Image Fusion

In order to contribute to the development of automatic methods for the detection of bacilli, TBimages is an image dataset composed of two subsets: TbImages_SS1 contains 10 images per field, of different focal depths, and aims to support the definition of autofocus metrics and also the development of extended focus imaging methods to facilitate the detection of bacilli in smear microscopy imaging.   TbImages_SS2  aims to support the development of automatic bacilli detection.


The addy Doctor dataset contains 16,225 labeled paddy leaf images across 13 classes (12 different paddy diseases and healthy leaves). It is the largest expert-annotated visual image dataset to experiment with and benchmark computer vision algorithms. The paddy leaf images were collected from real paddy fields using a high-resolution (1,080 x 1,440 pixels) smartphone camera. The collected images were carefully cleaned and annotated with the help of an agronomist.


Drone based wildfire detection and modeling methods enable high-precision, real-time fire monitoring that is not provided by traditional remote fire monitoring systems, such as satellite imaging. Precise, real-time information enables rapid, effective wildfire intervention and management strategies. Drone systems’ ease of deployment, omnidirectional maneuverability, and robust sensing capabilities make them effective tools for early wildfire detection and evaluation, particularly so in environments that are inconvenient for humans and/or terrestrial vehicles.


A Chinese character gesture dataset for 8 Chinese characters ( “国”,“图”,“木”,“工”,“口”,“中”,“国”,“人” ) .Datasets were collected in three different environments.


The C3I Synthetic Face Depth Dataset consists of 3D virtual human models and 2D rendered RGB and GT depth images in zipped version into two folders for male and female.


Akshi IMAGE, a new Indian ethnicity retinal fundus image database, has been established for the evaluation of computer-assisted glaucoma prescreening methods. ‘Akshi’ is a Sanskrit word for the ‘Eye’ and ‘IMAGE’ is an acronym for IISc-MAHE Glaucoma Evaluation database. This database is a result of an interdisciplinary collaboration between Indian Institute of Science (IISc) and Manipal Academy of Higher Education (MAHE). The database consists of retinal color fundus images acquired using three different devices.


The presented dataset is a supplementary material to the paper [1] and it represents the X-Ray Energy Dispersive (EDS)/ Scanning Electron Microscopy (SEM) images of a shungite-mineral particle. Pansharpening is a procedure for enhancing the spatial resolution of a multispectral image, here the EDS individual bands, with a high-spatial panchromatic image, here the SEM image. Pansharpening techniques are usually tested with remote sensed data, but the procedures have been efficient in close-range MS-PAN pairs as well [3].


The Contest: Goals and Organization

The 2022 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee, aims to promote research on semi-supervised learning. The overall objective is to build models that are able to leverage a large amount of unlabelled data while only requiring a small number of annotated training samples. The 2022 Data Fusion Contest will consist of two challenge tracks:

Track SLM:Semi-supervised Land Cover Mapping

Last Updated On: 
Mon, 03/07/2022 - 04:41

Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.