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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2012). Personalization and User Verification in Wearable Systems using Biometric Walking Patterns. PUC - Personal and Ubiquitous Computing, 16(5), 563–580.
Abstract: In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.
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Xavier Boix, Josep M. Gonfaus, Joost Van de Weijer, Andrew Bagdanov, Joan Serrat, & Jordi Gonzalez. (2012). Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation. IJCV - International Journal of Computer Vision, 96(1), 83–102.
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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Bhaskar Chakraborty, Michael Holte, Thomas B. Moeslund, & Jordi Gonzalez. (2012). Selective Spatio-Temporal Interest Points. CVIU - Computer Vision and Image Understanding, 116(3), 396–410.
Abstract: Recent progress in the field of human action recognition points towards the use of Spatio-TemporalInterestPoints (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques.
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Noha Elfiky, Fahad Shahbaz Khan, Joost Van de Weijer, & Jordi Gonzalez. (2012). Discriminative Compact Pyramids for Object and Scene Recognition. PR - Pattern Recognition, 45(4), 1627–1636.
Abstract: Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.
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Susana Alvarez, Anna Salvatella, Maria Vanrell, & Xavier Otazu. (2012). Low-dimensional and Comprehensive Color Texture Description. CVIU - Computer Vision and Image Understanding, 116(I), 54–67.
Abstract: Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap
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Alicia Fornes, Josep Llados, Gemma Sanchez, & Horst Bunke. (2012). Writer Identification in Old Handwritten Music Scores. In Copnstantin Papaodysseus (Ed.), Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology (pp. 27–63). IGI-Global.
Abstract: The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.
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Carolina Malagelada, F.De Lorio, Santiago Segui, S. Mendez, Michal Drozdzal, Jordi Vitria, et al. (2012). Functional gut disorders or disordered gut function? Small bowel dysmotility evidenced by an original technique. NEUMOT - Neurogastroenterology & Motility, 24(3), 223–230.
Abstract: JCR Impact Factor 2010: 3.349
Background This study aimed to determine the proportion of cases with abnormal intestinal motility among patients with functional bowel disorders. To this end, we applied an original method, previously developed in our laboratory, for analysis of endoluminal images obtained by capsule endoscopy. This novel technology is based on computer vision and machine learning techniques.
Methods The endoscopic capsule (Pillcam SB1; Given Imaging, Yokneam, Israel) was administered to 80 patients with functional bowel disorders and 70 healthy subjects. Endoluminal image analysis was performed with a computer vision program developed for the evaluation of contractile events (luminal occlusions and radial wrinkles), non-contractile patterns (open tunnel and smooth wall patterns), type of content (secretions, chyme) and motion of wall and contents. Normality range and discrimination of abnormal cases were established by a machine learning technique. Specifically, an iterative classifier (one-class support vector machine) was applied in a random population of 50 healthy subjects as a training set and the remaining subjects (20 healthy subjects and 80 patients) as a test set.
Key Results The classifier identified as abnormal 29% of patients with functional diseases of the bowel (23 of 80), and as normal 97% of healthy subjects (68 of 70) (P < 0.05 by chi-squared test). Patients identified as abnormal clustered in two groups, which exhibited either a hyper- or a hypodynamic motility pattern. The motor behavior was unrelated to clinical features.
Conclusions & Inferences With appropriate methodology, abnormal intestinal motility can be demonstrated in a significant proportion of patients with functional bowel disorders, implying a pathologic disturbance of gut physiology.
Keywords: capsule endoscopy;computer vision analysis;machine learning technique;small bowel motility
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Michal Drozdzal, Petia Radeva, Santiago Segui, Laura Igual, Carolina Malagelada, Fernando Azpiroz, et al. (2012). System and Method for Improving a Discriminative Model.
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Antonio Hernandez, Nadezhda Zlateva, Alexander Marinov, Miguel Reyes, Petia Radeva, Dimo Dimov, et al. (2012). Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps. In 25th IEEE Conference on Computer Vision and Pattern Recognition (pp. 726–732). IEEE Xplore.
Abstract: We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.
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Naveen Onkarappa, Sujay M. Veerabhadrappa, & Angel Sappa. (2012). Optical Flow in Onboard Applications: A Study on the Relationship Between Accuracy and Scene Texture. In 4th International Conference on Signal and Image Processing (Vol. 221, pp. 257–267).
Abstract: Optical flow has got a major role in making advanced driver assistance systems (ADAS) a reality. ADAS applications are expected to perform efficiently in all kinds of environments, those are highly probable, that one can drive the vehicle in different kinds of roads, times and seasons. In this work, we study the relationship of optical flow with different roads, that is by analyzing optical flow accuracy on different road textures. Texture measures such as TeX , TeX and TeX are evaluated for this purpose. Further, the relation of regularization weight to the flow accuracy in the presence of different textures is also analyzed. Additionally, we present a framework to generate synthetic sequences of different textures in ADAS scenarios with ground-truth optical flow.
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Mohammad Rouhani, & Angel Sappa. (2012). Implicit Polynomial Representation through a Fast Fitting Error Estimation. TIP - IEEE Transactions on Image Processing, 21(4), 2089–2098.
Abstract: Impact Factor
This paper presents a simple distance estimation for implicit polynomial fitting. It is computed as the height of a simplex built between the point and the surface (i.e., a triangle in 2-D or a tetrahedron in 3-D), which is used as a coarse but reliable estimation of the orthogonal distance. The proposed distance can be described as a function of the coefficients of the implicit polynomial. Moreover, it is differentiable and has a smooth behavior . Hence, it can be used in any gradient-based optimization. In this paper, its use in a Levenberg-Marquardt framework is shown, which is particularly devoted for nonlinear least squares problems. The proposed estimation is a generalization of the gradient-based distance estimation, which is widely used in the literature. Experimental results, both in 2-D and 3-D data sets, are provided. Comparisons with state-of-the-art techniques are presented, showing the advantages of the proposed approach.
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J. Stöttinger, A. Hanbury, N. Sebe, & Theo Gevers. (2012). Spars Color Interest Points for Image Retrieval and Object Categorization. TIP - IEEE Transactions on Image Processing, 21(5), 2681–2692.
Abstract: Impact factor 2010: 2.92
IF 2011/2012?: 3.32
Interest point detection is an important research area in the field of image processing and computer vision. In particular, image retrieval and object categorization heavily rely on interest point detection from which local image descriptors are computed for image matching. In general, interest points are based on luminance, and color has been largely ignored. However, the use of color increases the distinctiveness of interest points. The use of color may therefore provide selective search reducing the total number of interest points used for image matching. This paper proposes color interest points for sparse image representation. To reduce the sensitivity to varying imaging conditions, light-invariant interest points are introduced. Color statistics based on occurrence probability lead to color boosted points, which are obtained through saliency-based feature selection. Furthermore, a principal component analysis-based scale selection method is proposed, which gives a robust scale estimation per interest point. From large-scale experiments, it is shown that the proposed color interest point detector has higher repeatability than a luminance-based one. Furthermore, in the context of image retrieval, a reduced and predictable number of color features show an increase in performance compared to state-of-the-art interest points. Finally, in the context of object recognition, for the Pascal VOC 2007 challenge, our method gives comparable performance to state-of-the-art methods using only a small fraction of the features, reducing the computing time considerably.
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R. Valenti, N. Sebe, & Theo Gevers. (2012). What are you looking at? Improving Visual gaze Estimation by Saliency. IJCV - International Journal of Computer Vision, 98(3), 324–334.
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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R. Valenti, & Theo Gevers. (2012). Accurate Eye Center Location through Invariant Isocentric Patterns. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 34(9), 1785–1798.
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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Arjan Gijsenij, Theo Gevers, & Joost Van de Weijer. (2012). Improving Color Constancy by Photometric Edge Weighting. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 34(5), 918–929.
Abstract: : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy.
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