The data set has been consolidated for the task of Human Posture Apprehension. The data set consists of two postures namely -

  1. Sitting and,
  2. Standing,

There are images for each of the postures listed above. The images have a dimension of 53X160 to 1845×4608.

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107 Views

Computer vision can be used by robotic leg prostheses and exoskeletons to improve high-level transitions between different locomotion modes (e.g., level-ground walking to stair ascent) through the prediction of future environmental states. Here we developed the StairNet dataset to support research and development in vision-based automated stair recognition.

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488 Views

Retail Gaze, a dataset for remote gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles.Each image captures the third-person view of the customer and shelves. Location of the gaze point, the Bounding box of the person's head, segmentation masks of the gazed at product areas are provided as annotations.

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212 Views

This dataset was prepared to aid in the creation of a machine learning algorithm that would classify the white blood cells in thin blood smears of juvenile Visayan warty pigs. The creation of this dataset was deemed imperative because of the limited availability of blood smear images collected from the critically endangered species on the internet. The dataset contains 3,457 images of various types of white blood cells (JPEG) with accompanying cell type labels (XLSX).

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472 Views

This dataset contains a collection of videos consisting of satellite imagery augmented with 3D ship models, accompanied by the ships' corresponding AIS data. The intention of this dataset is for detecting dark ships, which are sea vessels acting maliciously, often while spoofing their AIS data. Multiple datasets exist that consist of satellite imagery of ships, however this dataset has the advantage of including each ships' corresponding AIS data. The simulated ships include both normal and anomalous behavior, whether the anomalous behavior is benign or malicious.

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190 Views

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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2457 Views

The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. In this dataset, we present different images and videos for computer vision-based research. The dataset comprises images and videos taken from different sources such as a Drone, a DSLR camera, and a mobile phone camera.

Instructions: 

Please find the attached file for complete description

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840 Views

The DREAM (Data Rang or EArth Monitoring): a multimode database including optics, radar, DEM and OSM labels for deep machine learning purposes.

DREAM, is a multimodal remote sensing database, developed from open-source data.

The database has been created using the Google Earth Engine platform, the GDAL python library; the “pyosm” python package developed by Alexandre Mayerowitz (Airbus, France). If you want to use this dataset in your study, please cite:

Instructions: 

The two datasets are stored in two separate zip files: USA_DREAM_MULTIMODAL.zip and France_DREAM_MULTIMODAL.zip.

After unzip, each directory contain different sub directories with different areas. Each available tile is a 1024x1024 tile GeoTiffs format.

In France:

  • CoupleZZ_S2_date1_date2_XX_YY (Uint16 GeoTiff, UTM, RGB)
  • CoupleZZ_SRTM_V2_XX_YY (Int16 GeoTiff)
  • CoupleZZ_S1_date2_date1_XX_YY (Float32 GeoTiff 2 bands, Red:VV, Green: HV)
  • CoupleZZ_S1moy_date2__dual_XX_YY (Float32 GeoTiff 2 bands, Red:VV, Green: HV)
  • CoupleZZ_OSMraster_XX_YY (Uint8 3 bands RGB GeoTIff)

In the USA There are directories named zoneZ that include following subdirectories

  • optique     contains    *_pauli_x***_y***_optique.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002_optique.tif
  • radar                            *_pauli_x***_y***.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002.tif
  • S1                                 *_pauli_x***_y***_S1moy.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002_S1moy.tif
  • S2                                 *_pauli_x***_y***_S2mosa.tif 
    • Ex: SanAnd_09018_18038_017_180730_L090_CX_01_pauli_x000_y002_S2mosa.tif
  • SRTM                           *__x***_y***_hgt.tif
    • Ex:  SanAnd_09018_18038_017_180730_L090_CX_01__x000_y002_hgt.tif

 

 

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473 Views

Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.

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1240 Views

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

Instructions: 

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

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2774 Views

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