Dominique Maniry

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This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and(More)
This paper describes the participation of the TUB-IRML group to the MediaEval 2014 Visual Privacy task. We present a method for privacy protection of individuals in surveillance videos. In order to achieve this, our method obscures both shape and appearance of identity-related regions through blurring and color remapping. The intelligibility is preserved by(More)
This paper presents a full-reference (FR) image quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN extracts features from distorted and reference image patches and estimates the quality of the distorted ones by combining and regressing the feature vectors using two fully connected layers. Experiments are performed on(More)
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained endto- end and comprises 10 convolutional layers and 5 pooling layers for feature extraction, and 2 fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that(More)
In this paper, we present a method for removing identityrelated information from image sequences for the privacy protection of individuals. The face, despite being an important feature to identify a person, is not the only body part that needs to be obscured. Therefore, we propose to replace the whole body of individuals by their silhouette defined by(More)
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