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Author |
Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models |
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2023 |
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Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” |
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SENS |
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23 |
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2 |
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621 |
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Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving |
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Abstract |
Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall
procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our
procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results. |
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ADAS; no proj |
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no |
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Admin @ si @ GVL2023 |
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3705 |
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Author |
P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
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Title |
Feature Point Descriptors: Infrared and Visible Spectra |
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2014 |
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Sensors |
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SENS |
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14 |
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2 |
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3690-3701 |
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This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. |
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ADAS;600.055; 600.076 |
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Admin @ si @ RCA2014a |
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2474 |
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Author |
Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
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Title |
Monocular visual odometry: A cross-spectral image fusion based approach |
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Journal Article |
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2016 |
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Robotics and Autonomous Systems |
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RAS |
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85 |
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26-36 |
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Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion |
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This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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Elsevier B.V. |
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ADAS;600.086; 600.076 |
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Admin @ si @SAC2016 |
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2811 |
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Author |
Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
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Title |
Real-time quality control of surgical material packaging by artificial vision |
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2005 |
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Assembly Automation |
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25 |
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3 |
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Abstract |
IF: 0.061) |
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ADAS;DAG |
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ADAS @ adas @ LVV2005 |
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552 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning photometric invariance for object detection |
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2010 |
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International Journal of Computer Vision |
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IJCV |
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90 |
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1 |
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45-61 |
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road detection |
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Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods |
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Springer US |
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0920-5691 |
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ADAS;ISE |
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ADAS @ adas @ AGL2010c |
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1451 |
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Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez |
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Title |
Road Geometry Classification by Adaptative Shape Models |
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Journal Article |
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2013 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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14 |
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1 |
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459-468 |
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road detection |
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Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions. |
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1524-9050 |
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ADAS;ISE |
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Admin @ si @ AGD2013;; ADAS @ adas @ |
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2269 |
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Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca |
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Quality control of safety belts by machine vision inspection for real-time production |
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2003 |
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Optical Engineering (IF: 0.877) |
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42 |
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4 |
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1114-1120 |
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SPIE |
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ADAS;ISE;CIC |
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ADAS @ adas @ PRL2003 |
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399 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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2005 |
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Pattern Recognition Letters |
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PRL |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS @ adas @ AmR2005b |
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592 |
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Jaume Amores; Petia Radeva |
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Retrieval of IVUS Images Using Contextual Information and Elastic Matching |
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2005 |
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International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) |
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ADAS @ adas @ AmR2005a |
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593 |
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Jaume Amores; N. Sebe; Petia Radeva |
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Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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3 |
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201–209 |
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ADAS @ adas @ ASR2006 |
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643 |
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