Body Detection

Manga109 has been compiled by the Aizawa Yamasaki Matsui Laboratory, Department of Information and Communication Engineering, the Graduate School of Information Science and Technology, the University of Tokyo. The compilation is intended for use in academic research on the media processing of Japanese manga. Manga109 is composed of 109 manga volumes drawn by professional manga artists in Japan. These manga were commercially made available to the public between the 1970s and 2010s, and encompass a wide range of target readerships and genres (see the table in Explore for further details.) Most of the manga in the compilation are available at the manga library “Manga Library Z” (formerly the “Zeppan Manga Toshokan” library of out-of-print manga). Source: Manga109 Image Source: https://arxiv.org/pdf/1510.04389v1.pdf

In Clipart1k, the target domain classes to be detected are the same as those in the source domain. All the images for a clipart domain were collected from one dataset (i.e., CMPlaces) and two image search engines (i.e., Openclipart2 and Pixabay3). Search queries used are 205 scene classes (e.g., pasture) used in CMPlaces to collect various objects and scenes with complex backgrounds.

Watercolor2k is a dataset used for cross-domain object detection which contains 2k watercolor images with image and instance-level annotations.

Comic2k is a dataset used for cross-domain object detection which contains 2k comic images with image and instance-level annotations. Image Source: https://naoto0804.github.io/cross_domain_detection/

The DCM dataset is composed of 772 annotated images from 27 golden age comic books. We freely collected them from the free public domain collection of digitized comic books Digital Comics Museum. One album per available publisher was selected to get as many different styles as possible. We made ground-truth bounding boxes of all panels, all characters (body + faces), small or big, human-like or animal-like. Image source: https://gitlab.univ-lr.fr/crigau02/dcm-dataset/-/tree/master

A dataset of 100K synthetic images of skin lesions, ground-truth (GT) segmentations of lesions and healthy skin, GT segmentations of seven body parts (head, torso, hips, legs, feet, arms and hands), and GT binary masks of non-skin regions in the texture maps of 215 scans from the 3DBodyTex.v1 dataset [2], [3] created using the framework described in [1]. The dataset is primarily intended to enable the development of skin lesion analysis methods. Synthetic image creation consisted of two main steps. First, skin lesions from the Fitzpatrick 17k dataset were blended onto skin regions of high-resolution three-dimensional human scans from the 3DBodyTex dataset [2], [3]. Second, two-dimensional renders of the modified scans were generated. Use of the dataset, in part or in full, is conditional on citation of the following work: [1] Ashish Sinha, Jeremy Kawahara, Arezou Pakzad, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, Ghassan Hamarneh, ‘DermSynth3D: Synthesis of in-the-wild annotated dermatology images’, 2023. [2] A. Saint, A. E. Rahman Shabayek, K. Cherenkova, G. Gusev, D. Aouada, and B. Ottersten, ‘Bodyfitr: Robust Automatic 3D Human Body Fitting’, in 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan: IEEE, Sep. 2019, pp. 484–488. doi: 10.1109/ICIP.2019.8803819. [3] A. Saint et al., ‘3DBodyTex: Textured 3D Body Dataset’, in 2018 International Conference on 3D Vision (3DV), Verona: IEEE, Sep. 2018, pp. 495–504. doi: 10.1109/3DV.2018.00063.