|
Records |
Links |
|
Author |
Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
|
|
Title |
Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization |
Type |
Journal Article |
|
Year |
2021 |
Publication |
International Journal of Imaging Systems and Technology |
Abbreviated Journal |
IMA |
|
|
Volume |
31 |
Issue |
4 |
Pages |
2170-2188 |
|
|
Keywords |
|
|
|
Abstract |
Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGR2021a |
Serial |
3630 |
|
Permanent link to this record |
|
|
|
|
Author |
Anjan Dutta; Jaume Gibert; Josep Llados; Horst Bunke; Umapada Pal |
|
|
Title |
Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents |
Type |
Conference Article |
|
Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1663-1666 |
|
|
Keywords |
|
|
|
Abstract |
This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging. |
|
|
Address |
Tsukuba, Japan |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
DAG |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGL2012 |
Serial |
2125 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon |
|
|
Title |
Incremental model learning for spectroscopy-based food analysis |
Type |
Journal Article |
|
Year |
2017 |
Publication |
Chemometrics and Intelligent Laboratory Systems |
Abbreviated Journal |
CILS |
|
|
Volume |
167 |
Issue |
|
Pages |
123-131 |
|
|
Keywords |
Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy |
|
|
Abstract |
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGK2017 |
Serial |
3002 |
|
Permanent link to this record |
|
|
|
|
Author |
Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva |
|
|
Title |
Introduction to the special issue: Egocentric Vision and Lifelogging |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
|
|
Volume |
55 |
Issue |
|
Pages |
352-353 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGC2018 |
Serial |
3187 |
|
Permanent link to this record |
|
|
|
|
Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell |
|
|
Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
Type |
Journal Article |
|
Year |
2017 |
Publication |
European Respiratory Journal |
Abbreviated Journal |
ERJ |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGC2017b |
Serial |
3632 |
|
Permanent link to this record |
|
|
|
|
Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso |
|
|
Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
Type |
Journal Article |
|
Year |
2017 |
Publication |
Journal of Thoracic Oncology |
Abbreviated Journal |
JTO |
|
|
Volume |
12 |
Issue |
1S |
Pages |
S596-S597 |
|
|
Keywords |
Thorax CT; diagnosis; Peripheral Pulmonary Nodule |
|
|
Abstract |
A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP). |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; 600.096; 600.075; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGC2017a |
Serial |
2883 |
|
Permanent link to this record |
|
|
|
|
Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell |
|
|
Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Chest Journal |
Abbreviated Journal |
CHEST |
|
|
Volume |
150 |
Issue |
4 |
Pages |
1003A |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; 600.096; 600.075 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGC2016 |
Serial |
3099 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
|
|
Title |
An overview of incremental feature extraction methods based on linear subspaces |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
|
|
Volume |
145 |
Issue |
|
Pages |
219-235 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0950-7051 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DFH2018 |
Serial |
3090 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
|
|
Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
Type |
Journal Article |
|
Year |
2015 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
|
|
Volume |
26 |
Issue |
8 |
Pages |
1761 - 1775 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2162-237X |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DFD2015 |
Serial |
2547 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
|
|
Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
Type |
Conference Article |
|
Year |
2013 |
Publication |
20th International Conference On Neural Information Processing |
Abbreviated Journal |
|
|
|
Volume |
8228 |
Issue |
III |
Pages |
368-375 |
|
|
Keywords |
|
|
|
Abstract |
An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
|
|
Address |
Daegu; Korea; November 2013 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-642-42050-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICONIP |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ DFD2013 |
Serial |
2439 |
|
Permanent link to this record |
|
|
|
|
Author |
Sounak Dey |
|
|
Title |
Mapping between Images and Conceptual Spaces: Sketch-based Image Retrieval |
Type |
Book Whole |
|
Year |
2020 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
This thesis presents several contributions to the literature of sketch based image retrieval (SBIR). In SBIR the first challenge we face is how to map two different domains to common space for effective retrieval of images, while tackling the different levels of abstraction people use to express their notion of objects around while sketching. To this extent we first propose a cross-modal learning framework that maps both sketches and text into a joint embedding space invariant to depictive style, while preserving semantics. Then we have also investigated different query types possible to encompass people's dilema in sketching certain world objects. For this we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set.
Finally, we explore the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognises two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended. We also in this dissertation pave the path to the future direction of research in this domain. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
Ph.D. thesis |
|
|
Publisher |
Ediciones Graficas Rey |
Place of Publication |
|
Editor |
Josep Llados;Umapada Pal |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-84-121011-8-8 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ Dey20 |
Serial |
3480 |
|
Permanent link to this record |
|
|
|
|
Author |
Ferran Diego; G.D. Evangelidis; Joan Serrat |
|
|
Title |
Night-time outdoor surveillance by mobile cameras |
Type |
Conference Article |
|
Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
|
|
|
Volume |
2 |
Issue |
|
Pages |
365-371 |
|
|
Keywords |
|
|
|
Abstract |
This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods. |
|
|
Address |
Algarve, Portugal |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPRAM |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ DES2012 |
Serial |
2035 |
|
Permanent link to this record |
|
|
|
|
Author |
Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
|
|
Title |
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
Type |
Miscellaneous |
|
Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DDT2018 |
Serial |
3085 |
|
Permanent link to this record |
|
|
|
|
Author |
Julie Digne; Mariella Dimiccoli; Neus Sabater; Philippe Salembier |
|
|
Title |
Neighborhood Filters and the Recovery of 3D Information |
Type |
Book Chapter |
|
Year |
2015 |
Publication |
Handbook of Mathematical Methods in Imaging |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
III |
Pages |
1645-1673 |
|
|
Keywords |
|
|
|
Abstract |
Following their success in image processing (see Chapter Local Smoothing Neighborhood Filters), neighborhood filters have been extended to 3D surface processing. This adaptation is not straightforward. It has led to several variants for surfaces depending on whether the surface is defined as a mesh, or as a raw data point set. The image gray level in the bilateral similarity measure is replaced by a geometric information such as the normal or the curvature. The first section of this chapter reviews the variants of 3D mesh bilateral filters and compares them to the simplest possible isotropic filter, the mean curvature motion.In a second part, this chapter reviews applications of the bilateral filter to a data composed of a sparse depth map (or of depth cues) and of the image on which they have been computed. Such sparse depth cues can be obtained by stereovision or by psychophysical techniques. The underlying assumption to these applications is that pixels with similar intensity around a region are likely to have similar depths. Therefore, when diffusing depth information with a bilateral filter based on locality and color similarity, the discontinuities in depth are assured to be consistent with the color discontinuities, which is generally a desirable property. In the reviewed applications, this ends up with the reconstruction of a dense perceptual depth map from the joint data of an image and of depth cues. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer New York |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-1-4939-0789-2 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ DDS2015 |
Serial |
2710 |
|
Permanent link to this record |
|
|
|
|
Author |
Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal |
|
|
Title |
Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario |
Type |
Conference Article |
|
Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
31-32 |
|
|
Keywords |
|
|
|
Abstract |
One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
GREC |
|
|
Notes |
DAG; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DDL2017 |
Serial |
3057 |
|
Permanent link to this record |