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Katerine Diaz, Francesc J. Ferri, & W. Diaz. (2015). Incremental Generalized Discriminative Common Vectors for Image Classification. TNNLS - IEEE Transactions on Neural Networks and Learning Systems, 26(8), 1761–1775.
Abstract: Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.
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Katerine Diaz, Francesc J. Ferri, & Aura Hernandez-Sabate. (2018). An overview of incremental feature extraction methods based on linear subspaces. KBS - Knowledge-Based Systems, 145, 219–235.
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.
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Katerine Diaz, & Francesc J. Ferri. (2013). Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes.
Abstract: Los métodos basados en subespacios son una herramienta muy utilizada en aplicaciones de visión por computador. Aquí se presentan y validan algunos algoritmos que hemos propuesto en este campo de investigación. El primer algoritmo está relacionado con una extensión del método de vectores comunes discriminantes con kernel, que reinterpreta el espacio nulo de la matriz de dispersión intra-clase del conjunto de entrenamiento para obtener las características discriminantes. Dentro de los métodos basados en subespacios existen diferentes tipos de entrenamiento. Uno de los más populares, pero no por ello uno de los más eficientes, es el aprendizaje por lotes. En este tipo de aprendizaje, todas las muestras del conjunto de entrenamiento tienen que estar disponibles desde el inicio. De este modo, cuando nuevas muestras se ponen a disposición del algoritmo, el sistema tiene que ser reentrenado de nuevo desde cero. Una alternativa a este tipo de entrenamiento es el aprendizaje incremental. Aquí se proponen diferentes algoritmos incrementales del método de vectores comunes discriminantes.
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Katerine Diaz, Aura Hernandez-Sabate, & Antonio Lopez. (2016). A reduced feature set for driver head pose estimation. ASOC - Applied Soft Computing, 45, 98–107.
Abstract: Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.
Keywords: Head pose estimation; driving performance evaluation; subspace based methods; linear regression
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Karla Lizbeth Caballero, Joel Barajas, & Petia Radeva. (2007). Using Reconstructed IVUS Images for Coronary Plaque Classification. In Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE (2167–2170).
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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, Neus Salvatella, & Petia Radeva. (2006). In-Vivo IVUS Tissue Classification: A Comparison Between RF Signal Analysis and Reconstructed Images. In 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 137–146.
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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, J. Mauri, & Petia Radeva. (2006). Using Radio Frequency Reconstructed IVUS Images in Tissue Classification.
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Karla Lizbeth Caballero, Joel Barajas, & Oriol Pujol. (2007). Reconstructing IVUS Images for an Accurate Tissue Classification. In Proceedings of the Second International Conference on Computer Vision Theory and Applications (Vol. Special Sessions, 113–119).
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Karla Lizbeth Caballero. (2007). Coronary Plaque Classification using Intravascular Ultrasound Images and Radio Frequency Signals.
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Karim Lekadir, Alfiia Galimzianova, Angels Betriu, Maria del Mar Vila, Laura Igual, Daniel L. Rubin, et al. (2017). A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. J-BHI - IEEE Journal Biomedical and Health Informatics, 21(1), 48–55.
Abstract: Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.
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Karel Paleček, David Geronimo, & Frederic Lerasle. (2012). Pre-attention cues for person detection. In Cognitive Behavioural Systems, COST 2102 International Training School (pp. 225–235). LNCS. Springer Berlin Heidelberg.
Abstract: Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.
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Kamal Nasrollahi, Sergio Escalera, P. Rasti, Gholamreza Anbarjafari, Xavier Baro, Hugo Jair Escalante, et al. (2015). Deep Learning based Super-Resolution for Improved Action Recognition. In 5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 (pp. 67–72).
Abstract: Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.
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Kaida Xiao, Sophie Wuerger, Chenyang Fu, & Dimosthenis Karatzas. (2011). Unique Hue Data for Colour Appearance Models. Part i: Loci of Unique Hues and Hue Uniformity. CRA - Color Research & Application, 36(5), 316–323.
Abstract: Psychophysical experiments were conducted to assess unique hues on a CRT display for a large sample of colour-normal observers (n 1⁄4 185). These data were then used to evaluate the most commonly used colour appear- ance model, CIECAM02, by transforming the CIEXYZ tris- timulus values of the unique hues to the CIECAM02 colour appearance attributes, lightness, chroma and hue angle. We report two findings: (1) the hue angles derived from our unique hue data are inconsistent with the commonly used Natural Color System hues that are incorporated in the CIECAM02 model. We argue that our predicted unique hue angles (derived from our large dataset) provide a more reliable standard for colour management applications when the precise specification of these salient colours is im- portant. (2) We test hue uniformity for CIECAM02 in all four unique hues and show significant disagreements for all hues, except for unique red which seems to be invariant under lightness changes. Our dataset is useful to improve the CIECAM02 model as it provides reliable data for benchmarking.
Keywords: unique hues; colour appearance models; CIECAM02; hue uniformity
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Kaida Xiao, Chenyang Fu, Dimosthenis Karatzas, & Sophie Wuerger. (2011). Visual Gamma Correction for LCD Displays. DIS - Displays, 32(1), 17–23.
Abstract: An improved method for visual gamma correction is developed for LCD displays to increase the accuracy of digital colour reproduction. Rather than utilising a photometric measurement device, we use observ- ers’ visual luminance judgements for gamma correction. Eight half tone patterns were designed to gen- erate relative luminances from 1/9 to 8/9 for each colour channel. A psychophysical experiment was conducted on an LCD display to find the digital signals corresponding to each relative luminance by visually matching the half-tone background to a uniform colour patch. Both inter- and intra-observer vari- ability for the eight luminance matches in each channel were assessed and the luminance matches proved to be consistent across observers (DE00 < 3.5) and repeatable (DE00 < 2.2). Based on the individual observer judgements, the display opto-electronic transfer function (OETF) was estimated by using either a 3rd order polynomial regression or linear interpolation for each colour channel. The performance of the proposed method is evaluated by predicting the CIE tristimulus values of a set of coloured patches (using the observer-based OETFs) and comparing them to the expected CIE tristimulus values (using the OETF obtained from spectro-radiometric luminance measurements). The resulting colour differences range from 2 to 4.6 DE00. We conclude that this observer-based method of visual gamma correction is useful to estimate the OETF for LCD displays. Its major advantage is that no particular functional relationship between digital inputs and luminance outputs has to be assumed.
Keywords: Display calibration; Psychophysics ; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration
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Kaida Xiao, Chenyang Fu, D.Mylonas, Dimosthenis Karatzas, & S. Wuerger. (2013). Unique Hue Data for Colour Appearance Models. Part ii: Chromatic Adaptation Transform. CRA - Color Research & Application, 38(1), 22–29.
Abstract: Unique hue settings of 185 observers under three room-lighting conditions were used to evaluate the accuracy of full and mixed chromatic adaptation transform models of CIECAM02 in terms of unique hue reproduction. Perceptual hue shifts in CIECAM02 were evaluated for both models with no clear difference using the current Commission Internationale de l'Éclairage (CIE) recommendation for mixed chromatic adaptation ratio. Using our large dataset of unique hue data as a benchmark, an optimised parameter is proposed for chromatic adaptation under mixed illumination conditions that produces more accurate results in unique hue reproduction. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013
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