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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Dimosthenis Karatzas |
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Title |
Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model |
Type |
Journal Article |
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Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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Volume |
13 |
Issue |
3 |
Pages |
229–241 |
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Abstract |
One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes. |
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Springer-Verlag |
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1433-2833 |
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DAG; IF 2009: 1,213 |
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no |
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DAG @ dag @ FLS2010a |
Serial |
1288 |
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Author |
Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva |
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Title |
On Tracking Inside Groups |
Type |
Journal Article |
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Year |
2010 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVA |
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Volume |
21 |
Issue |
2 |
Pages |
113–127 |
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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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Springer-Verlag |
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0932-8092 |
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ISE |
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no |
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ISE @ ise @ RGP2010 |
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1158 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic sign recognition system with β -correction |
Type |
Journal Article |
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Year |
2010 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVA |
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Volume |
21 |
Issue |
2 |
Pages |
99–111 |
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Abstract |
Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. |
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Springer-Verlag |
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0932-8092 |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2010a |
Serial |
1276 |
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Author |
Muhammad Muzzamil Luqman; Josep Llados; Jean-Yves Ramel; Thierry Brouard |
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Title |
A Fuzzy-Interval Based Approach For Explicit Graph Embedding, Recognizing Patterns in Signals, Speech, Images and Video |
Type |
Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
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Volume |
6388 |
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Pages |
93–98 |
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Abstract |
We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR. |
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Springer, Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-17710-1 |
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ICPR |
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DAG |
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no |
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Call Number |
DAG @ dag @ LLR2010 |
Serial |
1459 |
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Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Title |
Toward the Detection of Urban Infrastructures Edge Shadows |
Type |
Conference Article |
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Year |
2010 |
Publication |
12th International Conference on Advanced Concepts for Intelligent Vision Systems |
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Volume |
6474 |
Issue |
I |
Pages |
30–37 |
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Abstract |
In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising. |
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Sydney, Australia |
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Springer Berlin Heidelberg |
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Editor |
eds. Blanc–Talon et al |
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0302-9743 |
ISBN |
978-3-642-17687-6 |
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ACIVS |
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Notes |
OR;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ ISR2010 |
Serial |
1458 |
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Author |
Jaume Garcia; Debora Gil; Aura Hernandez-Sabate |
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Title |
Endowing Canonical Geometries to Cardiac Structures |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Statistical Atlases And Computational Models Of The Heart |
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Volume |
6364 |
Issue |
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Pages |
124-133 |
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Abstract |
International conference on Cardiac electrophysiological simulation challenge
In this paper, we show that canonical (shape-based) geometries can be endowed to cardiac structures using tubular coordinates defined over their medial axis. We give an analytic formulation of these geometries by means of B-Splines. Since B-Splines present vector space structure PCA can be applied to their control points and statistical models relating boundaries and the interior of the anatomical structures can be derived. We demonstrate the applicability in two cardiac structures, the 3D Left Ventricular volume, and the 2D Left-Right ventricle set in 2D Short Axis view. |
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Springer Berlin / Heidelberg |
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Editor |
Camara, O.; Pop, M.; Rhode, K.; Sermesant, M.; Smith, N.; Young, A. |
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Lecture Notes in Computer Science |
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LNCS |
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Notes |
IAM |
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no |
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Call Number |
IAM @ iam @ GGH2010b |
Serial |
1515 |
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Permanent link to this record |
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Author |
Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Juan J. Villanueva |
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Title |
Recursive Coarse-to-Fine Localization for fast Object Recognition |
Type |
Conference Article |
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Year |
2010 |
Publication |
11th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
6313 |
Issue |
II |
Pages |
280–293 |
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Abstract |
Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter. |
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Address |
Crete (Greece) |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-15566-6 |
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Conference |
ECCV |
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Notes |
ISE |
Approved |
no |
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Call Number |
DAG @ dag @ PGB2010 |
Serial |
1438 |
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Permanent link to this record |
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Author |
Carles Fernandez; Jordi Gonzalez; Xavier Roca |
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Title |
Automatic Learning of Background Semantics in Generic Surveilled Scenes |
Type |
Conference Article |
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Year |
2010 |
Publication |
11th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
6313 |
Issue |
II |
Pages |
678–692 |
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Abstract |
Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically infer semantics in locations where motion occurs, and such inferences are typically limited to abnormality. Thus, it is interesting to design contributions that automatically categorize more specific semantic regions. State-of-the-art approaches for unsupervised scene labeling exploit trajectory data to segment areas like sources, sinks, or waiting zones. Our method, in addition, incorporates scene-independent knowledge to assign more meaningful labels like crosswalks, sidewalks, or parking spaces. First, a spatiotemporal scene model is obtained from trajectory analysis. Subsequently, a so-called GI-MRF inference process reinforces spatial coherence, and incorporates taxonomy-guided smoothness constraints. Our method achieves automatic and effective labeling of conceptual regions in urban scenarios, and is robust to tracking errors. Experimental validation on 5 surveillance databases has been conducted to assess the generality and accuracy of the segmentations. The resulting scene models are used for model-based behavior analysis. |
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Address |
Crete (Greece) |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-15551-2 |
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ECCV |
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Notes |
ISE |
Approved |
no |
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Call Number |
ISE @ ise @ FGR2010 |
Serial |
1439 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny |
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Title |
Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. |
Type |
Conference Article |
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Year |
2010 |
Publication |
13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition |
Abbreviated Journal |
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Volume |
6218 |
Issue |
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Pages |
223–232 |
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Abstract |
Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster. |
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Springer Berlin Heidelberg |
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Editor |
In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano, |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-14979-5 |
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S+SSPR |
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DAG |
Approved |
no |
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Call Number |
DAG @ dag @ GiV2010 |
Serial |
1416 |
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Permanent link to this record |
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Author |
Wenjuan Gong; Andrew Bagdanov; Xavier Roca; Jordi Gonzalez |
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Title |
Automatic Key Pose Selection for 3D Human Action Recognition |
Type |
Conference Article |
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Year |
2010 |
Publication |
6th International Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
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Volume |
6169 |
Issue |
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Pages |
290–299 |
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Abstract |
This article describes a novel approach to the modeling of human actions in 3D. The method we propose is based on a “bag of poses” model that represents human actions as histograms of key-pose occurrences over the course of a video sequence. Actions are first represented as 3D poses using a sequence of 36 direction cosines corresponding to the angles 12 joints form with the world coordinate frame in an articulated human body model. These pose representations are then projected to three-dimensional, action-specific principal eigenspaces which we refer to as aSpaces. We introduce a method for key-pose selection based on a local-motion energy optimization criterion and we show that this method is more stable and more resistant to noisy data than other key-poses selection criteria for action recognition. |
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Springer Verlag |
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0302-9743 |
ISBN |
978-3-642-14060-0 |
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AMDO |
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Notes |
ISE |
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no |
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Call Number |
DAG @ dag @ GBR2010 |
Serial |
1317 |
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Permanent link to this record |
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Author |
Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
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Title |
3D Texton Spaces for color-texture retrieval |
Type |
Conference Article |
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Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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Volume |
6111 |
Issue |
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Pages |
354–363 |
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Abstract |
Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel. |
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Springer Berlin Heidelberg |
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Editor |
A.C. Campilho and M.S. Kamel |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-13771-6 |
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ICIAR |
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Notes |
CIC |
Approved |
no |
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Call Number |
CAT @ cat @ ASV2010a |
Serial |
1325 |
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Permanent link to this record |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
Type |
Conference Article |
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Year |
2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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Volume |
6111 |
Issue |
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Pages |
230-239 |
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Abstract |
This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
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Address |
Povoa de Varzim (Portugal) |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-13771-6 |
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ICIAR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ OnS2010 |
Serial |
1342 |
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Permanent link to this record |
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Author |
Salim Jouili; Salvatore Tabbone; Ernest Valveny |
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Title |
Comparing Graph Similarity Measures for Graphical Recognition |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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Volume |
6020 |
Issue |
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Pages |
37-48 |
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Abstract |
In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique. |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-13727-3 |
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GREC |
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DAG |
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no |
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Admin @ si @ JTV2010 |
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2404 |
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Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman |
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Title |
A Performance Characterization Algorithm for Symbol Localization |
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Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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260–271 |
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Abstract |
In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols). |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-13727-3 |
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Conference |
GREC |
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Notes |
DAG |
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no |
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Call Number |
Admin @ si @ DRV2010 |
Serial |
2406 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Touching Text Character Localization in Graphical Documents using SIFT |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
Abbreviated Journal |
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Volume |
6020 |
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Pages |
199-211 |
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Keywords |
Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform |
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Abstract |
Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches. |
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Publisher |
Springer Berlin Heidelberg |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-13727-3 |
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Area |
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Expedition |
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Conference |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ RPL2010c |
Serial |
2408 |
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Permanent link to this record |