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Author Karel Paleček; David Geronimo; Frederic Lerasle
Title Pre-attention cues for person detection Type (up) Conference Article
Year 2012 Publication Cognitive Behavioural Systems, COST 2102 International Training School Abbreviated Journal
Volume Issue Pages 225-235
Keywords
Abstract 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.
Address Dresden, Germany
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34583-8 Medium
Area Expedition Conference COST-TS
Notes ADAS Approved no
Call Number Admin @ si @ PGL2012 Serial 2148
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Author Petia Radeva; Michal Drozdzal; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria
Title Active labeling: Application to wireless endoscopy analysis Type (up) Conference Article
Year 2012 Publication High Performance Computing and Simulation, International Conference on Abbreviated Journal
Volume Issue Pages 174-181
Keywords
Abstract Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4673-2359-8 Medium
Area Expedition Conference HPCS
Notes MILAB; OR;MV Approved no
Call Number Admin @ si @ RDS2012 Serial 2152
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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez
Title Video Co-segmentation Type (up) Conference Article
Year 2012 Publication 11th Asian Conference on Computer Vision Abbreviated Journal
Volume 7725 Issue Pages 13-24
Keywords
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.
Address Daejeon, Korea
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-37443-2 Medium
Area Expedition Conference ACCV
Notes ADAS Approved no
Call Number Admin @ si @ RSL2012d Serial 2153
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Author Monica Piñol; Angel Sappa; Ricardo Toledo
Title MultiTable Reinforcement for Visual Object Recognition Type (up) Conference Article
Year 2012 Publication 4th International Conference on Signal and Image Processing Abbreviated Journal
Volume 221 Issue Pages 469-480
Keywords
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.
Address Coimbatore, India
Corporate Author Thesis
Publisher Springer India Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 1876-1100 ISBN 978-81-322-0996-6 Medium
Area Expedition Conference ICSIP
Notes ADAS Approved no
Call Number Admin @ si @ PST2012 Serial 2157
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Author Mohammad Rouhani; Angel Sappa
Title Non-Rigid Shape Registration: A Single Linear Least Squares Framework Type (up) Conference Article
Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal
Volume 7578 Issue Pages 264-277
Keywords
Abstract 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.
Address Florencia
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33785-7 Medium
Area Expedition Conference ECCV
Notes ADAS Approved no
Call Number Admin @ si @ RoS2012a Serial 2158
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Author Miguel Oliveira; V.Santos; Angel Sappa
Title Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition Type (up) Conference Article
Year 2012 Publication IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Algarve; Portugal
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference PPNIV
Notes ADAS Approved no
Call Number Admin @ si @ OSS2012c Serial 2159
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Author Marina Alberti; Simone Balocco; Xavier Carrillo; Josepa Mauri; Petia Radeva
Title Automatic Non-Rigid Temporal Alignment of IVUS Sequences Type (up) Conference Article
Year 2012 Publication 15th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume 1 Issue Pages 642-650
Keywords
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.
Address Nice, France
Corporate Author Thesis
Publisher Springer-Verlag Berlin, Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-33414-6 Medium
Area Expedition Conference MICCAI
Notes MILAB Approved no
Call Number Admin @ si @ ABC2012 Serial 2168
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Author Pedro Martins; Paulo Carvalho; Carlo Gatta
Title Stable Salient Shapes Type (up) Conference Article
Year 2012 Publication International Conference on Digital Image Computing: Techniques and Applications Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DICTA
Notes MILAB Approved no
Call Number Admin @ si @ MCG2012b Serial 2166
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Author Jaume Gibert; Ernest Valveny; Horst Bunke; Alicia Fornes
Title On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space Type (up) Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages 135-143
Keywords
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.
Address
Corporate Author Thesis
Publisher Springer-Berlag, Berlin Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes DAG Approved no
Call Number Admin @ si @ GVB2012c Serial 2167
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Author Rui Hua; Oriol Pujol; Francesco Ciompi; Marina Alberti; Simone Balocco; Josepa Mauri; Petia Radeva
Title Stent Strut Detection by Classifying a Wide Set of IVUS Features Type (up) Conference Article
Year 2012 Publication Computed Assisted Stenting Workshop Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Nice, France
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference STENT
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ HPC2012 Serial 2169
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Author Adela Barbulescu; Wenjuan Gong; Jordi Gonzalez; Thomas B. Moeslund; Xavier Roca
Title 3D Human Pose Estimation Using 2D Body Part Detectors Type (up) Conference Article
Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2484 - 2487
Keywords
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.
Address Tsubuka, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium
Area Expedition Conference ICPR
Notes ISE Approved no
Call Number Admin @ si @ BGG2012 Serial 2172
Permanent link to this record
 
 
Author Fadi Dornaika; A.Assoum; Bogdan Raducanu
Title Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection Type (up) Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages 575-583
Keywords
Abstract 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.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes OR;MV Approved no
Call Number Admin @ si @ DAR2012 Serial 2174
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika
Title Out-of-Sample Embedding by Sparse Representation Type (up) Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages 336-344
Keywords
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.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes OR;MV Approved no
Call Number Admin @ si @ RaD2012c Serial 2175
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika
Title Pose-Invariant Face Recognition in Videos for Human-Machine Interaction Type (up) Conference Article
Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal
Volume 7584 Issue Pages 566.575
Keywords
Abstract 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.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33867-0 Medium
Area Expedition Conference ECCVW
Notes OR;MV Approved no
Call Number Admin @ si @ RaD2012e Serial 2182
Permanent link to this record
 
 
Author Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez
Title Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features Type (up) Conference Article
Year 2012 Publication 12th European Conference on Computer Vision – Workshops and Demonstrations Abbreviated Journal
Volume 7584 Issue Pages 586-595
Keywords road detection
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.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33867-0 Medium
Area Expedition Conference ECCVW
Notes ADAS;ISE Approved no
Call Number Admin @ si @ ALG2012; ADAS @ adas Serial 2187
Permanent link to this record