…The PH² dataset has been developed for research and benchmarking purposes, in order to facilitate comparative studies on both segmentation and classification algorithms of dermoscopic images.
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…It's provided as a large-scale benchmark for food image segmentation.
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…In this dataset, we report the extracted segments used for an analysis of R peak detection algorithms during high intensity exercise. For each subject, 5 segments of 20 s were extracted from the ECG recordings and chosen based on different phases of the maximal exercise test (i.e., before and after the so-called second ventilatory threshold seg1 --> [VT2-50,VT2-30] seg2 --> [VT2+60,VT2+80] seg3 --> [VO2max-50,VO2max-30] seg4 --> [VO2max-10,VO2max+10] seg5 --> [VO2max+60,VO2max+80] The R peak locations were manually annotated in all segments Only segment 5 of subject 9 could not be annotated since there was a problem with the input signal. So, the total number of segments extracted were 20 * 5 - 1 = 99. Format of the extracted dataset The dataset is divided in two main folders: The folder `ecg_segments/` contains the ECG signals saved in two formats, `.csv` and `.mat`.
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…Researchers are encouraged to test their segmentation algorithms using this database. All thirty-nine fundus images were obtained from a diabetic retinopathy screening programme in the UK. 1-10: Glaucoma subgroup Images 11-20: Hypertension subgroup Images 21-30: Diabetic retinopathy subgroup Images 31-40: Age-related macular degeneration subgroup Besides the fundus images, the manual segmentation These manually segmented images can be used as the ground truth to compare and assess the automatic vessel extraction algorithms.
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…Two sets: ModelNet-C for point cloud classification and ShapeNet-C for part segmentation. Real-world corruption sources, ranging from object-, senor-, and processing-levels.
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NucMM is a dataset for segmenting 3D cell nuclei from microscopy image volumes that pushes the task forward to the sub-cubic millimeter scale.
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…Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented
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…This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco.
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To enrich the diversity, we also collect 92 images which are suitable for saliency detection from DAVIS [27], a densely annotated high-resolution video segmentation dataset.
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…The dataset also contains ground truth instance segmentation masks for 110 images in the test set.
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…In addition to the images, Google Earth Studio provides camera intrinsic and extrinsic parameters, making it possible to create automated annotations for semantic and building instance segmentation
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…PTR contains around 70k RGBD synthetic images with ground truth object and part level annotations regarding semantic instance segmentation, color attributes, spatial and geometric relationships, and certain
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…We published this new RGB-Thermal semantic segmentation dataset in support of further development of autonomous vehicles in the future.
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EgoISM-HOI contains a total of 39,304 RGB images, 23,356 depth maps and instance segmentation masks, 59,860 hand annotations, 237,985 object instances across 19 object categories and 35,416 egocentric
…identity label, head segmentation mask, and encounter timestamp.
The Sunnybrook Cardiac Data (SCD), also known as the 2009 Cardiac MR Left Ventricle Segmentation Challenge data, consist of 45 cine-MRI images from a mixed of patients and pathologies: healthy, hypertrophy Subset of this data set was first used in the automated myocardium segmentation challenge from short-axis MRI, held by a MICCAI workshop in 2009.
…images/raw_scale001-raw_scale0075) 11,280 images of the raw sensor data processed through twelve different pipelines (microscopy/images/processed_views) Raw-Drone: 548 raw drone camera images for car segmentation (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256).
Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy
…Several methods have been developed to automate polyp detection and segmentation. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists.
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…Participants are challenged to segment laparoscopic surgeries for gallbladder removal (cholecystectomy) into surgical phases, to recognize instrument presence and surgical actions as well as to classify to submit different results for phase segmentation, action recognition, instrument presence and skill classification .
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BRATS 2013 is a brain tumor segmentation dataset consists of synthetic and real images, where each of them is further divided into high-grade gliomas (HG) and low-grade gliomas (LG).
35 PAPERS • 2 BENCHMARKS
…Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation
…The dataset constitutes a low-quality frame, its high-quality counterpart, a teeth segmentation mask, and an inter-frame homography matrix.
…All unnecessary video segments (e.g., video introductions, news, etc.) that could disturb the learning process were removed.
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…It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information.
…In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects.
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The Semantic Boundaries Dataset (SBD) is a dataset for predicting pixels on the boundary of the object (as opposed to the inside of the object with semantic segmentation).
127 PAPERS • 3 BENCHMARKS
RoadAnomaly21 is a dataset for anomaly segmentation, the task of identify the image regions containing objects that have never been seen during training.
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…Each sample provides: RGB image (320x320 pixels) Depth map (320x320 pixels) Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm
…It contains over 10M segments of multilingual open data. The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites.
…The datasets contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of annotations for semantic segmentation.
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…Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction.
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…It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids).
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…We also provided the bounding box annotations (YOLO format) for the segmentation of words/lines and the ground truth annotations for full-text, along with the segmented images and their positions. The BN-HTRd dataset can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spotting, word/line segmentation, and so on.
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…To validate our approach we employ two popular video object segmentation datasets, DAVIS16 [38] and DAVIS17 [42]. For the multiple object video segmentation task we consider DAVIS17. As our goal is to segment objects in videos using language specifications, we augment all objects annotated with mask labels in DAVIS16 and DAVIS17 with non-ambiguous referring expressions. (We actually quantified that only∼ 15% of the collected descriptions become invalid over time and it does not affect strongly segmentation results as temporal consistency step helps to disambiguate some We believe the collected data will be of interest to segmentation as well as vision and language communities, providing an opportunity to explore language as alternative input for video object segmentation
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…V3C comes with a shot segmentation for each video, together with the resulting keyframes in original as well as reduced resolution and additional metadata.
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…It includes tasks like: word segmentation, part of speech tagging, reading comprehension and document retrieval.
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…We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.
…The dataset collects valuable contents from public perception dataset Waymo Open Dataset and provides lane&closest-in-path object(CIPO) annotation for 1000 segments.
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…It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark.
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…The dataset includes 177k training data and 33k validation data where both the 3D human poses and body segments are avaliable.
…This dataset is a collection of over 600 Transit agencies in the US with a concise and easy representation of GTFS data in the form of segments.
…It contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks.
…Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background.
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…The text were word-segmented, annotated with part-of-speech tagging and Cantonese pronunciation using the romanisation scheme of Linguistic Society of Hong Kong (LSHK).
…Data are segmented into sentences which are further word tokenized.
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Digital Peter is a dataset of Peter the Great's manuscripts annotated for segmentation and text recognition.
ATLANTIS is a benchmark for semantic segmentation of waterbody images.
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