, Bo, et al.
Publicado: (2021)

Transfer learning for data‐efficient abdominal muscle segmentation with convolutional neural networks
por: McSweeney, Dónal M., et al.
Publicado: (2022)

Rapid invisible frequency tagging (RIFT): a promising technique to study neural and cognitive processing using naturalistic paradigms
por: Seijdel, Noor, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_occfq6qe8r5hcsmr8e2dea82mj