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| Author | Arjan Gijsenij; Theo Gevers; Joost Van de Weijer | ||||
| Title | Generalized Gamut Mapping using Image Derivative Structures for Color Constancy | Type | Journal Article | ||
| Year | 2010 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 86 | Issue | 2-3 | Pages | 127-139 |
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| Abstract | The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut | ||||
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| Publisher | Kluwer Academic Publishers Hingham, MA, USA | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ISE | Approved | no | ||
| Call Number | CAT @ cat @ GGW2010 | Serial | 1274 | ||
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| Author | Jose Manuel Alvarez; Theo Gevers; Antonio Lopez | ||||
| Title | Learning photometric invariance for object detection | Type | Journal Article | ||
| Year | 2010 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 90 | Issue | 1 | Pages | 45-61 |
| Keywords | road detection | ||||
| Abstract | Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time. Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods |
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ADAS;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ AGL2010c | Serial | 1451 | ||
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| Author | Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders | ||||
| Title | Selective Search for Object Recognition | Type | Journal Article | ||
| Year | 2013 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 104 | Issue | 2 | Pages | 154-171 |
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| Abstract | This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). | ||||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ USG2013 | Serial | 2362 | ||
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| Author | Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez | ||||
| 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 |
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| 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. |
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ISE;CIC;ADAS | Approved | no | ||
| Call Number | Admin @ si @ BGW2012 | Serial | 1718 | ||
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| Author | R. Valenti; N. Sebe; Theo Gevers | ||||
| Title | What are you looking at? Improving Visual gaze Estimation by Saliency | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 98 | Issue | 3 | Pages | 324-334 |
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| Abstract | Impact factor 2010: 5.15
Impact factor 2011/12?: 5.36 In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems. |
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ VSG2012 | Serial | 1848 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell | ||||
| Title | Modulating Shape Features by Color Attention for Object Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 98 | Issue | 1 | Pages | 49-64 |
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| Abstract | Bag-of-words based image representation is a successful approach for object recognition. Generally, the subsequent stages of the process: feature detection,feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ KWV2012 | Serial | 1864 | ||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg | ||||
| 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 |
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| 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. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | CIC; ADAS; 600.057; 600.048 | Approved | no | ||
| Call Number | Admin @ si @ KRW2013 | Serial | 2285 | ||
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| Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
| 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. |
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ XRV2016 | Serial | 2669 | ||
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| Author | Antonio Hernandez; Sergio Escalera; Stan Sclaroff | ||||
| Title | Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 118 | Issue | 1 | Pages | 49–64 |
| Keywords | Contextual rescoring; Poselets; Human pose estimation | ||||
| Abstract | In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| ISSN | 0920-5691 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ HES2016 | Serial | 2719 | ||
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| Author | Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera | ||||
| Title | Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis | Type | Journal Article | ||
| Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 122 | Issue | 2 | Pages | 212–227 |
| Keywords | Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation | ||||
| Abstract | This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures. | ||||
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| Notes | HuPBA;MILAB; 303.100 | Approved | no | ||
| Call Number | Admin @ si @ PEB2017 | Serial | 2765 | ||
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| Author | Cristina Palmero; Albert Clapes; Chris Bahnsen; Andreas Møgelmose; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | Multi-modal RGB-Depth-Thermal Human Body Segmentation | Type | Journal Article | ||
| Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 118 | Issue | 2 | Pages | 217-239 |
| Keywords | Human body segmentation; RGB ; Depth Thermal | ||||
| Abstract | This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ PCB2016 | Serial | 2767 | ||
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| Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
| Title | Subspace Procrustes Analysis | Type | Journal Article | ||
| Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 121 | Issue | 3 | Pages | 327–343 |
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| Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
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| Notes | MILAB; HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ PTI2017 | Serial | 2841 | ||
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| Author | Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon | ||||
| Title | Looking at People Special Issue | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 126 | Issue | 2-4 | Pages | 141-143 |
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| Notes | HUPBA; ISE; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ EGJ2018 | Serial | 3093 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Feedback and Surround Modulated Boundary Detection | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 126 | Issue | 12 | Pages | 1367–1380 |
| Keywords | Boundary detection; Surround modulation; Biologically-inspired vision | ||||
| Abstract | Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. | ||||
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| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number | Admin @ si @ AkP2018b | Serial | 2991 | ||
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| Author | Adrien Gaidon; Antonio Lopez; Florent Perronnin | ||||
| Title | The Reasonable Effectiveness of Synthetic Visual Data | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 126 | Issue | 9 | Pages | 899–901 |
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GLP2018 | Serial | 3180 | ||
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