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Author |
Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez |
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Title |
Obstacle mapping module for quadrotors on outdoor Search and Rescue operations |
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Conference Article |
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2013 |
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International Micro Air Vehicle Conference and Flight Competition |
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UAV |
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Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments. |
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Toulouse; France; September 2013 |
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IMAV |
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ADAS; 600.054; 600.057;IAM |
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Admin @ si @ NSH2013 |
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2371 |
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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
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Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
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Book Chapter |
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Year |
2015 |
Publication |
Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 |
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9534 |
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69-79 |
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Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. |
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Munich; Germany; January 2015 |
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Springer International Publishing |
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LNCS |
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0302-9743 |
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978-3-319-28711-9 |
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STACOM |
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ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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Admin @ si @ KHM2015 |
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2734 |
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Author |
Joan Serrat; Enric Marti |
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Title |
Elastic matching using interpolation splines |
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Conference Article |
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1991 |
Publication |
IV Spanish Symposium of Pattern Recognition and image Analysis |
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ADAS;IAM; |
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IAM @ iam @ SMV1991 |
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1651 |
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Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
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Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
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Conference Article |
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2016 |
Publication |
5th International Conference Games and Learning Alliance |
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10056 |
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50-59 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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GALA |
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ADAS;IAM; |
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HAC2016 |
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2864 |
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Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
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Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
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Conference Article |
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2016 |
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14th European Conference on Computer Vision Workshops |
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9915 |
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894-900 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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ADAS;IAM; 600.085; 600.076 |
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MHE2016 |
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2865 |
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Author |
Gemma Sanchez; Josep Llados; Enric Marti |
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Title |
A string-based method to recognize symbols and structural textures in architectural plans |
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Conference Article |
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1997 |
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2nd IAPR Workshop on Graphics Recognition |
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91-103 |
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This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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Nancy, France |
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DAG; IAM |
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IAM @ iam @ SLE1997 |
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1498 |
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Author |
Josep Llados; Gemma Sanchez; Enric Marti |
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Title |
A string based method to recognize symbols and structural textures in architectural plans |
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Book Chapter |
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Year |
1998 |
Publication |
Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers |
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LNCS |
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1389 |
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1998 |
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91-103 |
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This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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Springer Link |
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DAG; IAM |
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IAM @ iam @ SLE1998 |
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1573 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
A model for image generation and symbol recognition through the deformation of lineal shapes |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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15 |
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2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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IAM @ iam @ VAM2003 |
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1653 |
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Author |
Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
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Title |
Symbol recognition: current advances and perspectives |
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2002 |
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Graphics Recognition Algorithms And Applications |
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LNCS |
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2390 |
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104-128 |
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The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content. |
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London, UK |
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Springer-Verlag |
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Dorothea Blostein and Young- Bin Kwon |
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Lecture Notes in Computer Science |
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3-540-44066-6 |
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GREC |
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DAG; IAM; |
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IAM @ iam @ LVS2002 |
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1572 |
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Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
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Journal Article |
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2022 |
Publication |
Big Data Research |
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BDR |
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29 |
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100332 |
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Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
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Abstract |
Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
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August 28, 2022 |
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DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
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Admin @ si @ RBG2022a |
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3718 |
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