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26 results for “urban scenes”

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zenodo44/100

Raw images (photographs) of urban text scenes for camera-based Thai text recognition

Raw image collection of city scenes in Thailand with text content. Text is photographed from diffeent angles. Also morning and evening photographs were taken in order to capture different lighting conditions. The material, 309 images, was photographed in 2013 by Bowornrat Sriman and volunteers. Example EXIF: JPEG image data, Exif standard: [TIFF image data, little-endian, direntries=13, height=2448, manufacturer=SAMSUNG, model=GT-I9300, orientation=upper-right, xresolution=220, yresolution=228, resolutionunit=2, software=I9300XXEMA2, datetime=2013:03:14 18:17:49, GPS-Data, width=3264], baseline, precision 8, 3264x2448, frames 3 The images are not labeled. The orientation (landscape/portrait) is not corrected yet. This material was used in preparation of the publication: Sriman, B.  & Schomaker, L. (2015). Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT, Proceedings of the International Conference on Pattern Recognition Applications and Methods: ICPRAM 2015.  De Marsico, M., Figueiredo, M.  & Fred, A. (Eds.).  Lisbon, Portugal: SciTePress, Vol.  1, p.  304-311 8 p. Please cite this publication when using these data.

opencc-by-4.0Mar 2013View details →

zenodo40/100

"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15

opencc-by-4.0Dec 2019View details →

zenodo36/100

HSICityV2: Urban Scene Understanding via Hyperspectral Images

Light in all spectrum travel in the physical world. The trichromatism (RGB) human vision captures and understands it. Machine vision makes an analogy which use RGB camera for semantic segmentation and scene understanding. We argue that such machine vision suffers from metamerism, that different objects may appear in same RGB color while actually distinctive in spectrum. While learning based solutions, especially deep learning, have been heavily explored, they do not solve the fundamental physical limitation. In this paper, we propose to use Hyperspectral images (HSIs), which capture hundreds of consecutive narrow bands from the real visible world and therefore metamerism no longer exists. In short, we aim to 'see beyond human vision'. In practice, we introduce a novel large scale high quality HSI dataset for semantic segmentation in cityscapes. Namely, Hyperspectral City dataset. The dataset contains 1330 HSIs which are captured in typical urban driving scenes. Each HSI has 1889×1422 spatial resolution and 128 spectral channels ranged from 450nm to 950nm. The dataset provides semantic annotation at pixel level which is done manually by professional annotators. We believe this dataset enables a new direction for scene understanding.

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Dataset results

Raw images (photographs) of urban text scenes for camera-based Thai text recognition

HSICityV2: Urban Scene Understanding via Hyperspectral Images

TAU Urban Acoustic Scenes 2020 Mobile, Development dataset

TAU Urban Acoustic Scenes 2020 Mobile, Evaluation dataset

TAU Urban Acoustic Scenes 2022 Mobile, Development dataset

TAU Urban Acoustic Scenes 2022 Mobile, Evaluation dataset

TAU Urban Acoustic Scenes 2024 Mobile, Evaluation dataset

TUT Urban Acoustic Scenes 2018 Mobile, Development dataset

TUT Urban Acoustic Scenes 2018, Development dataset

TUT Urban Acoustic Scenes 2018 Mobile, Leaderboard dataset

TUT Urban Acoustic Scenes 2018, Leaderboard dataset

TUT Urban Acoustic Scenes 2018 Mobile, Evaluation dataset

TUT Urban Acoustic Scenes 2018, Evaluation dataset

TAU Urban Acoustic Scenes 2019 Mobile, Development dataset

TAU Urban Acoustic Scenes 2019, Development dataset

TAU Urban Acoustic Scenes 2019 Openset, Development dataset

TAU Urban Acoustic Scenes 2019, Evaluation dataset

TAU Urban Acoustic Scenes 2019 Mobile, Evaluation dataset

TAU Urban Acoustic Scenes 2019 Openset, Evaluation dataset

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Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

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International Brain Laboratory public data

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