This contains data corresponding to the paper Multi-Resolution Data Fusion for Super-Resolution Imaging. 

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Without publicly available dataset, specifically in handwritten document recognition (HDR), we cannot make a fair and/or reliable comparison between the methods. Considering HDR, Indic script’s document recognition is still in its early stage compared to others such as Roman and Arabic. In this paper, we present a page-level handwritten document image dataset (PHDIndic_11), of 11 official Indic scripts: Bangla, Devanagari, Roman, Urdu, Oriya, Gurumukhi, Gujarati, Tamil, Telugu, Malayalam and Kannada.

Instructions: 

See the attached pdf in documentation for more details about the dataset and benchmark results. Cite the following paper if you use the dataset for research purpose.

Obaidullah, S.M., Halder, C., Santosh, K.C. et al. PHDIndic_11: page-level handwritten document image dataset of 11 official Indic scripts for script identification. Multimed Tools Appl 77, 1643–1678 (2018). https://doi.org/10.1007/s11042-017-4373-y

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An offline handwritten signature dataset from two most popular scripts in India namely Roman and Devanagari is proposed here. 

Instructions: 

Writer identification dataset availability on Indic scripts is a major issue to carry forward research in this domain. Devanagari and Roman are two most popular and widely used scripts of India. We have a total of 5433 signatures of 126 writers, out of which 3929 signatures from 80 writers in Roman script and 1504 signatures from 46 writers in Devanagari scripts. Script-wise per writer 49 signatures from Roman and 32 signatures from Devanagari are considered making an average of 43 signatures per writer on whole dataset. We have reported a benchmark results on this dataset for writer identification task using a lightweight CNN architecture. Our proposed method is compared with state-of-the-art handcrafted feature based method such as gray level co-occurrence matrix (GLCM), Zernike moments, histogram of oriented gradients (HOG), local binary pattern (LBP), weber local descriptor (WLD), gabor wavelet transform (GWT) and it outperforms. In addition, few well known CNN arechitechture is also compared with the proposed method and it shows comparable performance. 

User guidance: The images are available in .jpg format with 24 bit color. The dataset is freely available for research work. Cite the following paper while using the dataset

Sk Md Obaidullah, Mridul Ghosh, Himadri Mukherjee, Kaushik Roy and Umapada Pal “Automatic Signature-based Writer Identification in Mixed-script Scenarios”, in 16th International Conference on Document Analysis and Recognition (ICDAR 2021), Lussane, Switzerland, 2021

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<p>The dataset comprises 2035 images from 14 different software architectural patterns (100+ images each), viz., Broker, Client Server, Microkernel, Repository, Publisher-Subscriber, Peer-to-Peer, Event Bus, Model View Controller, REST, Layered, Presentation Abstraction Controller, Microservices, and Space-based patterns.</p>

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Supplementary materials for manuscript: Self-absorption Correction in X-ray Fluorescence Computed Tomography with Deep Convolutional Neural Network, 

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Dataset including over 40,000 generated images of malicious binaries for malware classification in machine learning as outlined in NARAD - A Novel Auto-learn Real-time Fuzzy Machine Learning Anomaly Detection and Classification System.

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Medical Symptoms are sometimes very tricky to analyse in real time as it takes time for example, first to detect the symptoms then to perform some tests and finally coming to a solution. This process can be eliminated and lot of time can be saved by introducing the concept of Deep learning. CNNs create a network for extracting the features of a given image in order to evaluate the image based on the conditions required. This property of the CNN is used as a certain advantage in order to detect the symptoms based on the type of X-ray images provided.

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The Objects Mosaic Hyperspectral Database contains 10,666 hyperspectral cubes of size 256x256x29 in the 420-700nm spectral range. This original hyperspectral database of real objects was experimentally acquired as described in the paper "SHS-GAN: Synthetic enhancement of a natural hyperspectral database", by J. Hauser, G. Shtendel, A. Zeligman, A. Averbuch, and M. Nathan, in the IEEE Transactions on Computational Imaging.

In addition, the database contains the SHS-GAN algorithm, which enables to generate synthetic database of hyperspectral images. 

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The dataset consists of 751 videos, each containing the performance one of the handball actions out of 7 categories (passing, shooting, jump-shot, dribbling, running, crossing, defence). The videos were manually extracted from longer videos recorded in handball practice sessions. 

Instructions: 

The directory scenes/ contains the videos in mp4 format with actions of interest performed in context of other players present in the scene. The files are arranged in subdirectories according to the action class of the action of interest. The directory actions/ contains the videos of performances of actions by single players isolated from the videos in scenes directory. The files are arranged in subdirectories according to the performed action class. Files are named so that the beginning of the name matches the original video from which the action is extracted. The directory player_detections/ contains the object detections for each frame in the videos.

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Of late, efforts are underway to build computer-assisted diagnostic tools for cancer diagnosis via image processing. Such computer-assisted tools require capturing of images, stain color normalization of images, segmentation of cells of interest, and classification to count malignant versus healthy cells. This dataset is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer. The images are provided after stain color normalization.

Instructions: 

IMPORTANT:

If you use this dataset, please cite below publications-

  1. Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788. (2020 IF: 11.148)
  2. Shiv Gehlot, Anubha Gupta and Ritu Gupta, "EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1389-1393.
  3. Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta, and Rahul Duggal, "PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma," PLoS ONE 13(12): e0207908, Dec 2018. DOI: 10.1371/journal.pone.0207908
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