搜藏一个较全的数据集目录

             这个页面比较详细：http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm             

             此外cvpapers的页面一直更新：http://www.cvpapers.com/datasets.html 

    

室内RGB_D场景分割：https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html

         Microsoft COCO - Common Objects in Context (Tsung-Yi Lin et al)

COCO

COCO(Common Objects in Context)是一个新的图像识别、分割和图像语义数据集，它有如下特点：

1）Object segmentation2）Recognition in Context3）Multiple objects per image4）More than 300,000 images5）More than 2 Million instances6）80 object categories7）5 captions per image8）Keypoints on 100,000 people
COCO数据集由微软赞助，其对于图像的标注信息不仅有类别、位置信息，还有对图像的语义文本描述，
COCO数据集的开源使得近两三年来图像分割语义理解取得了巨大的进展，也几乎成为了图像语义理解
算法性能评价的“标准”数据集。






Segmentation (General)


  1. : Shadow Detection/Texture Segmentation Computer Vision Dataset- Video based sequences for shadow detection/suppression, with ground truth (Newey, C., Jones, O., & Dee, H.
 M.)
  2. Aberystwyth Leaf Evaluation Dataset- Timelapse plant images with hand marked up leaf-level segmentations for some time steps, and biological data from plant sacrifice. (Bell,
 Jonathan; Dee, Hannah M.)
  3. Alpert et al. Segmentation evaluation database (Sharon Alpert, Meirav Galun, Ronen Basri, Achi Brandt)
  4. BMC (Background Model Challenge)- A dataset for comparing background subtraction algorithms, comp=osed of real and synthetic videos(Antoine)
  5. Berkeley Segmentation Dataset and Benchmark (David Martin and Charless Fowlkes)
  6. CAD 120 affordance dataset - Pixelwise affordance annotation in human context (Sawatzky, Srikantha, Gall)
  7. CTU Color and Depth Image Dataset of Spread Garments- Images of spread garments with annotated corners.(Wagner, L., Krejov D., and Smutn V. (Czech Technical
 University in Prague))
  8. CTU Garment Folding Photo Dataset- Color and depth images from various stages of garment folding.(Sushkov R., Melkumov I., Smutn y V. (Czech Technical
 University in Prague))
  9. DeformIt 2.0- Image Data Augmentation Tool: Simulate novel images with ground truth segmentations from a single image-segmentation pair (Brian Booth
 and Ghassan Hamarneh)
  10. GrabCut Image database (C. Rother, V. Kolmogorov, A. Blake, M. Brown)
  11. ICDAR'15 Smartphone document capture and OCR competition - challenge 1 - videos of documents filmed by a user with a smartphone to simulate mobile document capture, and ground truth
 coordinates of the document corners to detect. (Burie, Chazalon, Coustaty, Eskenazi, Luqman, Mehri, Nayef, Ogier, Prum and Rusinol)
  12. Intrinsic Images in the Wild (IIW)- Intrinsic Images in the Wild, is a large-scale, public dataset for evaluating intrinsic image decompositions of
 indoor scenes (Sean Bell, Kavita Bala, Noah Snavely)
  13. LabelMe images database and online annotation tool (Bryan Russell, Antonio Torralba, Kevin Murphy, William Freeman)
  14. LITS Liver Tumor Segmentation - 130 3D CT scans with segmentations of the liver and liver tumor. Public benchmark with leaderboard at Codalab.org (Patrick Christ)
  15. Materials in Context (MINC)- The Materials in Context Database (MINC) builds on OpenSurfaces, but includes millions of point annotations of material labels.
 (Sean Bell, Paul Upchurch, Noah Snavely, Kavita Bala)
  16. OpenSurfaces- OpenSurfaces consists of tens of thousands of examples of surfaces segmented from consumer photographs of interiors, and annotated
 with material parameters, texture information, and contextual information . (Kavita Bala et al.)
  17. Osnabrück gaze tracking data - 318 video sequences from several different gaze tracking data sets with polygon based object annotation (Schöning, Faion, Heidemann, Krumnack,
 Gert, Açik, Kietzmann, Heidemann & König)
  18. PetroSurf3D- 26 high resolution (sub-millimeter accuracy) 3D scans of rock art with pixelwise labeling of petroglyphs for segmentation(Poier, Seidl,
 Zeppelzauer, Reinbacher, Schaich, Bellandi, Marretta, Bischof)
  19. SYNTHIA
- Large set (~half million) of virtual-world images for training autonomous cars to see. (ADAS Group at Computer Vision Center)
  20. Stony Brook University Shadow Dataset (SBU-Shadow5k)- Large scale shadow detection dataset from a wide variety of scenes and photo
 types, with human annotations (Tomas F.Y. Vicente, Le Hou, Chen-Ping Yu, Minh Hoai, Dimitris Samaras)
Urban Datasets


  1. Barcelona - 15,150 images, urban views of Barcelona (Tighe and Lazebnik)
  2. CMP Facade Database- Includes 606 rectified images of facades from various places with 12 architectural classes annotated.(Radim Tylecek)
  3. LM+SUN - 45,676 images, mainly urban or human related scenes (Tighe and Lazebnik)
  4. MIT CBCL StreetScenes Challenge Framework: (Stan Bileschi)
  5. Queen Mary Multi-Camera Distributed Traffic Scenes Dataset (QMDTS)- The QMDTS is collected from urban surveillance environment for the study
 of surveillance behaviours in distributed scenes.(Dr. Xun Xu. Prof. Shaogang Gong and Dr. Timothy Hospedales)
  6. Robust Global Translations with 1DSfMthe numerical data describing global structure from motion problems for each dataset (Kyle Wilson and Noah Snavely)
  7. Sift Flow (also known as LabelMe Outdoor, LMO) - 2688 images, mainly outdoor natural and urban (Tighe and Lazebnik)
  8. Street-View Change Detection with Deconvolutional Networks- Database with aligned image pairs from street-view imagery with structural,lighting, weather and seasonal
 changes.(Pablo F. Alcantarilla, Simon Stent, German Ros, Roberto Arroyo and Riccardo Gherardi)
  9. SydneyHouse
- Streetview house images with accurate 3D house shape, facade object label, dense point correspondence, and annotation toolbox.(Hang Chu, Shenlong Wang, Raquel Urtasun,Sanja Fidler)
  10. Traffic Signs Dataset- recording sequences from over 350 km of Swedish highways and city roads (Fredrik Larsson)

            





            

            
posted @ 
2017-12-13 10:48 
wishchin 
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