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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Video Co-segmentation |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th Asian Conference on Computer Vision |
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Volume |
7725 |
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Pages |
13-24 |
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Abstract |
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos. |
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Daejeon, Korea |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-37443-2 |
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ACCV |
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ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012d |
Serial |
2153 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
MultiTable Reinforcement for Visual Object Recognition |
Type |
Conference Article |
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Year |
2012 |
Publication |
4th International Conference on Signal and Image Processing |
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Volume |
221 |
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Pages |
469-480 |
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Abstract |
This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach. |
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Coimbatore, India |
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Springer India |
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ISSN |
1876-1100 |
ISBN |
978-81-322-0996-6 |
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ICSIP |
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ADAS |
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no |
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Call Number |
Admin @ si @ PST2012 |
Serial |
2157 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Non-Rigid Shape Registration: A Single Linear Least Squares Framework |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
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Volume |
7578 |
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Pages |
264-277 |
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This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided. |
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Address |
Florencia |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-33785-7 |
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ECCV |
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ADAS |
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no |
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Call Number |
Admin @ si @ RoS2012a |
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2158 |
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Author |
Miguel Oliveira; V.Santos; Angel Sappa |
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Title |
Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles |
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Address |
Algarve; Portugal |
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PPNIV |
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ADAS |
Approved |
no |
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Call Number |
Admin @ si @ OSS2012c |
Serial |
2159 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Automatic Non-Rigid Temporal Alignment of IVUS Sequences |
Type |
Conference Article |
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Year |
2012 |
Publication |
15th International Conference on Medical Image Computing and Computer Assisted Intervention |
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Volume |
1 |
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Pages |
642-650 |
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Abstract |
Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability. |
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Address |
Nice, France |
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Publisher |
Springer-Verlag Berlin, Heidelberg |
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978-3-642-33414-6 |
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MICCAI |
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Notes |
MILAB |
Approved |
no |
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Call Number |
Admin @ si @ ABC2012 |
Serial |
2168 |
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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
Stable Salient Shapes |
Type |
Conference Article |
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Year |
2012 |
Publication |
International Conference on Digital Image Computing: Techniques and Applications |
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DICTA |
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MILAB |
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no |
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Call Number |
Admin @ si @ MCG2012b |
Serial |
2166 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke; Alicia Fornes |
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Title |
On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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Volume |
7626 |
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Pages |
135-143 |
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Abstract |
Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected. |
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Springer-Berlag, Berlin |
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LNCS |
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ISBN |
978-3-642-34165-6 |
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SSPR&SPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GVB2012c |
Serial |
2167 |
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Author |
Rui Hua; Oriol Pujol; Francesco Ciompi; Marina Alberti; Simone Balocco; J. Mauri; Petia Radeva |
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Title |
Stent Strut Detection by Classifying a Wide Set of IVUS Features |
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Conference Article |
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Year |
2012 |
Publication |
Computed Assisted Stenting Workshop |
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Address |
Nice, France |
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STENT |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ HPC2012 |
Serial |
2169 |
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Permanent link to this record |
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Author |
Adela Barbulescu; Wenjuan Gong; Jordi Gonzalez; Thomas B. Moeslund; Xavier Roca |
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Title |
3D Human Pose Estimation Using 2D Body Part Detectors |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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Pages |
2484 - 2487 |
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Abstract |
Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as support vector machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additional input data, such as silhouettes, or controlled camera settings. We present a framework that is capable of estimating the 3D pose of a person from single images or monocular image sequences without requiring background information and which is robust to camera variations. The framework models the non-linearity present in human pose estimation as it benefits from flexible learning approaches, including a highly customizable 2D detector. Results on the HumanEva benchmark show how they perform and influence the quality of the 3D pose estimates. |
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Tsubuka, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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ISE |
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no |
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Call Number |
Admin @ si @ BGG2012 |
Serial |
2172 |
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Author |
Fadi Dornaika; A.Assoum; Bogdan Raducanu |
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Title |
Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
Abbreviated Journal |
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Volume |
7626 |
Issue |
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Pages |
575-583 |
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A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis. |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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OR;MV |
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no |
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Call Number |
Admin @ si @ DAR2012 |
Serial |
2174 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Out-of-Sample Embedding by Sparse Representation |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
Abbreviated Journal |
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Volume |
7626 |
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Pages |
336-344 |
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Abstract |
A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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OR;MV |
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no |
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Admin @ si @ RaD2012c |
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2175 |
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Permanent link to this record |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Pose-Invariant Face Recognition in Videos for Human-Machine Interaction |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
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7584 |
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566.575 |
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Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-33867-0 |
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ECCVW |
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OR;MV |
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no |
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Admin @ si @ RaD2012e |
Serial |
2182 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez |
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Title |
Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
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7584 |
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586-595 |
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road detection |
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Abstract |
Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-33867-0 |
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ECCVW |
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ADAS;ISE |
Approved |
no |
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Call Number |
Admin @ si @ ALG2012; ADAS @ adas |
Serial |
2187 |
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Permanent link to this record |
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Author |
Jordi Roca; Maria Vanrell; C. Alejandro Parraga |
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Title |
What is constant in colour constancy? |
Type |
Conference Article |
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Year |
2012 |
Publication |
6th European Conference on Colour in Graphics, Imaging and Vision |
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337-343 |
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Color constancy refers to the ability of the human visual system to stabilize
the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy
algorithms [3] for estimating the scene illuminant. |
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9781622767014 |
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RVP2012 |
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2189 |
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David Fernandez; Josep Llados; Alicia Fornes; R.Manmatha |
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On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts |
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2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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763-768 |
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document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding |
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he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20% |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
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Admin @ si @ FLF2012 |
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2200 |
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