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Marçal Rusiñol; J. Chazalon; Katerine Diaz |
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Title |
Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
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Journal Article |
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2018 |
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Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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77 |
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11 |
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13773-13798 |
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Augmented reality; Document image matching; Educational applications |
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This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
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DAG; ADAS; 600.084; 600.121; 600.118; 600.129 |
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no |
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Admin @ si @ RCD2018 |
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2996 |
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Author |
Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon |
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Title |
Incremental model learning for spectroscopy-based food analysis |
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Journal Article |
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2017 |
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Chemometrics and Intelligent Laboratory Systems |
Abbreviated Journal |
CILS |
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167 |
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123-131 |
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Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy |
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In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. |
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ADAS; 600.118 |
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Admin @ si @ DGK2017 |
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3002 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
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Decremental generalized discriminative common vectors applied to images classification |
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Journal Article |
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2017 |
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Knowledge-Based Systems |
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KBS |
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131 |
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46-57 |
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Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
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In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
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ADAS; 600.118; 600.121 |
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Admin @ si @ DMH2017a |
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3003 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri |
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Title |
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction |
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Journal Article |
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Year |
2018 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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60 |
Issue |
4 |
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512-524 |
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This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 |
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Admin @ si @ DMH2018a |
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3062 |
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Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
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Title |
Learning to measure for preshipment garment sizing |
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Journal Article |
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Year |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
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130 |
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327-339 |
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Apparel; Computer vision; Structured prediction; Regression |
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Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
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ADAS; MSIAU; 600.122; 600.118 |
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Admin @ si @ SLR2018 |
Serial |
3128 |
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