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Author |
Marcel P. Lucassen; Theo Gevers; Arjan Gijsenij |
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Title |
Texture Affects Color Emotion |
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2011 |
Publication |
Color Research & Applications |
Abbreviated Journal |
CRA |
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36 |
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6 |
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426–436 |
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Keywords |
color;texture;color emotion;observer variability;ranking |
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Abstract |
Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard-Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group-averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R2 = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R2 = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010 |
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Admin @ si @ LGG2011 |
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1844 |
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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Determining the Best Suited Semantic Events for Cognitive Surveillance |
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Journal Article |
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Year |
2011 |
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Expert Systems with Applications |
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EXSY |
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38 |
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4 |
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4068–4079 |
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Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces |
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State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. |
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Elsevier |
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Admin @ si @ FBR2011a |
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1722 |
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Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez |
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Title |
Chromatic shadow detection and tracking for moving foreground segmentation |
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2015 |
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Image and Vision Computing |
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IMAVIS |
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41 |
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42-53 |
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Detecting moving objects; Chromatic shadow detection; Temporal local gradient; Spatial and Temporal brightness and angle distortions; Shadow tracking |
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Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus a ecting the performance of the nal detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution re nes even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows.
To do so, a combination of motion lters in a data association framework exploits the temporal consistency between objects and shadows to increase
the shadow detection rate. Experimental results exceed current state-of-the-
art in shadow accuracy for multiple well-known surveillance image databases which contain di erent shadowed materials and illumination conditions. |
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ISE; 600.078; 600.063 |
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Admin @ si @ HHM2015 |
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2703 |
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Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca |
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Title |
Quality control of safety belts by machine vision inspection for real-time production |
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2003 |
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Optical Engineering (IF: 0.877) |
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42 |
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4 |
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1114-1120 |
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ADAS @ adas @ PRL2003 |
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399 |
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Author |
Ignasi Rius; Jordi Gonzalez; J. Varona; Xavier Roca |
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Title |
Action-specific motion prior for efficient bayesian 3D human body tracking |
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2009 |
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Pattern Recognition |
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PR |
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42 |
Issue |
11 |
Pages |
2907–2921 |
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In this paper, we aim to reconstruct the 3D motion parameters of a human body
model from the known 2D positions of a reduced set of joints in the image plane.
Towards this end, an action-specific motion model is trained from a database of real
motion-captured performances. The learnt motion model is used within a particle
filtering framework as a priori knowledge on human motion. First, our dynamic
model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints. |
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0031-3203 |
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ISE @ ise @ RGV2009 |
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1159 |
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