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J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
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Title |
Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
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2013 |
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Expert Systems with Applications |
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EXWA |
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40 |
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17 |
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6707-6712 |
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Neural gas; Expert vision; Eye-tracking; Fixations |
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Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
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0957-4174 |
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Admin @ si @ CRM2013 |
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2438 |
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Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez |
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Title |
Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters |
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Journal Article |
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Year |
2014 |
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Expert Systems With Applications |
Abbreviated Journal |
EXSY |
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41 |
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16 |
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7281–7290 |
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Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks |
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Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour. |
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ADAS; 600.055; 600.057; 600.076 |
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Admin @ si @ LPA2014 |
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2500 |
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Fadi Dornaika; Angel Sappa |
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Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models |
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2007 |
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Pattern Recognition Letters |
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PRL |
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28 |
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15 |
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2116-2126 |
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ADAS @ adas @ DoS2007c |
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877 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
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Title |
On-board camera extrinsic parameter estimation |
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Year |
2006 |
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Electronics Letters |
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EL |
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42 |
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13 |
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745–746 |
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An efficient technique for real-time estimation of camera extrinsic parameters is presented. It is intended to be used on on-board vision systems for driving assistance applications. The proposed technique is based on the use of a commercial stereo vision system that does not need any visual feature extraction. |
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IEE |
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ADAS |
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ADAS @ adas @ SGD2006a |
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655 |
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Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Domain Adaptation of Deformable Part-Based Models |
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2014 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
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12 |
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2367-2380 |
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Domain Adaptation; Pedestrian Detection |
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The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors. |
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0162-8828 |
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ADAS; 600.057; 600.054; 601.217; 600.076 |
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ADAS @ adas @ XRV2014b |
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2436 |
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