Computer Vision

This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and
unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The
study provides useful insights and establishes connections between the methods, thereby facilitating a profound understand-
ing of the methodological landscape. The methods considered in our study are extensively evaluated in T1w MRI images

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Dataset of images of dragon fruit plants, collected from different media and taken from a dragon fruit field in Rio Branco, Brazil, with a total of 600 images
classified among 300 photos of sick plants, with fish eyes among others and 300 photos of healthy plants. For many of the photos, a simple smartphone 
camera was used to capture the images.

 

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This dataset comprises a diverse array of image files, each captured using either a mobile phone or a camera. The primary subject of these images is experiment reports, reflecting a wide range of experimental scenarios. These images have been taken in various environments, showcasing the flexibility of the dataset in accommodating different shooting conditions. Formatted as JPG documents, the images exhibit variations in size, offering a rich diversity for analysis.

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The JKU-ITS AVDM contains data from 17 participants performing different tasks with various levels of distraction.
The data collection was carried out in accordance with the relevant guidelines and regulations and informed consent was obtained from all participants.
The dataset was collected using the JKU-ITS research vehicle with automated capabilities under different illumination and weather conditions along a secure test route within the

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Nasal Cytology, or Rhinology, is the subfield of otolaryngology, focused on the microscope observation of samples of the nasal mucosa, aimed to recognize cells of different types, to spot and diagnose ongoing pathologies. Such methodology can claim good accuracy in diagnosing rhinitis and infections, being very cheap and accessible without any instrument more complex than a microscope, even optical ones.

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This database contains Synthetic High-Voltage Power Line Insulator Images.

There are two sets of images: one for image segmentation and another for image classification.

The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.

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We present ViSnow: a large image dataset for snow-covered roads in an urban setting. The dataset includes an extensive collection of images from traffic surveillance cameras installed in Montreal, Quebec, Canada, during the winters of 2022 and 2023. ViSnow dataset aims to enable computer vision applications in intelligent transportation and winter road maintenance. ViSnow comprises 294,000 images describing various settings spanning day and night periods, different weather conditions (snow, rain, clear), and multiple urban areas (residential, commercial, industrial).

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In this study, an equatorial telescope with an aperture of 310 mm, which will be installed in Antarctica in 2024, is chosen as the research subject. The Hour angle that the telescope pointing at is in the range of t[0, 360], and that for the declination axis is [-90, 30].The dataset contains around 3,000 images. The overall workflow is to collect images of the telescope in various poses and then collect two of each pose of the telescope from the TCS side of the telescope

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SeaIceWeather Dataset 

This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. To the best of our knowledge, this is the first such publicly available dataset for the sea ice domain. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518 

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DIRS24.v1 presents a dataset captured in campus environment. These images are curated suitably for the utilization in developing perception modules. These modules can be very well employed in Advanced Driver Assistance Systems (ADAS). The images of dataset are annotated in diversified formats such as COCO-MMDetection, Pascal-VOC, TensorFlow, YOLOv7-PyTorch, YOLOv8-Oriented Bounding Box, and YOLOv9.

 

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