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Xavier Roca, & Jordi Vitria. (1993). Multiscale Structure Extraction using Morphological Tools. Applications to Edge Detection. In SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV)..
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Maria Vanrell, & Jordi Vitria. (1993). Mathematical Morphology, Granulometries and Texture Perception. In SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV)..
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B. Gotschy, Matthias S. Keil, H. Klos, & I. Rystau. (1994). Transition from static to dynamic Jahn-Teller distortion in (P(C6 H5)4)2 C60|. Solid State Communications, 92(12), 935–938.
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H. Martin Kjer, Jens Fagertun, Sergio Vera, & Debora Gil. (2017). Medial structure generation for registration of anatomical structures. In Skeletonization, Theory, Methods and Applications (Vol. 11).
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Xavier Otazu, Olivier Penacchio, & Xim Cerda-Company. (2015). Brightness and colour induction through contextual influences in V1. In Scottish Vision Group 2015 SGV2015 (Vol. 12, pp. 1208–2012).
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X. Binefa, Jordi Vitria, & Xavier Roca. (1993). Deteccion de profundidad en imagenes monoculares mediante vision activa. Revista de Optica Pura y Aplicada, 26(3), 636–648.
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Josep Llados, Ernest Valveny, & Enric Marti. (2000). Symbol Recognition in Document Image Analysis: Methods and Challenges. In Recent Research Developments in Pattern Recognition, Transworld Research Network, (Vol. 1, 151–178.).
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Javier Varona, & Juan J. Villanueva. (1997). NeuroFilters: Neural Networks for image Processing. In Proceedings Volume 3101, New Image Processing Techniques and Applications: Algorithms, Methods, and Components II (Vol. 3101).
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M. Bressan, & Jordi Vitria. (2002). Independent Component Analysis and Naïve Bayes Classification. In Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501..
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Chenshen Wu, & Joost Van de Weijer. (2023). Density Map Distillation for Incremental Object Counting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2505–2514).
Abstract: We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods.
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Hao Fang, Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, & Zhen Lei. (2023). Surveillance Face Presentation Attack Detection Challenge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 6360–6370).
Abstract: Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.
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Debora Gil, Petia Radeva, & Fernando Vilariño. (2003). Anisotropic Contour Completion. In Proceedings of the IEEE International Conference on Image Processing (I-869). Barcelona, Spain.
Abstract: In this paper we introduce a novel application of the diffusion tensor for anisotropic image processing. The Anisotropic Contour Completion (ACC) we suggest consists in extending the characteristic function of the open curve by means of a degenerated diffusion tensor that prevents any diffusion in the normal direction. We show that ACC is equivalent to a dilation with a continuous elliptic structural element that takes into account the local orientation of the contours to be closed. Experiments on contours extracted from real images show that ACC produces shapes able to adapt to any curve in an active contour framework. 1.
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A. Martinez, S. Gonzalez, Jordi Vitria, & J. Lopez. (1997). NAT: a robot that recognizes offices. In Proceedings of CAEPIA–97. VII Conferencia de la Asociación Española para la Inteligencia Artificial. (pp. 327–336).
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Xose M. Pardo, Petia Radeva, & Juan J. Villanueva. (1999). Self-Training Statistic Snake for Image Segmentation and Tracking..
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V. Valev, & Petia Radeva. (1992). Determining structural descriptions by boolean formulas advances in structural and syntactic Pattern Recognition. In Proceeding of the International Workshop on Structural and Syntactic Pattern Recognition. Ed. Bunke, World Scientific Pub..
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