Synthetic image data set using a generative model with explicit control over the head pose. HPGEN offers a promising solution to address data set bias in the head pose estimation as current benchmarks suffer from a limited number of images, imbalanced data distributions, the high cost of annotation, and ethical concerns.
NICU-Care is a high-quality video dataset designed to support visual recognition tasks in Neonatal Intensive Care Unit (NICU) scenarios, including nursing action recognition, object detection, and semantic segmentation. It was constructed in a standardized simulated NICU environment, capturing multi-view RGB videos of professional nurses performing six types of routine caregiving procedures on simulated infants. The dataset provides fine-grained temporal annotations and pixel-level segmentation masks for key objects like nurse hands, medical tools, and infant body parts.
This dataset aims to support research on temporal segmentation of the Timed Up and Go (TUG) test using a first-person wearable camera. The data collection includes a training set of 8 participants and a test set of 60 participants. Among the 8 participants, the test was completed at both a normal walking pace and a simulated slower walking pace to mimic elderly movement patterns. The 60 participants were randomly divided into two groups: one group completed the test at a normal walking pace, and the other group simulated slower walking speed to mimic elderly movement patterns.
Making images unlearnable through imperceptible perturbations is to prevent unauthorized image scraping from training deep neural networks (DNNs). Most existing methods for breaking these unlearnable data focus on applying image transformation techniques to disrupt the added perturbations, with limited attention given to modifying the classification tasks (target classes) of DNNs as an alternative approach.
Digital microfluidics are a unique technique for operation of nano-to-micro liter droplets based on electrowetting on dielectric. It has great application potential in the field on clinic diagnosis, life science and environment monitoring. Due to the fast droplet moving speed and high degree of freedom for droplet manipulation, it is urgent to develop automated and intelligent approaches for droplet monitoring and control.
Introduction
Welcome to the new challenging event-based multi-object tracking dataset (DSEC-MOT) repository. Our goal is to provide a challenging and diverse event-based MOT dataset with various real-world scenarios to facilitate the objective and comphrehensive evaluation of event-based multi-object tracking algorithms. This dataset, built upon DSEC, contains a variety of traffic entities and complex scenarios, aiming to address the current lack of event-based MOT datasets.
This study utilizes the open-source datasets FAIR1M and HRSC2016 as foundational resources to construct an optical remote sensing image dataset for rotated ship target detection. The dataset encompasses nine ship categories: Dry-Cargo-Ship, Engineering-Ship, Fishing-Boat, Motorboat, Tugboat, Passenger-Ship, Warship, Liquid-Cargo-Ship, and Other-Ship.
Attached Image data set from combined OCT-SLO is used to train AI models and identify features to maximize quality of data set to adjust MZI reference arm, PMT Voltage of Liquid Lens and location of object. Why adjustment is needed is explained below:
The NEU-DET dataset is a set of images characterized by surface defects on hot rolled steel strip. These defects are classified into six categories: cracks (cr), inclusions (in), patches (pa), pitted surfaces (ps), rolled scales (rs) and scratches (sc). The dataset contains 300 grayscale images for each category, for a total of 1800 images, each of which is 200×200 pixels in size.
The painting style data sets were constructed by searching, selecting and collecting the public painting works on the internet, treating the painting style and artists' names as keywords. The data set collected 750 painting works in all, including five kinds of styles. They were receptively Cubism, Op Art, Color Field Painting, Post Impressionism and Rococo.
