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Found 18 papers, 2 papers with code
A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification
Classification of very high-resolution (VHR) aerial remote sensing (RS) images is a well-established research area in the remote sensing community as it provides valuable spatial information for decision-making.
Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification
Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems.
Neonatal Face and Facial Landmark Detection from Video Recordings
This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign estimation, pain assessment, sleep-wake classification, and jaundice detection.
Monkeypox virus detection using pre-trained deep learning-based approaches
In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection.
Recent Advances in Scene Image Representation and Classification
For this, we, first, devise the taxonomy using the seminal existing methods proposed in the literature to this date {using deep learning (DL)-based, computer vision (CV)-based, and search engine (SE)-based methods}.
Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features
Nevertheless, owing to the limited number of tokens/words in each tweet, it is always crucial to capture multiple information associated with them for their better understanding.
Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds
Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications.
Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds
The median processing time for the sound separation of a 10s recording was found to be 28. 3s for NMCF and 342ms for NMF.
Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications
In this study, a new method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time and to assess the effect of signal quality on vital sign estimation.
Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation
In particular, physiological monitoring methods such as Electroencephalography (EEG) are intrusive to drivers; while behavioural monitoring is least robust, affected by external factors such as lighting, as well as being subject to privacy concerns.
Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model
Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care.
New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis
General deep features may not be appropriate to represent chest x-rays as they have a few semantic regions.
Content and Context Features for Scene Image Representation
Existing research in scene image classification has focused on either content features (e. g., visual information) or context features (e. g., annotations).
Scene Image Representation by Foreground, Background and Hybrid Features
In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images.
HDF: Hybrid Deep Features for Scene Image Representation
In this paper, we propose a novel type of features -- hybrid deep features, for scene images.
Unsupervised Deep Features for Privacy Image Classification
Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information.
Tag-based Semantic Features for Scene Image Classification
In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web.
Indoor image representation by high-level semantic features
Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e. g., object association).
