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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
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Title |
Deep semantic pyramids for human attributes and action recognition |
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Conference Article |
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Year |
2015 |
Publication |
Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 |
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9127 |
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341-353 |
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Keywords |
Action recognition; Human attributes; Semantic pyramids |
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Abstract |
Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature. |
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Denmark; Copenhagen; June 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-19664-0 |
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SCIA |
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LAMP; 600.068; 600.079;ADAS |
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Admin @ si @ KRW2015b |
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2672 |
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Author |
Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez |
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Title |
Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning |
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Conference Article |
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Year |
2023 |
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International Conference on Intelligent Robots and Systems |
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On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning. |
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Detroit; USA; October 2023 |
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IROS |
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ADAS |
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no |
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Admin @ si @ XCP2023 |
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3930 |
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Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca |
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Title |
Transformer based Image Dehazing |
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Conference Article |
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2022 |
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16th IEEE International Conference on Signal Image Technology & Internet Based System |
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atmospheric light; brightness component; computational cost; dehazing quality; haze-free image |
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This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models. |
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Dijon; France; October 2022 |
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SITIS |
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MSIAU; no proj |
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no |
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Admin @ si @ SCS2022 |
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3803 |
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Author |
Subhajit Maity; Sanket Biswas; Siladittya Manna; Ayan Banerjee; Josep Llados; Saumik Bhattacharya; Umapada Pal |
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Title |
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference on Doccument Analysis and Recognition |
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14187 |
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342–360 |
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Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: this https URL |
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Document Layout Analysis; Document |
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ICDAR |
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DAG |
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no |
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Call Number |
Admin @ si @ MBM2023 |
Serial |
3990 |
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Author |
Karel Paleček; David Geronimo; Frederic Lerasle |
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Title |
Pre-attention cues for person detection |
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Conference Article |
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Year |
2012 |
Publication |
Cognitive Behavioural Systems, COST 2102 International Training School |
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225-235 |
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Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector. |
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Dresden, Germany |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-34583-8 |
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COST-TS |
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ADAS |
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no |
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Call Number |
Admin @ si @ PGL2012 |
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2148 |
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Author |
Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados |
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Title |
Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval |
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Conference Article |
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2011 |
Publication |
33rd European Conference on Information Retrieval |
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6611 |
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314-325 |
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In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset. |
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Dublin, Ireland |
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Springer |
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Berlin |
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P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch |
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978-3-642-20160-8 |
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ECIR |
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DAG; RV;ADAS |
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no |
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Call Number |
Admin @ si @ RAK2011 |
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1737 |
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Author |
Enric Marti; Debora Gil; Carme Julia |
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Title |
A PBL experience in the teaching of Computer Graphics |
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Conference Article |
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2005 |
Publication |
EUROGRAPHICS Proceedings |
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5 |
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1 |
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95-103 |
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project-based learning; computer graphics education; Open GL; rendering techniques; computer animation techniques; Graphics packages; Hierarchy and geometric transformations; Animation; Color; shading; shadowing and texture; fractals; hidden line/surface removal; Problem Based Learning |
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Abstract |
Project-Based Learning (PBL) is an educational strategy to improve student’s learning capability that, in recent years, has had a progressive acceptance in undergraduate studies. This methodology is based on solving a problem or project in a student working group. In this way, PBL focuses on learning the necessary tools to correctly find a solution to given problems. Since the learning initiative is transferred to the student, the PBL method promotes students own abilities. This allows a better assessment of the true workload that carries out the student in the subject. It follows that the methodology conforms to the guidelines of the Bologna document, which quantifies the student workload in a subject by means of the European credit transfer system (ECTS). PBL is currently applied in undergraduate studies needing strong practical training such as medicine, nursing or law sciences. Although this is also the case in engineering studies, amazingly, few experiences have been reported. In this paper we propose to use PBL in the educational organization of the Computer Graphics subjects in the Computer Science degree. Our PBL project focuses in the development of a C++ graphical environment based on the OpenGL libraries for visualization and handling of different graphical objects. The starting point is a basic skeleton that already includes lighting functions, perspective projection with mouse interaction to change the point of view and three predefined objects. Students have to complete this skeleton by adding their own functions to solve the project. A total number of 10 projects have been proposed and successfully solved. The exercises range from human face rendering to articulated objects, such as robot arms or puppets. In the present paper we extensively report the statement and educational objectives for two of the projects: solar system visualization and a chess game. We report our earlier educational experience based on the standard classroom theoretical, problem and practice sessions and the reasons that motivated searching for other learning methods. We have mainly chosen PBL because it improves the student learning initiative. We have applied the PBL educational model since the beginning of the second semester. The student’s feedback increases in his interest for the subject. We present a comparative study of the teachers’ and students’ workload between PBL and the classic teaching approach, which suggests that the workload increase in PBL is not as high as it seems. |
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Dublin; Ireland; September 2005 |
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EUROGRAPHICS |
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IAM;ADAS; |
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IAM @ iam @ MGJ2005 |
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1593 |
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Author |
Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen |
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Title |
Multi-modal Deep Learning Approach for Flood Detection |
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Conference Article |
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2017 |
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MediaEval Benchmarking Initiative for Multimedia Evaluation |
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In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task. |
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Dublin; Ireland; September 2017 |
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MediaEval |
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LAMP; 600.084; 600.109; 600.120 |
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Admin @ si @ LWB2017a |
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2974 |
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Author |
Jaime Lopez-Krahe; Josep Llados; Enric Marti |
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Architectural Floor Plan Analysis |
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2000 |
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CVonline |
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Edimburg, UK |
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University of Edinburgh |
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Robert B. Fisher |
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IAM @ iam @ LLM2000 |
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1561 |
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Fadi Dornaika; Angel Sappa |
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Real Time on Board Stereo Camera Pose through Image Registration |
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2008 |
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IEEE Intelligent Vehicles Symposium, |
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804–809 |
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Eindhoven (Netherlands) |
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ADAS @ adas @ DoS2008a |
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1015 |
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Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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Illuminant Invariant Model-Based Road Segmentation |
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2008 |
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IEEE Intelligent Vehicles Symposium, |
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1155–1180 |
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road detection |
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Eindhoven (The Netherlands) |
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ADAS @ adas @ ALB2008 |
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1045 |
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Ignasi Rius; Dani Rowe; Jordi Gonzalez; Xavier Roca |
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A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking |
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2005 |
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Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 529–536 |
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Estoril (Portugal) |
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ISE |
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ISE @ ise @ RRG2005b |
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544 |
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Dani Rowe; Ignasi Rius; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva |
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Probabilistic Image-Based Tracking: Improving Particle Filtering |
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2005 |
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Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 85–92 |
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ISE |
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Call Number |
ISE @ ise @ RRG2005a |
Serial |
545 |
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Author |
Agata Lapedriza; David Masip; Jordi Vitria |
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Title |
The contribution of external features to face recognition |
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Book Chapter |
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2005 |
Publication |
Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 537–544 |
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Estoril (Portugal) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ LMV2005a |
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546 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Efficient Object-Class Recognition by Boosting Contextual Information |
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Miscellaneous |
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2005 |
Publication |
Pattern Recognition and Image Analysis, IbPRIA 2005, LNCS 3522:28–35 |
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ADAS;MILAB |
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no |
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Call Number |
ADAS @ adas @ ASR2005b |
Serial |
554 |
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| Permanent link to this record |
