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Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli |
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Computer Vision for Ambient Intelligence |
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2011 |
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Journal of Ambient Intelligence and Smart Environments |
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JAISE |
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3 |
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3 |
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187-191 |
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Admin @ si @ SGS2011a |
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1725 |
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Michael Holte; Bhaskar Chakraborty; Jordi Gonzalez; Thomas B. Moeslund |
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Title |
A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points |
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2012 |
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IEEE Journal of Selected Topics in Signal Processing |
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J-STSP |
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6 |
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5 |
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553-565 |
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In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach. |
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1932-4553 |
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Admin @ si @ HCG2012 |
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1994 |
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A. Toet; M. Henselmans; M.P. Lucassen; Theo Gevers |
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Title |
Emotional effects of dynamic textures |
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2011 |
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i-Perception |
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iPER |
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2 |
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9 |
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969 – 991 |
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This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures’ area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval. |
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2041-6695 |
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ALTRES;ISE |
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Admin @ si @THL2011 |
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1843 |
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Author |
Jordi Gonzalez; Dani Rowe; J. Varona; Xavier Roca |
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Title |
Understanding Dynamic Scenes based on Human Sequence Evaluation |
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2009 |
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Image and Vision Computing |
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IMAVIS |
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27 |
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10 |
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1433–1444 |
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Image Sequence Evaluation; High-level processing of monitored scenes; Segmentation and tracking in complex scenes; Event recognition in dynamic scenes; Human motion understanding; Human behaviour interpretation; Natural-language text generation; Realistic demonstrators |
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In this paper, a Cognitive Vision System (CVS) is presented, which explains the human behaviour of monitored scenes using natural-language texts. This cognitive analysis of human movements recorded in image sequences is here referred to as Human Sequence Evaluation (HSE) which defines a set of transformation modules involved in the automatic generation of semantic descriptions from pixel values. In essence, the trajectories of human agents are obtained to generate textual interpretations of their motion, and also to infer the conceptual relationships of each agent w.r.t. its environment. For this purpose, a human behaviour model based on Situation Graph Trees (SGTs) is considered, which permits both bottom-up (hypothesis generation) and top-down (hypothesis refinement) analysis of dynamic scenes. The resulting system prototype interprets different kinds of behaviour and reports textual descriptions in multiple languages. |
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ISE @ ise @ GRV2009 |
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1211 |
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Author |
Noha Elfiky; Jordi Gonzalez; Xavier Roca |
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Title |
Compact and Adaptive Spatial Pyramids for Scene Recognition |
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2012 |
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Image and Vision Computing |
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IMAVIS |
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30 |
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8 |
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492–500 |
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Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets. |
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Admin @ si @ EGR2012 |
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2004 |
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