|
Antonio Lopez and 6 others. 2000. New improvements in the multiscale analysis of trabecular bone patterns. Pattern Recognition and Applications. IOS Press, 251–260.
|
|
|
Antonio Lopez, Jiaolong Xu, Jose Luis Gomez, David Vazquez and German Ros. 2017. From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example. In Gabriela Csurka, ed. Domain Adaptation in Computer Vision Applications. Springer, 243–258.
Abstract: Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world.
Keywords: Domain Adaptation
|
|
|
Antonio Lopez, David Vazquez and Gabriel Villalonga. 2018. Data for Training Models, Domain Adaptation. Intelligent Vehicles. Enabling Technologies and Future Developments.395–436.
Abstract: Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection.
Keywords: Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data
|
|
|
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.
|
|
|
Antonio Lopez. 2000. Multilocal Methods for Ridge and Valley Delineation in Image Analysis. (Ph.D. thesis, .)
|
|
|
Antonio Lopez. 2018. Pedestrian Detection Systems. Wiley Encyclopedia of Electrical and Electronics Engineering.
Abstract: Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning.
|
|
|
Angel Sappa, Niki Aifanti, Sotiris Malassiotis and Michael G. Strintzis. 2009. Prior Knowledge Based Motion Model Representation. In Horst Bunke, JuanJose Villanueva and Gemma Sanchez, eds. Progress in Computer Vision and Image Analysis.
|
|
|
Angel Sappa, Niki Aifanti, N. Grammalidis and Sotiris Malassiotis. 2004. Advances in Vision-Based Human Body Modeling. In N. Sarris and M. Strintzis., ed. 3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body.1–26.
|
|
|
Angel Sappa and Jordi Vitria. 2013. Multimodal Interaction in Image and Video Applications. Springer Berlin Heidelberg.
Abstract: Book Series Intelligent Systems Reference Library
|
|
|
Angel Sappa and George A. Triantafyllid. 2012. Computer Graphics and Imaging.
|
|