From Multimedia Computing Lab - University of Augsburg
An annotated dataset of shredded documents
|All images are taken from 'Bild der Wissenschaft 08/2010'|
|We created a dataset consisting of 96 magazine pages that have been shredded by hand. Besides the digitized document fragments, the dataset also provides an annotation which gives sufficient information about the layout of the intact document. More details...|
Dataset of logos in real-world images: FlickrLogos-32
The dataset FlickrLogos-32 contains photos depicting product logos. We collected logos for 32 different classes from Flickr. The dataset is designed for the evaluation of logo retrieval, multi-class logo detection as well as object recognition techniques. We provide images, ground truth, pixel-level annotations, evaluation software and pre-computed visual features. More details...
An annotated data set for pose estimation of swimmers
| In this work we present an annotated data set for two-dimensional pose estimation of swimmers. The data set contains fifteen cycles of swimmers swimming backstroke with more than 1200 annotated video frames. |
|For more information please see Annotated Data Set For Swimmers|
2005 DARPA Grand Challenge Source Code
The 2005 DARPA Grand Challenge is a 132 mile race through the desert with autonomous robotic vehicles. Lasers mounted on the car roof provide a map of the road up to 20 meters ahead of the car but the car needs to see further in order to go fast enough to win the race. Computer vision can extend that map of the road ahead but desert road is notoriously similar to the surrounding desert. Various machine learning algorithm (Classification and Regression Trees) provided a machine learning boost to find road while at the same time measuring when that road could not be distinguished from surrounding desert.
Source code, videos, and ground truth data of
- Bob Davies and Rainer Lienhart. Using CART to Segment Road Images. SPIE Multimedia Content Analysis, Management, and Retrieval 2006, 15-19 Jan. 2006, San Jose, 2006.
can be downloaded from here:
Sub-pixel feature detection via openCV
With openCV it is - besides many other functions - possible to detect features in correspondent images with a sub-pixel accuracy. The optical flow in this function (CalcOpticalFlow) is limited to simple translation. We have done some work to extend the function to affine transformations and extended the algorithm by adding illumination normalization. The new function (CalcAffineOpticalFlow) was already part of openCV, but not activated due to some bugs, which are fixed in our version. Some additional hints how to modify your openCV code can be found in following page: cvlkpyramid.cpp-Notes. The source-code  is available online, too.