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Antonio Lopez, Atsushi Imiya, Tomas Pajdla and Jose Manuel Alvarez. 2017. Computer Vision in Vehicle Technology: Land, Sea & Air. John Wiley & Sons, Ltd.
Abstract: Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition.
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Javier Marin, David Geronimo, David Vazquez and Antonio Lopez. 2012. Pedestrian Detection: Exploring Virtual Worlds. Handbook of Pattern Recognition: Methods and Application. iConcept Press, 145–162.
Abstract: Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition.
Keywords: Virtual worlds; Pedestrian Detection; Domain Adaptation
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Lluis Pere de las Heras, David Fernandez, Alicia Fornes, Ernest Valveny, Gemma Sanchez and Josep Llados. 2014. Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans. Graphics Recognition. Current Trends and Challenges. Springer Berlin Heidelberg, 135–146. (LNCS.)
Abstract: 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.
Keywords: Graphics recognition; Graphics retrieval; Image classification
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Angel Sappa and Boris X. Vintimilla. 2008. Edge Point Linking by Means of Global and Local Schemes. In E. Damiani, ed. in Signal Processing for Image Enhancement and Multimedia Processing. Springer, 115–125.
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Jose Manuel Alvarez and Antonio Lopez. 2012. Photometric Invariance by Machine Learning. In Theo Gevers, A.G., Joost van de Weijer, Jan-Mark Geusebroek, ed. Color in Computer Vision: Fundamentals and Applications. iConcept Press Ltd, 113–134.
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David Aldavert and Ricardo Toledo. 2008. Stereo Vision Local Map Alignment for Robot Environment Mapping. Robot Vision Second International Workshop, RobVis.111–124. (LNCS.)
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Lluis Pere de las Heras, Ernest Valveny and Gemma Sanchez. 2014. Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies. Graphics Recognition. Current Trends and Challenges. Springer Berlin Heidelberg, 109–121. (LNCS.)
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.
Keywords: Graphics recognition; Floor plan analysis; Object segmentation
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Cristhian Aguilera, M.Ramos and Angel Sappa. 2012. Simulated Annealing: A Novel Application of Image Processing in the Wood Area. In Marcos de Sales Guerra Tsuzuki, ed. Simulated Annealing – Advances, Applications and Hybridizations.91–104.
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Hanne Kause and 6 others. 2015. Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging. 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. Springer International Publishing, 69–79. (LNCS.)
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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Niki Aifanti, Angel Sappa, N. Grammalidis and Sotiris Malassiotis. 2009. Advances in Tracking and Recognition of Human Motion. Encyclopedia of Information Science and Technology.65–71.
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