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Craig Von Land, Ricardo Toledo, & Juan J. Villanueva. (1996). Object Oriented Design of the DICOM standard.
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David Masip, & Jordi Vitria. (2004). Object Recognition using Boosted Adaptive Features..
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Craig Von Land, V. Lashin, A. Oriol, & Juan J. Villanueva. (1997). Object-oriented Design of the DICOM Standard and its Application to Cardiovascular Imaging..
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V. Valev, & Petia Radeva. (1993). On the Determining of Non-Reducible Descriptors for Multidimensional Pattern Recognition Problems..
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Akhil Gurram, & Antonio Lopez. (2023). On the Metrics for Evaluating Monocular Depth Estimation.
Abstract: Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose.
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David Masip, & Jordi Vitria. (2003). On the Nearest Neighbor Approach for Gender Recognition.
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David Masip, & Jordi Vitria. (2003). On the Nearest Neighbor Approach for Gender Recognition.
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Alejandro Cartas, Estefania Talavera, Petia Radeva, & Mariella Dimiccoli. (2018). On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams.
Abstract: Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users.
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Petia Radeva. (2003). On the Role of Intravascular Ultrasound Image Analysis.
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A. Pujol, Alex Caralps, & Juan J. Villanueva. (2001). On the suitability of pixel-outlier removal in face recognition..
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N. Zakaria, Jean-Marc Ogier, & Josep Llados. (2005). On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix.
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Jordi Gonzalez, Javier Varona, Juan J. Villanueva, & Xavier Roca. (2001). On-line Human Activity Recognition for Video Surveillance..
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Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, & Joost Van de Weijer. (2022). One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift.
Abstract: In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a (
+1)-way classifier to predict the
source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show:
After source training, the resulting source model can get excellent performance for
;
After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method.
When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.
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Oriol Pujol, & Petia Radeva. (2006). Optimal extension of Error Correcting Output Codes.
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J. Weickert, Bart M. Ter Haar Romeny, Antonio Lopez, & W. Van Enk. (1997). Orientation Analysis by Coherence-Enhancing Diffusion..
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