Convolutional Autoencoder aided loop closure detection for monocular SLAM
Sign Up to like & getrecommendations! Published in 2018 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2018.09.486
Abstract: Abstract A correct loop closure detection is an important component of a robust SLAM (simultaneous localization and mapping) system. Loop closing refers to the process of correctly asserting that a mobile robot has returned to… read more here.
Keywords: closure; loop closure; convolutional autoencoder; autoencoder aided ... See more keywords
Surface Morphology Analysis Using Convolutional Autoencoder in Additive Manufacturing with Laser Engineered Net Shaping
Sign Up to like & getrecommendations! Published in 2021 at "Procedia Manufacturing"
DOI: 10.1016/j.promfg.2021.06.005
Abstract: Abstract Additive manufacturing (AM) has gained increasing popularity in various quality critical applications such as aerospace and healthcare due to its high flexibility in fabricating complex geometries with novel materials. However, the relatively poor layer-wise… read more here.
Keywords: machine; surface; morphology; convolutional autoencoder ... See more keywords
Laser stripe image denoising using convolutional autoencoder
Sign Up to like & getrecommendations! Published in 2018 at "Results in Physics"
DOI: 10.1016/j.rinp.2018.08.023
Abstract: Abstract Convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth… read more here.
Keywords: stripe image; convolutional autoencoder; laser stripe;
Hyperspectral unmixing using deep convolutional autoencoder
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2020.1724346
Abstract: ABSTRACT Hyperspectral Unmixing (HU) estimates the combination of endmembers and their corresponding fractional abundances in each of the mixed pixels in the hyperspectral remote sensing image. In this paper, we address the linear unmixing problem… read more here.
Keywords: unmixing using; deep convolutional; hyperspectral unmixing; convolutional autoencoder ... See more keywords
Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
Sign Up to like & getrecommendations! Published in 2020 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/staa1015
Abstract: In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to… read more here.
Keywords: technique; unsupervised machine; machine learning; convolutional autoencoder ... See more keywords
One-Class Classification in Images and Videos Using a Convolutional Autoencoder With Compact Embedding
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2992804
Abstract: In One-Class Classification (OCC) problems, the classifier is trained with samples of a class considered normal, such that exceptional patterns can be identified as anomalies. Indeed, for real-world problems, the representation of the normal class… read more here.
Keywords: class classification; one class; class; convolutional autoencoder ... See more keywords
Image Retrieval Using Convolutional Autoencoder, InfoGAN, and Vision Transformer Unsupervised Models
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3241858
Abstract: Query by Image Content (QBIC), subsequently known as Content-Based Image Retrieval (CBIR), offers an advantageous solution in a variety of applications, including medical, meteorological, search by image, and other applications. Such CBIR systems primarily use… read more here.
Keywords: autoencoder infogan; infogan vision; convolutional autoencoder; retrieval ... See more keywords
A Band Selection Method With Masked Convolutional Autoencoder for Hyperspectral Image
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3178824
Abstract: Band selection (BS) is an effective means to solve the problems of spectral redundancy and Hughes phenomenon in hyperspectral images (HSIs). However, existing BS methods fail to consider the representativeness, redundancy, and information content of… read more here.
Keywords: band; information; redundancy; convolutional autoencoder ... See more keywords
Convolutional Autoencoder and Transfer Learning for Automatic Virtual Metrology
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3187617
Abstract: To ensure stable processing and high-yield production, high-tech factories (e.g., semiconductor, TFT-LCD) demand product quality total inspection. Generally speaking, sampling inspection only measures a few samples and comes with metrology delay, thus it usually cannot… read more here.
Keywords: convolutional autoencoder; virtual metrology; transfer learning; metrology ... See more keywords
Unsupervised Change Detection Using Convolutional-Autoencoder Multiresolution Features
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3140404
Abstract: The use of deep learning (DL) methods for change detection (CD) is currently dominated by supervised models that require a large number of labeled samples. However, these samples are difficult to acquire in the multitemporal… read more here.
Keywords: change detection; feature maps; convolutional autoencoder; multiresolution ... See more keywords
Field experiment on a PSC-I bridge for convolutional autoencoder-based damage detection
Sign Up to like & getrecommendations! Published in 2020 at "Structural Health Monitoring"
DOI: 10.1177/1475921720926267
Abstract: In this study, a field experiment was performed for damage detection on a PSC-I bridge based on a convolutional autoencoder using the damage detection approach proposed in a previous study by the authors. The field… read more here.
Keywords: autoencoder based; damage detection; detection; convolutional autoencoder ... See more keywords