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Author | Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez | ||||
Title | Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters | Type | Journal Article | ||
Year | 2014 | Publication | Expert Systems With Applications | Abbreviated Journal | EXSY |
Volume | 41 | Issue | 16 | Pages | 7281–7290 |
Keywords | Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks | ||||
Abstract | 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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Notes | ADAS; 600.055; 600.057; 600.076 | Approved | no | ||
Call Number | Admin @ si @ LPA2014 | Serial | 2500 | ||
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Author | C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger | ||||
Title | Limitations of visual gamma corrections in LCD displays | Type | Journal Article | ||
Year | 2014 | Publication | Displays | Abbreviated Journal | Dis |
Volume | 35 | Issue | 5 | Pages | 227–239 |
Keywords | Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration | ||||
Abstract | A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements. | ||||
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Notes | CIC; DAG; 600.052; 600.077; 600.074 | Approved | no | ||
Call Number | Admin @ si @ PRK2014 | Serial | 2511 | ||
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Author | Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Leveraging local neighborhood topology for large scale person re-identification | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 12 | Pages | 3767–3778 |
Keywords | Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 | ||||
Abstract | In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data. | ||||
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Notes | LAMP; 601.240; 600.079 | Approved | no | ||
Call Number | Admin @ si @ KLB2014a | Serial | 2522 | ||
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Author | Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi | ||||
Title | Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars | Type | Journal Article | ||
Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 150 | Issue | A | Pages | 147-154 |
Keywords | document image analysis; stochastic context-free grammars; text classication features | ||||
Abstract | In this paper we dene a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classication features are used to perform an initial classication of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models and the results showed that the proposed grammatical model outperformed the other methods. Furthermore, grammars also provide the document structure along with its segmentation. |
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Notes | DAG; 601.158; 600.077; 600.061 | Approved | no | ||
Call Number | Admin @ si @ ACS2015 | Serial | 2531 | ||
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Author | Miguel Oliveira; Victor Santos; Angel Sappa | ||||
Title | Multimodal Inverse Perspective Mapping | Type | Journal Article | ||
Year | 2015 | Publication | Information Fusion | Abbreviated Journal | IF |
Volume | 24 | Issue | Pages | 108–121 | |
Keywords | Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles | ||||
Abstract | Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. | ||||
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Notes | ADAS; 600.055; 600.076 | Approved | no | ||
Call Number | Admin @ si @ OSS2015c | Serial | 2532 | ||
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Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
Title | Efficient segmentation-free keyword spotting in historical document collections | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 48 | Issue | 2 | Pages | 545–555 |
Keywords | Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization | ||||
Abstract | In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches. | ||||
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Notes | DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 | Approved | no | ||
Call Number | Admin @ si @ RAT2015a | Serial | 2544 | ||
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Author | Frederic Sampedro; Anna Domenech; Sergio Escalera | ||||
Title | Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans | Type | Journal Article | ||
Year | 2014 | Publication | Journal of Medical Imaging and Health Informatics | Abbreviated Journal | JMIHI |
Volume | 4 | Issue | 6 | Pages | 825-831 |
Keywords | CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION | ||||
Abstract | In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ SDE2014b | Serial | 2548 | ||
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Author | Frederic Sampedro; Sergio Escalera; Anna Puig | ||||
Title | Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 46 | Issue | Pages | 1-10 | |
Keywords | Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation | ||||
Abstract | In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ SEP2014 | Serial | 2550 | ||
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Author | Daniel Sanchez; Miguel Angel Bautista; Sergio Escalera | ||||
Title | HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs | Type | Journal Article | ||
Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 150 | Issue | A | Pages | 173–188 |
Keywords | Human limb segmentation; ECOC; Graph-Cuts | ||||
Abstract | Human multi-limb segmentation in RGB images has attracted a lot of interest in the research community because of the huge amount of possible applications in fields like Human-Computer Interaction, Surveillance, eHealth, or Gaming. Nevertheless, human multi-limb segmentation is a very hard task because of the changes in appearance produced by different points of view, clothing, lighting conditions, occlusions, and number of articulations of the human body. Furthermore, this huge pose variability makes the availability of large annotated datasets difficult. In this paper, we introduce the HuPBA8k+ dataset. The dataset contains more than 8000 labeled frames at pixel precision, including more than 120000 manually labeled samples of 14 different limbs. For completeness, the dataset is also labeled at frame-level with action annotations drawn from an 11 action dictionary which includes both single person actions and person-person interactive actions. Furthermore, we also propose a two-stage approach for the segmentation of human limbs. In a first stage, human limbs are trained using cascades of classifiers to be split in a tree-structure way, which is included in an Error-Correcting Output Codes (ECOC) framework to define a body-like probability map. This map is used to obtain a binary mask of the subject by means of GMM color modelling and GraphCuts theory. In a second stage, we embed a similar tree-structure in an ECOC framework to build a more accurate set of limb-like probability maps within the segmented user mask, that are fed to a multi-label GraphCut procedure to obtain final multi-limb segmentation. The methodology is tested on the novel HuPBA8k+ dataset, showing performance improvements in comparison to state-of-the-art approaches. In addition, a baseline of standard action recognition methods for the 11 actions categories of the novel dataset is also provided. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ SBE2015 | Serial | 2552 | ||
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Author | G. Zahnd; Simone Balocco; A. Serusclat; P. Moulin; M. Orkisz; D. Vray | ||||
Title | Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment Ultrasound in Medicine and Biology | Type | Journal Article | ||
Year | 2015 | Publication | Ultrasound in Medicine and Biology | Abbreviated Journal | UMB |
Volume | 41 | Issue | 1 | Pages | 339-345 |
Keywords | Arterial stiffness; Atherosclerosis; Common carotid artery; Longitudinal kinetics; Motion tracking; Ultrasound imaging | ||||
Abstract | Longitudinal kinetics (LOKI) of the arterial wall consists of the shearing motion of the intima-media complex over the adventitia layer in the direction parallel to the blood flow during the cardiac cycle. The aim of this study was to investigate the local variability of LOKI amplitude along the length of the vessel. By use of a previously validated motion-estimation framework, 35 in vivo longitudinal B-mode ultrasound cine loops of healthy common carotid arteries were analyzed. Results indicated that LOKI amplitude is progressively attenuated along the length of the artery, as it is larger in regions located on the proximal side of the image (i.e., toward the heart) and smaller in regions located on the distal side of the image (i.e., toward the head), with an average attenuation coefficient of -2.5 ± 2.0%/mm. Reported for the first time in this study, this phenomenon is likely to be of great importance in improving understanding of atherosclerosis mechanisms, and has the potential to be a novel index of arterial stiffness. | ||||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ ZBS2014 | Serial | 2556 | ||
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Author | Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente | ||||
Title | Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy | Type | Journal Article | ||
Year | 2014 | Publication | Journal of Biomedical Optics | Abbreviated Journal | JBiO |
Volume | 19 | Issue | 12 | Pages | 126004-1 - 126004-9 |
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Abstract | The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. | ||||
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Notes | ADAS; 600.076 | Approved | no | ||
Call Number | Admin @ si @ BLS2014 | Serial | 2563 | ||
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Author | Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera | ||||
Title | A Gesture Recognition System for Detecting Behavioral Patterns of ADHD | Type | Journal Article | ||
Year | 2016 | Publication | IEEE Transactions on System, Man and Cybernetics, Part B | Abbreviated Journal | TSMCB |
Volume | 46 | Issue | 1 | Pages | 136-147 |
Keywords | Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data | ||||
Abstract | We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. | ||||
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Notes | HuPBA; MILAB; | Approved | no | ||
Call Number | Admin @ si @ BHE2016 | Serial | 2566 | ||
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Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen | ||||
Title | Compact color texture description for texture classification | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 51 | Issue | Pages | 16-22 | |
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Abstract | Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively. |
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Notes | LAMP; 600.068; 600.079;ADAS | Approved | no | ||
Call Number | Admin @ si @ KRW2015a | Serial | 2587 | ||
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Author | Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras | ||||
Title | Multi-part body segmentation based on depth maps for soft biometry analysis | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 56 | Issue | Pages | 14-21 | |
Keywords | 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis | ||||
Abstract | This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data. | ||||
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Notes | HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB | Approved | no | ||
Call Number | Admin @ si @ MEG2015 | Serial | 2588 | ||
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Author | Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu | ||||
Title | Combining where and what in change detection for unsupervised foreground learning in surveillance | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 48 | Issue | 3 | Pages | 709-719 |
Keywords | Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance | ||||
Abstract | Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. | ||||
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Notes | ISE; 600.063; 600.078 | Approved | no | ||
Call Number | Admin @ si @ HPG2015 | Serial | 2589 | ||
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