Suchergebnisse - "Fully convolutional encoder–decoder networks"

Optimizing the Hyperparameters of Fully Convolutional Encoder–Decoder Networks for SAR Image Segmentation

Autoren: Yuanyue Liu Jin Zhao Jianchao Fan et al.

Quelle: IEEE Geoscience and Remote Sensing Letters. 21:1-5

Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks

Autoren: Erees Queen B. Macabebe Felan Carlo C. Garcia

Quelle: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. :2075-2078

Schlagwörter: 13. Climate action, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy, 12. Responsible consumption

Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks

Autoren: Andreas Uhl Ehsaneddin Jalilian

Quelle: Advances in Computer Vision and Pattern Recognition ISBN: 9783319616568

Zugangs-URL: https://rd.springer.com/chapter/10.1007/978-3-319-61657-5_6
https://link.springer.com/chapter/10.1007%2F978-3-319-61657-5_6
https://uni-salzburg.elsevierpure.com/en/publications/iris-segmentation-using-fully-convolutional-encoder-decoder-netwo

Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image

Autoren: Grard, Matthieu Dellandréa, Emmanuel Chen, Liming

Weitere Verfasser: Grard, Matthieu Dellandréa, Emmanuel Chen, Liming

Quelle: International Journal of Computer Vision. 128:1331-1359

Schlagwörter: Domain adaptation, FOS: Computer and information sciences, Computer Science - Machine Learning, Synthetic data, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 02 engineering and technology, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Fully convolutional encoder-decoder networks, Instance boundary and occlusion detection, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/1906.07480
http://arxiv.org/abs/1906.07480
https://link.springer.com/article/10.1007/s11263-020-01323-0
http://ui.adsabs.harvard.edu/abs/2019arXiv190607480G/abstract
https://dblp.uni-trier.de/db/journals/ijcv/ijcv128.html#GrardDC20
https://doi.org/10.1007/s11263-020-01323-0
https://hal.archives-ouvertes.fr/hal-02151828
https://arxiv.org/abs/1906.07480
https://arxiv.org/pdf/1906.07480v3

DRSegNet: A cutting-edge approach to Diabetic Retinopathy segmentation and classification using parameter-aware Nature-Inspired optimization.

Autoren: Kamal, Sundreen Asad Du, Youtian Khalid, Majdi et al.

Quelle: PLoS ONE; 12/5/2024, Vol. 19 Issue 12, p1-40, 40p

Schlagwörter: EXTREME learning machines, GENERATIVE adversarial networks, GREY Wolf Optimizer algorithm, DIABETIC retinopathy, IMAGE analysis

SABA: Scale-adaptive Attention and Boundary Aware Network for real-time semantic segmentation

Autoren: Luo, Huilan Liu, Chunyan Shark, Lik

Index Begriffe: I270 - Intelligent & expert systems, Article, PeerReviewed

URL: https://clok.uclan.ac.uk/id/eprint/55285/1/55285%20Luo%20et%20al.%20AAM.pdf
https://clok.uclan.ac.uk/id/eprint/55285/
https://doi.org/10.1016/j.eswa.2025.127680
https://clok.uclan.ac.uk/id/eprint/55285
https://doi.org/10.1016/j.eswa.2025.127680
doi:10.1016/j.eswa.2025.127680

Employing Attention-Based Learning For Medical Image Segmentation

Additional Titles: Användande av uppmärksamhetsbegrepp för medicinsk bildsegmentering

Autoren: Ferles, Alexandros

Index Begriffe: Computer and Information Sciences, Data- och informationsvetenskap, Student thesis, info:eu-repo/semantics/bachelorThesis, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-271185
TRITA-EECS-EX

Post-Heuristic Cancer Segmentation Refinement over MRI Images and Deep Learning Models.

Autoren: Christakakis, Panagiotis Protopapadakis, Eftychios

Quelle: AI; Sep2025, Vol. 6 Issue 9, p212, 38p

Schlagwörter: DEEP learning, GLIOMAS, MAGNETIC resonance imaging, TUMOR classification, IMAGE enhancement (Imaging systems)

Late reverberation suppression using U-nets

Autoren: León, Diego Tobar, Felipe

Index Begriffe: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound, text

URL: http://arxiv.org/abs/2110.02144

A Quantitative Comparison between Focal Loss and Binary Cross-Entropy Loss in Brain Tumor Auto-Segmentation Using U-Net.

Autoren: Shafiei Neyestanak, Mahdi Jahani, Hamid Khodarahmi, Mohsen et al.

Quelle: Journal of Biostatistics & Epidemiology; 2025, Vol. 11 Issue 1, p15-35, 21p

Schlagwörter: CONVOLUTIONAL neural networks, CANCER diagnosis, BRAIN tumors, CONTRAST media, MAGNETIC resonance imaging

A new diffusion method for blind image denoising.

Autoren: Zhu, Yonggui Chen, Yaling

Quelle: Pattern Analysis & Applications; Dec2024, Vol. 27 Issue 4, p1-14, 14p

A Comprehensive Evaluation of Iris Segmentation on Benchmarking Datasets.

Autoren: Sumi, Mst Rumana Das, Priyanka Hossain, Afzal et al.

Quelle: Sensors (14248220); Nov2024, Vol. 24 Issue 21, p7079, 19p

Schlagwörter: BIOMETRIC identification, DEEP learning, BIOMETRY, ALGORITHMS, SCARCITY

Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet.

Autoren: Yadav, Dhirendra Prasad Sharma, Bhisham Chauhan, Shivank et al.

Quelle: Sustainability (2071-1050); Jun2024, Vol. 16 Issue 11, p4409, 21p

New attention-gated residual deep convolutional network for accurate lung segmentation in chest x-rays.

Autoren: Boudoukhani, Nesrine Elberrichi, Zakaria Oulladji, Latefa et al.

Quelle: Evolving Systems; Jun2024, Vol. 15 Issue 3, p919-938, 20p

Enhancing the accuracy of prototype learning in road anomaly segmentation by adding adversarial perturbations to data.

Autoren: Lin, Yu-Sian Lin, Chow-Sing

Quelle: Multimedia Tools & Applications; May2024, Vol. 83 Issue 18, p55627-55640, 14p

Schlagwörter: ARTIFICIAL neural networks, MACHINE learning, TRAFFIC accidents, DRIVERLESS cars, PROTOTYPES

Design and development of an integrated approach towards detection and tracking of iris using deep learning.

Autoren: Ahmad, Naseem Yadav, Kuldeep Singh Kirupakaran, Anish Monsley et al.

Quelle: Multimedia Tools & Applications; May2024, Vol. 83 Issue 15, p44661-44683, 23p

Schlagwörter: IRIS (Eye), EYE tracking, DEEP learning, OPHTHALMIC surgery, SURGICAL robots, BIOMETRIC identification

Iris-LAHNet: a lightweight attention-guided high-resolution network for iris segmentation and localization.

Autoren: Yan, Yue Wang, Qi Zhu, Hegui et al.

Quelle: Multimedia Systems; Apr2024, Vol. 30 Issue 2, p1-19, 19p

An iris segmentation scheme based on bendlets.

Autoren: Aghazadeh, Nasser Abbasi, Mandana Noras, Parisa

Quelle: Signal, Image & Video Processing; Apr2024, Vol. 18 Issue 3, p2683-2693, 11p

3D Convolutional Long Short-Term Encoder-Decoder Network for Moving Object Segmentation.

Autoren: Turker, Anil Eksioglu, Ender Mete

Quelle: Computer Science & Information Systems; Jan2024, Vol. 21 Issue 1, p363-378, 16p

A deep learning-based approach for fault diagnosis of current-carrying ring in catenary system.

Autoren: Chen, Yuwen Song, Bin Zeng, Yuan et al.

Quelle: Neural Computing & Applications; Nov2023, Vol. 35 Issue 33, p23725-23737, 13p

Schlagwörter: FAULT diagnosis, CATENARY, DEEP learning, INTERNET of things, DIAGNOSIS, PROBLEM solving