The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances.

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517 Views

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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165 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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1015 Views

The dataset used in the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos" is provided here. It contains both the segmentation and classification images with manual labels. 

Instructions: 

The dataset contains the ultrasound mouse embyro images with manual labels. For more detail, please look into each subfolder and the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos". Or you can contact the author by zq415@nyu.edu if you have any question about the dataset and the paper. Thanks!

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168 Views

A fundamental building block of any computer-assisted interventions (CAI) is the ability to automatically understand what the surgeons are performing throughout the surgery. In other words, recognizing the surgical activities being performed or the tools being used by the surgeon can be deemed as an essential steps toward CAI. The main motivation for these tasks is to design efficient solutions for surgical workflow analysis. The CATARACTS dataset was proposed in this context. This dataset consists of 50 cataract surgery.

Instructions: 

The dataset consists of 50 videos of cataract surgeries performed in Brest University Hospital. Patients were 61 years old on average (minimum: 23,maximum: 83,standard deviation: 10). Each surgery was recorded in two videos: the microscope video and the surgical tray video. The frame definition was 1920x1080 pixels (full HD resolution) for both types of videos. The frame rate was approximately 30 frames per second for the tool-tissue interaction videos and 50 frames per second for the surgical tray videos. Microscope videos had a duration of 10 minutes and 56 s on average (minimum: 6 minutes 23 s, maximum: 40 minutes 34 s, standard deviation:6 minutes 5 s). Surgical tray videos had a duration of 11 minutes and 3 s on average (minimum: 6 minutes 30 s, maximum: 40 minutes 48 s, standard deviation: 6 minutes 3 s). In total, more than nine hours of surgery (for each video type) have been video recorded. For more details about the dataset and the different tasks proposed, please refer to the links provided in the abstract.

Please note that the evaluation scripts (for the microscope test set) used in the challenges are available now. For CATARACTS 2018, in addition to the videos, we provide the images (images.zip) used in the challenge and the ground truth.

If you use this dataset, please cite the following paper:
Al Hajj, Hassan, et al. "CATARACTS: Challenge on automatic tool annotation for cataRACT surgery." Medical image analysis 52 (2019): 24-41.

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1026 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query.

 

Classes in this dataset:

Airplane

Baseball Diamond

Buildings

Freeway

Golf Course

Harbor

Intersection

Mobile home park

Overpass

Parking lot

River

Runway

Storage tank

Tennis court

Paper

The paper is also available on ArXiv: A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

 

Feel free to cite the author, if the work is any help to you:

 

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

 

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322 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

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68 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Categories:
22 Views

With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query..

Classes in this dataset:

  1. Airplane
  2. Baseball Diamond
  3. Buildings
  4. Freeway
  5. Golf Course
  6. Harbor
  7. Intersection
  8. Mobile home park
  9. Overpass
  10. Parking lot
  11. River
  12. Runway
  13. Storage tank
  14. Tennis court

Paper

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

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26 Views

With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query..

Classes in this dataset:

  1. Airplane
  2. Baseball Diamond
  3. Buildings
  4. Freeway
  5. Golf Course
  6. Harbor
  7. Intersection
  8. Mobile home park
  9. Overpass
  10. Parking lot
  11. River
  12. Runway
  13. Storage tank
  14. Tennis court

Paper

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

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20 Views

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