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Widely anticipated in HCI is that computing will move to the background, weaving itself into the fabric of our everyday living and projecting the human user into the foreground. To realize this goal, next-generation computing (a.k.a. pervasive computing, ambient intelligence, and human computing) will need to develop human-centered user interfaces that respond readily to naturally occurring, multimodal, human communication. These interfaces will need the capacity to perceive, understand, and respond appropriately to human intentions and cognitive-emotional states as communicated by social and affective signals. Motivated by this vision of the future, automated analysis of nonverbal behavior has attracted increasing attention in diverse disciplines, including psychology, computer science, linguistics, and neuroscience. Promising approaches have been reported, especially in the areas of facial expression and multimodal communication. Until recently, much of this work has focused on posed, often exaggerated expressions (for reviews, see (Pantic & Rothkrantz, 2003; Tian et al., 2005; Zeng et al., 2009)). Yet, increasing evidence suggests that deliberate or posed behaviour differs in appearance and timing from that which occurs in daily life. For example, brow raises have larger amplitude, faster onset, and shorter duration when posed than when not (Schmidt et al., 2009). The morphology of facial actions differs as well. As (Littlewort, et al., 2009) in this issue report, facial actions systematically differ between spontaneous and feigned pain. Approaches to automatic behaviour analysis that have been trained on deliberate and typically exaggerated behaviours may fail to generalize to the complexity of expressive behaviour found in real-world settings. This Special Issue of Image and Vision Computing brings together cutting edge work on the automatic analysis of non-posed, real-world human behavior. It includes state-of-the-art reviews of computational approaches to conversation analysis (Gatica-Perez, 2009) and social signal processing (Vinciarelli et al., 2009), recent advances in generic face modeling (Lucey et al., 2009), automated detection of pain from facial behavior (Littlewort et al., 2009) (Ashraf et al., 2009), cognitive states of interest from facial, vocal, and gestural behavior (Schuller et al., 2009), automatic detection of diverse human activities from spatio-temporal features (Oikonomopoulos et al., 2009), and automatic recognition of American Sign Language (Ding & Martinez, 2009). These papers represent an exciting advance toward human-centered interfaces that can perceive and understand real-world human behavior. Of course, as discussed by Zeng et al. (2009) and by Vinciarelli et al. (2009) and Gatica-Perez (2009) in this issue, significant scientific and technical challenges remain to be addressed. However, we are optimistic about the continued progress. A principal reason is that automatic multimodal analysis of human naturalistic behavior is prerequisite in achieving next-generation, human-centered computing (Jaimes et al., 2006; Pantic et al., 2006, 2009); and this topic is poised to become one of the most active research topics in the computer vision and signal processing communities. To support these efforts, infrastructure is emerging from the extensive efforts of investigators, international sponsors, and professional societies. A sampling of research activities includes basic research on machine analysis of human behavior (e.g., European Research Council (ERC) MAHNOB project), automatic analysis of face-to-face and small group interactions (e.g., see the projects of MIT Human Dynamics Laboratory and European Commission (EC) FP6 AMIDA project), social signaling (e.g., EC FP7 Social Signal Processing NoE project), human-computer interactions (e.g., EC FP7 Semaine project), applications in mental health (Cohn et al., 2009), and other areas. The contributions in this Special Issue highlight recent advances and point to continued progress toward the goal of human-centered interfaces that can understand human intentions and behavior and respond intelligently.

The geolocalization of aerial images is important for extracting geospatial information (e.g. the position of buildings, streets, and cars) and for creating maps. The standard is to use an expensive aerial imaging system equipped with an accurate GPS and IMU and/or do laborious ground control point measurements. In this paper we present a novel method to recognize the geolocation of aerial images automatically without any GPS or IMU. We extract road segments in the image sequence by detecting and tracking cars. We search in a database created from a road network map for the best matches between the road database and the extracted road segments. Geometric hashing is used to retrieve a shortlist of matches. The matches in the shortlist are ranked by a verification process. The highest scoring match gives the location and orientation of the images. We show in the experiments that our method can correctly geolocalize the aerial images in various scenes: e.g. urban, suburban, and rural with motorway. Besides the current images only the road map is needed over the search area. We can search an area of 22,500km2 containing 32,000km of streets within minutes on a single cpu. We utilize roads as appearance invariant features for geolocalization.Using the road map an image database over the search area is not needed.We present a geometric hashing method to match partial line segments to line structures.The search method does not need a complete road detector which detects relations between roads as a graph, e.g. intersections.Using car tracks as road segment detection is invariant to different light conditions, seasonal changes and scale.