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Author Xavier Boix; Josep M. Gonfaus; Joost van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
  Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 96 Issue 1 Pages 83-102  
  Keywords  
  Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
 
  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 (up) 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes CAT;ISE;CIC;ADAS Approved no  
  Call Number Admin @ si @ BGW2012 Serial 1718  
Permanent link to this record
 

 
Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg edit   pdf
doi  openurl
  Title Coloring Action Recognition in Still Images Type Journal Article
  Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 105 Issue 3 Pages 205-221  
  Keywords  
  Abstract In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes CIC; ADAS; 600.057; 600.048 Approved no  
  Call Number Admin @ si @ KRW2013 Serial 2285  
Permanent link to this record
 

 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Hierarchical Adaptive Structural SVM for Domain Adaptation Type Journal Article
  Year 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 119 Issue 2 Pages 159-178  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number Admin @ si @ XRV2016 Serial 2669  
Permanent link to this record
 

 
Author Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas Type Journal Article
  Year 2011 Publication Journal of Intelligent and Robotic Systems Abbreviated Journal JIRC  
  Volume 64 Issue 3-4 Pages 625-649  
  Keywords  
  Abstract Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0921-0296 ISBN Medium  
  Area Expedition Conference  
  Notes RV;ADAS Approved no  
  Call Number Admin @ si @ RGA2011 Serial 1728  
Permanent link to this record
 

 
Author Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot Type Journal Article
  Year 2012 Publication Journal of Intelligent and Robotic Systems Abbreviated Journal JIRC  
  Volume 68 Issue 2 Pages 185-208  
  Keywords  
  Abstract This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0921-0296 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RAV2012 Serial 2150  
Permanent link to this record
 

 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title Rank Estimation in Missing Data Matrix Problems Type Journal Article
  Year 2011 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 39 Issue 2 Pages 140-160  
  Keywords  
  Abstract A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach.  
  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 (up) 0924-9907 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ JSL2011; Serial 1710  
Permanent link to this record
 

 
Author Naveen Onkarappa; Angel Sappa edit  doi
openurl 
  Title A Novel Space Variant Image Representation Type Journal Article
  Year 2013 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 47 Issue 1-2 Pages 48-59  
  Keywords Space-variant representation; Log-polar mapping; Onboard vision applications  
  Abstract Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 0924-9907 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.055; 605.203; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013a Serial 2243  
Permanent link to this record
 

 
Author Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors Type Journal Article
  Year 2009 Publication Autonomous Robots Abbreviated Journal AR  
  Volume 27 Issue 4 Pages 373-385  
  Keywords  
  Abstract This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.
 
  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 (up) 0929-5593 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RTA2009 Serial 1245  
Permanent link to this record
 

 
Author Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate edit  url
doi  openurl
  Title An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
  Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 145 Issue Pages 219-235  
  Keywords  
  Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.  
  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 (up) 0950-7051 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DFH2018 Serial 3090  
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Author J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin edit  url
doi  openurl
  Title Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm Type Journal Article
  Year 2013 Publication Expert Systems with Applications Abbreviated Journal EXWA  
  Volume 40 Issue 17 Pages 6707-6712  
  Keywords Neural gas; Expert vision; Eye-tracking; Fixations  
  Abstract Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.  
  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 (up) 0957-4174 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ CRM2013 Serial 2438  
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