| Records |
| Author |
Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras |
| Title |
Information Theoretic Rotationwise Robust Binary Descriptor Learning |
Type |
Conference Article |
| Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
368-378 |
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| Abstract |
In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. |
| Address |
Mérida; Mexico; November 2016 |
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S+SSPR |
| Notes |
DAG; ADAS; 600.097; 600.086 |
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no |
| Call Number |
Admin @ si @ RLL2016 |
Serial |
2871 |
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| Author |
Anjan Dutta; Umapada Pal; Josep Llados |
| Title |
Compact Correlated Features for Writer Independent Signature Verification |
Type |
Conference Article |
| Year |
2016 |
Publication |
23rd International Conference on Pattern Recognition |
Abbreviated Journal |
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Pages |
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This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300. |
| Address |
Cancun; Mexico; December 2016 |
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Conference |
ICPR |
| Notes |
DAG; 600.097 |
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no |
| Call Number |
Admin @ si @ DPL2016 |
Serial |
2875 |
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| Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
| Title |
Local Binary Pattern for Word Spotting in Handwritten Historical Document |
Type |
Conference Article |
| Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
574-583 |
| Keywords |
Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data |
| Abstract |
Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. |
| Address |
Merida; Mexico; December 2016 |
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LNCS |
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S+SSPR |
| Notes |
DAG; 600.097; 602.006; 603.053 |
Approved |
no |
| Call Number |
Admin @ si @ DNL2016 |
Serial |
2876 |
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| Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
| Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
Type |
Conference Article |
| Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
Abbreviated Journal |
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| Volume |
10029 |
Issue |
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Pages |
543-552 |
| Keywords |
Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
| Abstract |
The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
| Address |
Merida; Mexico; December 2016 |
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| Publisher |
Springer International Publishing |
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LNCS |
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ISBN |
978-3-319-49054-0 |
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Conference |
S+SSPR |
| Notes |
DAG; 600.097; 602.006 |
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no |
| Call Number |
Admin @ si @ TSF2016 |
Serial |
2877 |
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| Author |
Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau |
| Title |
Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain |
Type |
Conference Article |
| Year |
2016 |
Publication |
7th International Workshop on Statistical Atlases & Computational Modelling of the Heart |
Abbreviated Journal |
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| Volume |
10124 |
Issue |
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Pages |
163-171 |
| Keywords |
Laplacian; Constrained maps; Parameterization; Basal ring |
| Abstract |
Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries. |
| Address |
Athens; October 2016 |
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LNCS |
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STACOM |
| Notes |
IAM; |
Approved |
no |
| Call Number |
Admin @ si @ GGM2016 |
Serial |
2884 |
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| Author |
Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell |
| Title |
Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches |
Type |
Conference Article |
| Year |
2016 |
Publication |
19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops |
Abbreviated Journal |
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| Volume |
9401 |
Issue |
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Pages |
62-70 |
| Keywords |
Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy |
| Abstract |
Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface. |
| Address |
Quebec; Canada; September 2016 |
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LNCS |
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MICCAIW |
| Notes |
IAM; MV; 600.060; 600.075 |
Approved |
no |
| Call Number |
Admin @ si @ SGB2016 |
Serial |
2885 |
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| Author |
Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
| Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
Type |
Conference Article |
| Year |
2016 |
Publication |
21st Ibero American Congress on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
467-475 |
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| Abstract |
This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
| Address |
Lima; Perú; November 2016 |
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LNCS |
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CIARP |
| Notes |
ADAS; 600.086 |
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no |
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Admin @ si @ CRS2016 |
Serial |
2913 |
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| Author |
H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena |
| Title |
Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior |
Type |
Journal Article |
| Year |
2016 |
Publication |
Patter Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
76 |
Issue |
1 |
Pages |
76-82 |
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| Notes |
IAM; 600.060 |
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no |
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Admin @ si @ MFV2017b |
Serial |
2941 |
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| Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
| Title |
Banknote counterfeit detection through background texture printing analysis |
Type |
Conference Article |
| Year |
2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
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This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
| Address |
Rumania; May 2016 |
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DAS |
| Notes |
DAG; 600.061; 601.269; 600.097 |
Approved |
no |
| Call Number |
Admin @ si @ BRL2016 |
Serial |
2950 |
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| Author |
Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer |
| Title |
Development of general‐purpose projection‐based augmented reality systems |
Type |
Journal |
| Year |
2016 |
Publication |
IADIs international journal on computer science and information systems |
Abbreviated Journal |
IADIs |
| Volume |
11 |
Issue |
2 |
Pages |
1-18 |
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| Abstract |
Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups |
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| Notes |
DAG; 600.084 |
Approved |
no |
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Admin @ si @ SCK2016 |
Serial |
2890 |
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| Author |
Ivet Rafegas; Maria Vanrell |
| Title |
Color spaces emerging from deep convolutional networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
24th Color and Imaging Conference |
Abbreviated Journal |
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Issue |
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Pages |
225-230 |
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| Abstract |
Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
| Address |
San Diego; USA; November 2016 |
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CIC |
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CIC |
Approved |
no |
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Admin @ si @ RaV2016a |
Serial |
2894 |
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| Author |
Ivet Rafegas; Maria Vanrell |
| Title |
Colour Visual Coding in trained Deep Neural Networks |
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Abstract |
| Year |
2016 |
Publication |
European Conference on Visual Perception |
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| Address |
Barcelona; Spain; August 2016 |
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ECVP |
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CIC |
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no |
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Admin @ si @ RaV2016b |
Serial |
2895 |
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| Author |
Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol |
| Title |
La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals |
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Journal |
| Year |
2016 |
Publication |
Lligall, Revista Catalana d'Arxivística |
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39 |
Issue |
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Pages |
20-46 |
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| Notes |
DAG; 600.097 |
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no |
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Admin @ si @ FLR2016 |
Serial |
2897 |
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| Author |
Arash Akbarinia; C. Alejandro Parraga |
| Title |
Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception |
Type |
Conference Article |
| Year |
2016 |
Publication |
European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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NEUROBIT |
Approved |
no |
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Admin @ si @ AkP2016b |
Serial |
2900 |
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| Author |
C. Alejandro Parraga; Arash Akbarinia |
| Title |
Colour Constancy as a Product of Dynamic Centre-Surround Adaptation |
Type |
Conference Article |
| Year |
2016 |
Publication |
16th Annual meeting in Vision Sciences Society |
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| Volume |
16 |
Issue |
12 |
Pages |
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| Abstract |
Colour constancy refers to the human visual system's ability to preserve the perceived colour of objects despite changes in the illumination. Its exact mechanisms are unknown, although a number of systems ranging from retinal to cortical and memory are thought to play important roles. The strength of the perceptual shift necessary to preserve these colours is usually estimated by the vectorial distances from an ideal match (or canonical illuminant). In this work we explore how much of the colour constancy phenomenon could be explained by well-known physiological properties of V1 and V2 neurons whose receptive fields (RF) vary according to the contrast and orientation of surround stimuli. Indeed, it has been shown that both RF size and the normalization occurring between centre and surround in cortical neurons depend on the local properties of surrounding stimuli. Our stating point is the construction of a computational model which includes this dynamical centre-surround adaptation by means of two overlapping asymmetric Gaussian kernels whose variances are adjusted to the contrast of surrounding pixels to represent the changes in RF size of cortical neurons and the weights of their respective contributions are altered according to differences in centre-surround contrast and orientation. The final output of the model is obtained after convolving an image with this dynamical operator and an estimation of the illuminant is obtained by considering the contrast of the far surround. We tested our algorithm on naturalistic stimuli from several benchmark datasets. Our results show that although our model does not require any training, its performance against the state-of-the-art is highly competitive, even outperforming learning-based algorithms in some cases. Indeed, these results are very encouraging if we consider that they were obtained with the same parameters for all datasets (i.e. just like the human visual system operates). |
| Address |
Florida; USA; May 2016 |
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VSS |
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NEUROBIT |
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no |
| Call Number |
Admin @ si @ PaA2016b |
Serial |
2901 |
| Permanent link to this record |
