Datasets

Binaries/Code

Datasets

Open Source Software

		Freiburg-Berkeley Motion Segmentation Dataset			Video Segmentation Benchmark
		Image Sequences			TEM Dataset
		TILDA Textile Texture Database			Training data for Exemplar CNN
		Generated Matching Dataset			Training data for chair generation
		Stereo Ego-motion Dataset			Optical Flow Datasets: "Flying Chairs", "ChairsSDHom"
		Scene Flow Datasets			Human Part Segmentation Datasets
		Rendered Handpose Dataset			Pedestrian Zone Scene
		FreiHAND Dataset			HanCo Dataset
		Human Pose RGBD Datasets			OVAD: Open-Vocabulary Attribute Detection Dataset
		ADE-OoD: a benchmark for dense Out-of-Distribution detection on natural images.

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TILDA Textile Texture-Database

Version 1.0, 1996

TILDA is a Textile Texture Database which was developed within the framework of the working group Texture Analysis of the DFG`s (Deutsche Forschungsgemeinschaft) major research programme "Automatic Visual Inspection of Technical Objects". This working group developed and analysed methods which made it possible to recognise and distinguish textures of varying kinds.

A total of eight representative textile kinds were included in the database. Based on the analysis of textile atlases, seven error classes were defined. With the reference classification - a class of textiles without error - there exists therefore eight sorts of classes for each kind of textile.

For each of the above classes 50 TIF-pictures (768x512 pixel, greylevel image 8 bit) were acquired through relocation and rotation of the textile sample. The entire texture textile database consists of 3200 TIF pictures with a total size of 1.2 Gbyte.

For every picture in the TILDA database there exists an ASCII-file which briefly describes the error. There is a detailed description of the database in a PDF file by H. Schulz-Mirbach available (German only!): "Ein Referenzdatensatz zur Evaluierung von Sichtprüfungsverfahren für Textiloberflächen"

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