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Author |
E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer |
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Title |
Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
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13th IEEE International Conference on Computer Vision |
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850-857 |
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Barcelona |
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ICCV |
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MILAB |
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no |
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Admin @ si @ SRL2011 |
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1803 |
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Author |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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Year |
2011 |
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13th IEEE International Conference on Computer Vision |
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1776-1783 |
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Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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Admin @ si @ CHM2011 |
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1811 |
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Author |
Wenjuan Gong; Jürgen Brauer; Michael Arens; Jordi Gonzalez |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Modeling vs. Learning Approaches for Monocular 3D Human Pose Estimation |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2011 |
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1st IEEE International Workshop on Performance Evaluation on Recognition of Human Actions and Pose Estimation Methods |
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London, United Kingdom |
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PERHAPS |
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Admin @ si @ GBA2011 |
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1812 |
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Author |
Jordi Gonzalez; Josep M. Gonfaus; Carles Fernandez; Xavier Roca |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Exploiting Natural-Language Interaction in Video Surveillance Systems |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
V&L Net Workshop on Vision and Language |
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Brighton, UK |
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VL |
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Admin @ si @ GGF2011 |
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1813 |
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Author |
Albert Gordo; Florent Perronnin |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Asymmetric Distances for Binary Embeddings |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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729 - 736 |
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. |
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Providence, RI |
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978-1-4577-0394-2 |
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CVPR |
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DAG |
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no |
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Call Number |
Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
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1817 |
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Permanent link to this record |
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Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Title |
Descriptor-based Svm Wall Detector |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
9th International Workshop on Graphic Recognition |
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Architectural floorplans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. In this paper we describe an evolution of this new approach in two directions: firstly we evaluate different features to obtain the description of every patch. Secondly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These modifications of the method have been tested for wall detection on two datasets of architectural floorplans with different notations and compared with the results obtained with the original approach. |
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GREC |
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DAG |
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no |
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Admin @ si @ HMS2011b |
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1819 |
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Author |
Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Classification of Administrative Document Images by Logo Identification |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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Year |
2011 |
Publication |
In proceedings of 9th IAPR Workshop on Graphic Recognition |
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This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier's graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. |
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Seoul, Corea |
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GREC |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ RPK2011 |
Serial |
1821 |
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Permanent link to this record |
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Author |
Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke |
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Title |
A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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Year |
2011 |
Publication |
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing |
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83-90 |
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The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach. |
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ACM |
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978-1-4503-0916-5 |
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HIP |
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DAG |
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Admin @ si @ FFF2011a |
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1823 |
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Permanent link to this record |
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Author |
Andreas Fischer; Volkmar Frinken; Alicia Fornes; Horst Bunke |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Transcription Alignment of Latin Manuscripts Using Hidden Markov Models |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing |
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29-36 |
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Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models. |
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ACM |
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Admin @ si @ FFF2011b |
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1824 |
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Permanent link to this record |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
In proceedings of 9th IAPR Workshop on Graphic Recognition |
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Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words. |
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Seoul, Korea |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-36823-3 |
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GREC |
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DAG |
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no |
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Admin @ si @ DLP2011c |
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1825 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Correspondence Free Registration through a Point-to-Model Distance Minimization |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2011 |
Publication |
13th IEEE International Conference on Computer Vision |
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2150-2157 |
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This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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ADAS |
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no |
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Admin @ si @ RoS2011b; ADAS @ adas @ |
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1832 |
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Author |
Jürgen Brauer; Wenjuan Gong; Jordi Gonzalez; Michael Arens |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
On the Effect of Temporal Information on Monocular 3D Human Pose Estimation |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
2nd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams |
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906 - 913 |
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We address the task of estimating 3D human poses from monocular camera sequences. Many works make use of multiple consecutive frames for the estimation of a 3D pose in a frame. Although such an approach should ease the pose estimation task substantially since multiple consecutive frames allow to solve for 2D projection ambiguities in principle, it has not yet been investigated systematically how much we can improve the 3D pose estimates when using multiple consecutive frames opposed to single frame information. In this paper we analyze the difference in quality of 3D pose estimates based on different numbers of consecutive frames from which 2D pose estimates are available. We validate the use of temporal information on two major different approaches for human pose estimation – modeling and learning approaches. The results of our experiments show that both learning and modeling approaches benefit from using multiple frames opposed to single frame input but that the benefit is small when the 2D pose estimates show a high quality in terms of precision. |
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Barcelona |
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978-1-4673-0062-9 |
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ARTEMIS |
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ISE |
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no |
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Admin @ si @BGG 2011 |
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1860 |
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Permanent link to this record |
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Author |
G.D. Evangelidis; Ferran Diego; Joan Serrat; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Slice Matching for Accurate Spatio-Temporal Alignment |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2011 |
Publication |
In ICCV Workshop on Visual Surveillance |
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video alignment |
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Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works. |
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Admin @ si @ EDS2011; ADAS @ adas @ eds2011a |
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1861 |
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Gemma Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella |
![goto web page (via DOI) doi](img/doi.gif)
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Hierarchical CRF with product label spaces for parts-based Models |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2011 |
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IEEE Conference on Automatic Face and Gesture Recognition |
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657-664 |
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Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features |
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Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset. |
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Santa Barbara, CA, USA, 2011 |
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Admin @ si @ RBT2011 |
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1862 |
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Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell |
![download PDF file pdf](img/file_PDF.gif)
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Portmanteau Vocabularies for Multi-Cue Image Representation |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2011 |
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25th Annual Conference on Neural Information Processing Systems |
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We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation |
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CIC |
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no |
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Admin @ si @ KWB2011 |
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1865 |
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