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D. Smith. (1999). Solving the mean string problem for 2D shapes.
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D.Sanchez, J.C.Ortega, & Miguel Angel Bautista. (2013). Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 50–58). LNCS. Springer Berlin Heidelberg.
Abstract: Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches.
Keywords: Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts
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Damian Sojka, Sebastian Cygert, Bartlomiej Twardowski, & Tomasz Trzcinski. (2023). AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 3491–3495).
Abstract: Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision. Yet, current methods are usually evaluated on benchmarks that are only a simplification of real-world scenarios. Hence, we propose to validate test-time adaptation methods using the recently introduced datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift, often resulting in degraded performance that falls below that of the source model. We noticed that the root of the problem lies in the inability to preserve the knowledge of the source model and adapt to dynamically changing, temporally correlated data streams. Therefore, we enhance well-established self-training framework by incorporating a small memory buffer to increase model stability and at the same time perform dynamic adaptation based on the intensity of domain shift. The proposed method, named AR-TTA, outperforms existing approaches on both synthetic and more real-world benchmarks and shows robustness across a variety of TTA scenarios.
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Damian Sojka, Yuyang Liu, Dipam Goswami, Sebastian Cygert, Bartłomiej Twardowski, & Joost van de Weijer. (2023). Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation.
Abstract: The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset – SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available.
The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios.
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Dan Norton, Fernando Vilariño, & Onur Ferhat. (2015). Memory Field – Creative Engagement in Digital Collections. In Internet Librarian International Conference.
Abstract: “Memory Fields” is a trans-disciplinary project aiming at the (re)valorisation of digital collections.Its main deliverable is an interface for a dual screen installation, used to access and mix the public library digital collections. The collections being used in this case are a collection of digitised posters from the Spanish Civil War, belonging to the Arxiu General de Catalunya, and a collection of field recordings made by Dan Norton. The system generates visualisations, and the images and sounds are mixed together using narrative primitives of video dj. Users contribute to the digital collections by adding personal memories and observations. The comments and recollections appear as flowers growing in a “memory field” and memories remain public in a Twitter feed (@Memoryfields).
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Dani Rowe. (2005). Probabilistic Image-based Tracking in Complex Human Environments.
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Dani Rowe. (2007). Towards Robust Multiple-People Tracking in Unconstrained Environments.
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Dani Rowe. (2008). Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments.
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Dani Rowe, I. Reid, Jordi Gonzalez, & Juan J. Villanueva. (2006). Unconstrained Multiple-People Tracking. In 28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 505–514, ISBN 978–3–540–44412–1.
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Dani Rowe, Ignasi Rius, Jordi Gonzalez, & Juan J. Villanueva. (2005). Robust Particle Filtering for Object Tracking.
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Dani Rowe, Ignasi Rius, Jordi Gonzalez, & Juan J. Villanueva. (2005). Improving Tracking by Handling Occlusions.
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Dani Rowe, Ignasi Rius, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2005). Probabilistic Image-Based Tracking: Improving Particle Filtering. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 85–92.
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Dani Rowe, Ivan Huerta, Jordi Gonzalez, & Juan J. Villanueva. (2007). Robust Multiple-People Tracking Using Colour-Based Particle Filters. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120.
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Dani Rowe, Jordi Gonzalez, Ivan Huerta, & Juan J. Villanueva. (2007). On Reasoning over Tracking Events. In 15th Scandinavian Conference on Image Analysis (Vol. 4522, 502–511). LNCS.
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Dani Rowe, Jordi Gonzalez, Marco Pedersoli, & Juan J. Villanueva. (2010). On Tracking Inside Groups. MVA - Machine Vision and Applications, 21(2), 113–127.
Abstract: This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions.
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