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Author Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton edit  doi
openurl 
  Title Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis Type Journal Article
  Year 2016 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 28 Issue Pages 1489 - 1491  
  Keywords  
  Abstract The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others.  
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  Area Expedition Conference  
  Notes HuPBA; ISE;MV; Approved no  
  Call Number Admin @ si @ Serial 2851  
Permanent link to this record
 
 
Author R. Valenti; Theo Gevers edit  doi
openurl 
  Title Accurate Eye Center Location through Invariant Isocentric Patterns Type Journal Article
  Year 2012 Publication IEEE Transaction on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 34 Issue 9 Pages 1785-1798  
  Keywords  
  Abstract Impact factor 2010: 5.308
Impact factor 2011/12?: 5.96
Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery.		  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ VaG 2012a Serial 1849  
Permanent link to this record
 
 
Author Arjan Gijsenij; Theo Gevers edit  doi
openurl 
  Title Color Constancy Using Natural Image Statistics and Scene Semantics Type Journal Article
  Year 2011 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 33 Issue 4 Pages 687-698  
  Keywords  
  Abstract Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms.  
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  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ GiG2011 Serial 1724  
Permanent link to this record
 
 
Author K.E.A. van de Sande; Theo Gevers; C.G.M. Snoek edit  doi
openurl 
  Title Evaluating Color Descriptors for Object and Scene Recognition Type Journal Article
  Year 2010 Publication IEEE Transaction on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 32 Issue 9 Pages 1582 - 1596  
  Keywords  
  Abstract Impact factor: 5.308
Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.		  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ SGS2010 Serial 1846  
Permanent link to this record
 
 
Author Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez edit   pdf
doi  openurl
  Title Pay attention to the activations: a modular attention mechanism for fine-grained image recognition Type Journal Article
  Year 2020 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM  
  Volume 22 Issue 2 Pages 502-514  
  Keywords  
  Abstract Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%.  
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  Notes ISE; 600.119; 600.098 Approved no  
  Call Number Admin @ si @ RVC2020a Serial 3417  
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