Rene Grzeszick

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The classification of acoustic events in indoor environments is an important task for many practical applications in smart environments. In this paper a novel approach for classifying acoustic events that is based on a Bag-of-Features approach is proposed. Mel and gammatone frequency cepstral coefficients that originate from psychoacoustic models are used(More)
Image parsing describes a very fine grained analysis of natural scene images, where each pixel is assigned a label describing the object or part of the scene it belongs to. This analysis is a keystone to a wide range of applications that could benefit from detailed scene understanding, such as keyword based image search, sentence based image or video(More)
The detection and classification of acoustic events in various environments is an important task. Its applications range from multimedia analysis to surveillance of humans or even animal life. Several of these tasks require the capability of online processing. Besides many approaches that tackle the task of acoustic event detection, methods that are based(More)
In this paper the application of uncertainty modeling to convolutional neural networks is evaluated. A novel method for adjusting the network’s predictions based on uncertainty information is introduced. This allows the network to be either optimistic or pessimistic in its prediction scores. The proposed method builds on the idea of applying dropout at test(More)
This paper presents a novel method for combining local image features and spatial information for object classification tasks using the Bag-of-Features principle. The feature descriptor is extended by additional spatial information. Hence, similar feature descriptors do not only describe similar image patches, but similar patches in roughly the same region.(More)
Training recognizers for handwritten characters is still a very time consuming task involving tremendous amounts of manual annotations by experts. In this paper we present semi-supervised labeling strategies that are able to considerably reduce the human effort. We propose two different methods to label and later recognize characters in collections of(More)
Handwritten historical documents pose extremely challenging problems for automatic analysis. This is due to the high variability observed in handwritten script, the use of writing styles and script types unknown today, the frequently lacking orthographic standardization, and the degradation of the respective documents. Therefore, it is currently out of(More)
In this paper a bottom-up approach for detecting and recognizing objects in complex scenes is presented. In contrast to top-down methods, no prior knowledge about the objects is required beforehand. Instead, two different views on the data are computed: First, a GIST descriptor is used for clustering scenes with a similar global appearance which produces a(More)