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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 |
Abbreviated Journal |
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Volume |
7578 |
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Pages |
264-277 |
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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. |
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Address |
Florencia |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33785-7 |
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Conference |
ECCV |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ RoS2012a |
Serial |
2158 |
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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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Volume |
7584 |
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Pages |
586-595 |
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Keywords |
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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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33867-0 |
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ECCVW |
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Notes |
ADAS;ISE |
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no |
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Call Number |
Admin @ si @ ALG2012; ADAS @ adas |
Serial |
2187 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
Type |
Conference Article |
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Year |
2013 |
Publication |
20th International Conference On Neural Information Processing |
Abbreviated Journal |
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Volume |
8228 |
Issue |
III |
Pages |
368-375 |
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Abstract |
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Address |
Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-42050-4 |
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Conference |
ICONIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ DFD2013 |
Serial |
2439 |
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Author |
Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo |
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Title |
Multispectral Stereo Image Correspondence |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
8048 |
Issue |
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Pages |
217-224 |
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Abstract |
This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach. |
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Address |
York; uk; August 2013 |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-40245-6 |
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Conference |
CAIP |
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Notes |
ADAS; 600.055 |
Approved |
no |
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Call Number |
Admin @ si @ PST2013 |
Serial |
2561 |
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Permanent link to this record |
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Author |
Gioacchino Vino; Angel Sappa |
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Title |
Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach |
Type |
Conference Article |
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Year |
2013 |
Publication |
10th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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Volume |
7950 |
Issue |
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Pages |
354-363 |
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Abstract |
This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach. |
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Address |
Póvoa de Varzim; Portugal; June 2013 |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-39093-7 |
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Conference |
ICIAR |
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Notes |
ADAS; 600.055 |
Approved |
no |
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Call Number |
Admin @ si @ ViS2013 |
Serial |
2562 |
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Permanent link to this record |
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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 |
Abbreviated Journal |
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Volume |
221 |
Issue |
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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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Address |
Coimbatore, India |
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Publisher |
Springer India |
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LNCS |
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Edition |
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ISSN |
1876-1100 |
ISBN |
978-81-322-0996-6 |
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Conference |
ICSIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ PST2012 |
Serial |
2157 |
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Permanent link to this record |
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Author |
Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen |
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Title |
Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging |
Type |
Conference Article |
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Year |
2014 |
Publication |
17th International Conference on Medical Image Computing and Computer Assisted Intervention |
Abbreviated Journal |
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Volume |
8896 |
Issue |
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Pages |
231-238 |
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Keywords |
Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging |
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Abstract |
Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across dierent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three dierent OF methods, including HARP. |
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Address |
Boston; USA; September 2014 |
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Publisher |
Springer International Publishing |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-319-14677-5 |
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Conference |
STACOM |
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Notes |
IAM; ADAS; 600.060; 601.145; 600.076; 600.075 |
Approved |
no |
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Call Number |
Admin @ si @ MKF2014 |
Serial |
2495 |
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Permanent link to this record |
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Author |
Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos |
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Title |
A predictive model for human activity recognition by observing actions and context |
Type |
Conference Article |
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Year |
2015 |
Publication |
Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 |
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Volume |
9386 |
Issue |
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Pages |
323-333 |
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Abstract |
This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. |
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Catania; Italy; October 2015 |
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Publisher |
Springer International Publishing |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-319-25902-4 |
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ACIVS |
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Notes |
ADAS; 600.076 |
Approved |
no |
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Call Number |
Admin @ si @ RFS2015 |
Serial |
2661 |
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Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Visible-Thermal Fusion based Monocular Visual Odometry |
Type |
Conference Article |
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Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
Abbreviated Journal |
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Volume |
417 |
Issue |
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Pages |
517-528 |
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Keywords |
Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. |
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Abstract |
The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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Address |
Lisboa; Portugal; November 2015 |
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Publisher |
Springer International Publishing |
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ISSN |
2194-5357 |
ISBN |
978-3-319-27145-3 |
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Conference |
ROBOT |
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Notes |
ADAS; 600.076; 600.086 |
Approved |
no |
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Call Number |
Admin @ si @ PAD2015 |
Serial |
2663 |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
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Title |
Deep semantic pyramids for human attributes and action recognition |
Type |
Conference Article |
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Year |
2015 |
Publication |
Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 |
Abbreviated Journal |
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Volume |
9127 |
Issue |
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Pages |
341-353 |
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Keywords |
Action recognition; Human attributes; Semantic pyramids |
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Abstract |
Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature. |
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Address |
Denmark; Copenhagen; June 2015 |
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Publisher |
Springer International Publishing |
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ISSN |
0302-9743 |
ISBN |
978-3-319-19664-0 |
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Conference |
SCIA |
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Notes |
LAMP; 600.068; 600.079;ADAS |
Approved |
no |
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Call Number |
Admin @ si @ KRW2015b |
Serial |
2672 |
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Permanent link to this record |