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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Title |
Prior Knowledge Based Motion Model Representation |
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Book Chapter |
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Year |
2009 |
Publication |
Progress in Computer Vision and Image Analysis |
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16 |
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Horst Bunke; JuanJose Villanueva; Gemma Sanchez |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ SAM2009 |
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1235 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Real Time Image Registration for Planar Structure and 3D Sensor Pose Estimation |
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Book Chapter |
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Year |
2008 |
Publication |
Stereo Vision |
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18 |
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Pages |
299–316 |
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Asim Bhatti |
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ADAS |
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no |
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ADAS @ adas @ DoS2008c |
Serial |
1057 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Interactive Training of Human Detectors |
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Book Chapter |
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Year |
2013 |
Publication |
Multiodal Interaction in Image and Video Applications |
Abbreviated Journal |
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Volume |
48 |
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Pages |
169-182 |
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Keywords |
Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation |
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Abstract |
Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations. |
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Springer Heidelberg New York Dordrecht London |
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Springer Berlin Heidelberg |
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English |
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1868-4394 |
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978-3-642-35931-6 |
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ADAS; 600.057; 600.054; 605.203 |
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VLP2013; ADAS @ adas @ vlp2013 |
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2193 |
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Author |
Angel Sappa; Jordi Vitria |
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Title |
Multimodal Interaction in Image and Video Applications |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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48 |
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Abstract |
Book Series Intelligent Systems Reference Library |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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Notes |
ADAS; OR;MV |
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no |
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Call Number |
Admin @ si @ SaV2013 |
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2199 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez |
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Title |
Moving object detection from mobile platforms using stereo data registration |
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Book Chapter |
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Year |
2012 |
Publication |
Computational Intelligence paradigms in advanced pattern classification |
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Volume |
386 |
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Pages |
25-37 |
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Keywords |
pedestrian detection |
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Abstract |
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. |
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Springer Berlin Heidelberg |
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Editor |
Marek R. Ogiela; Lakhmi C. Jain |
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1860-949X |
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978-3-642-24048-5 |
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ADAS |
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no |
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Call Number |
Admin @ si @ SGD2012 |
Serial |
2061 |
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Author |
David Aldavert; Ricardo Toledo |
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Title |
Stereo Vision Local Map Alignment for Robot Environment Mapping |
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Book Chapter |
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Year |
2008 |
Publication |
Robot Vision Second International Workshop, RobVis |
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Volume |
4931 |
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Pages |
111–124 |
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Auckland (New Zealand) |
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ADAS |
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Call Number |
Admin @ si @ AlT2008 |
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1100 |
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Author |
Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat |
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Title |
Pattern Recognition and Image Analysis |
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2007 |
Publication |
3rd Iberian Conference |
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6669 |
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4477-4478 |
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Girona (Spain) |
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IbPRIA |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ MBM2007 |
Serial |
994 |
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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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109-121 |
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Keywords |
Graphics recognition; Floor plan analysis; Object segmentation |
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Abstract |
In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions. |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
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Notes |
DAG; ADAS; 600.076; 600.077 |
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Call Number |
Admin @ si @ HVS2014 |
Serial |
2535 |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
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Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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135-146 |
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Graphics recognition; Graphics retrieval; Image classification |
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This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 |
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no |
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Admin @ si @ HFF2014 |
Serial |
2536 |
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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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Volume |
9534 |
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69-79 |
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Abstract |
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 |
ISBN |
978-3-319-28711-9 |
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STACOM |
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Notes |
ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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Call Number |
Admin @ si @ KHM2015 |
Serial |
2734 |
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