Christian Wallraven

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Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for(More)
Categorization of objects solely based on shape and appearance is still a largely unresolved issue. With the advent of new sensor technologies, such as consumer-level range sensors, new possibilities for shape processing have become available for a range of new application domains. In the first part of this paper, we introduce a novel, large dataset(More)
Similarity has been proposed as a fundamental principle underlying mental object representations and capable of supporting cognitive-level tasks such as categorization. However, much of the research has considered connections between similarity and categorization for tasks performed using a single perceptual modality. Considering similarity and(More)
Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Several studies have explored the processes underlying human scene categorization, but they have focused on processing global image information. In this study, we present both psychophysical and computational experiments that(More)
We present a method for image interpolation that is able to create high-quality, perceptually convincing transitions between recorded images. By implementing concepts derived from human vision, the problem of a physically correct image interpolation is relaxed to that of image interpolation which is perceived as visually correct by human observers. We find(More)
Communication plays a central role in everday life. During an average conversation, information is exchanged in a variety of ways, including through facial motion. Here, we employ a custom, model-based image manipulation technique to selectively “freez ” portions of a face in video recordings in order to determine the areas that are sufficient(More)
The human face is capable of producing an astonishing variety of expressions—expressions for which sometimes the smallest difference changes the perceived meaning considerably. Producing realistic-looking facial animations that are able to transmit this degree of complexity continues to be a challenging research topic in computer graphics. One(More)
Primates possess the remarkable ability to differentiate faces of group members and to extract relevant information about the individual directly from the face. Recognition of conspecific faces is achieved by means of holistic processing, i.e. the processing of the face as an unparsed, perceptual whole, rather than as the collection of independent features(More)
The human face is an important and complex communication channel. Humans can, however, easily read in a face not only identity information but also facial expressions with high accuracy. Here, we present the results of four psychophysical experiments in which we systematically manipulated certain facial areas in video sequences of nine conversational(More)
For humans, faces are highly overlearned stimuli, which are encountered in everyday life in all kinds of poses and views. Using psychophysics we investigated the effects of viewpoint on human face recognition. The experimental paradigm is modeled after the inter-extra-ortho experiment using unfamiliar objects by Bülthoff and Edelman [5]. Our results show a(More)