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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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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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Admin @ si @ RCD2018 |
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2996 |
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Author |
Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal |
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Title |
Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing |
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2014 |
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Multimedia Tools and Applications |
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MTAP |
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72 |
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1 |
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515-539 |
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In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well. |
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Springer US |
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1380-7501 |
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DAG; 600.077 |
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Admin @ si @ SDT2014 |
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2357 |
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Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate |
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Title |
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors |
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2019 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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61 |
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3 |
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331-351 |
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Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
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In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. |
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DAG; ADAS; 600.084; 600.118; 600.121; 600.129;IAM |
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Admin @ si @ DRR2019 |
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3172 |
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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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2018 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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60 |
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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;IAM |
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Admin @ si @ DMH2018a |
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3062 |
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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades |
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Title |
Flowchart Recognition for Non-Textual Information Retrieval in Patent Search |
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Journal Article |
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Year |
2014 |
Publication |
Information Retrieval |
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IR |
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17 |
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5-6 |
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545-562 |
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Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition |
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Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. |
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1386-4564 |
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DAG; 600.077 |
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Admin @ si @ RHR2013 |
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2342 |
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