The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis.
The dataset contains UAV imagery and fracture interpretation of rock outcrops acquired in Praia das Conchas, Cabo Frio, Rio de Janeiro, Brazil. Along with georeferenced .geotiff images, the dataset contains filtered 500 x 500 .png tiles containing only scenes with fracture data, along with .png binary masks for semantic segmentation and original georeferenced shapefile annotations. This data can be useful for segmentation and extraction of geological structures from UAV imagery, for evaluating computer vision methodologies or machine learning techniques.
In this brief,
The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.
This dataset contains three benchmark datasets as part of the scholarly output of an ICDAR 2021 paper:
Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Robert S. Laramee, Han-Wei Shen, Jian Wu, and C. Lee Giles, Document Domain Randomization for Deep Learning Document Layout Extraction, 16th International Conference on Document Analysis and Recognition (ICDAR) 2021. September 5-10, Lausanne, Switzerland.
The dataset is divided into two sub-folders - 'source' and 'target'. The 'source' folder has a total of 4,080 images of Chest X-rays. The 'target' folder has a total of 4,080 Dual-Energy subtracted images corresponding to the images present in 'source' folder.
We have developed this dataset for the Bangla image caption. Here, we have recorded 500 images with one caption of each. Basically the lifestyle, festivals are mainly focused in this dataset. We have accomplished rice/harvest festivals, snake charming, palanquin, merry-go-round, slum, blacksmith, potter, fisherman, tat shilpo, jamdani, shutki chash, date juice, hal chash, tokai, pohela falgun, gaye holud, etc.
Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. Crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations.
