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30th SIBGRAPI Conference on graphics, Patterns and images Tutorials (SIBGRAPI-T)

作者： Sampaio, Rafael de A. Jackowski, Marcel P. Univ Sao Paulo Inst Matemat & Estat Sao Paulo Brazil

ISBN: (纸本)9781538622193

Real-time Magnetic Resonance Imaging (rtMRI) leads to the dynamic observation of hidden processes of articulation in an unprecedented way. the non-invasive image acquisition nature of MRI combined with enhanced anatomical discrimination made rtMRI the reference in capturing vocal tract configurations during speech production. However, this development also unveiled challenges, such as the shape extraction and analysis of the vocal tract contours automatically. this work describes automated techniques for the segmentation of the vocal tract and identification of articulatory structures using rtMRI. the identification of these structures is vital for modeling articulatory synthesis. the methodology is based on level set methods to outline the vocal tract shape. Changes in the vocal tract shape and its structures were investigated for different corpora in order to bind the expression of phonemes and the behavior of the anatomical shapes. these shapes were labeled from basal form invariants, whose final evolution yielded the classification of regions of interest. the methodology resulting from this work may be employed in accent-suppression systems, speech production for laryngectomized patients, and therapetic techniques for children suffering from speech apraxia.

关键词： Shape image segmentation Speech Level set Production Tongue

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Orthogonal Hankel Subspaces for Applications in Gesture Recognition 30

Orthogonal Hankel Subspaces for Applications in Gesture Reco...

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30th SIBGRAPI Conference on graphics, Patterns and images Tutorials (SIBGRAPI-T)

作者： Gatto, Bernardo B. dos Santos, Eulanda M. Waldir, S. S. Junior Univ Fed Amazonas Manaus Amazonas Brazil

ISBN: (纸本)9781538622193

Gesture recognition is an important research area in video analysis and computer vision. Gesture recognition systems include several advantages, such as the interaction with machines without needing additional external devices. Moreover, gesture recognition involves many challenges, as the distribution of a specific gesture largely varies depending on viewpoints due to its multiple joint structures. In this paper, We present a novel framework for gesture recognition. the novelty of the proposed framework lies in three aspects: first, we propose a new gesture representation based on a compact trajectory matrix, which preserves spatial and temporal information. We understand that not all images of a gesture video are useful for the recognition task, therefore it is necessary to create a method where it is possible to detect the images that do not contribute to the recognition task, decreasing the computational cost of the overall framework. Second, we represent this compact trajectory matrix as a subspace, achieving discriminative information, as the trajectory matrices obtained from different gestures generate dissimilar clusters in a low dimension space. Finally, we introduce an automatic procedure to infer the optimal dimension of each gesture subspace. We show that our compact representation presents practical and theoretical advantages, such as compact representation and low computational requirements. We demonstrate the advantages of the proposed method by experimentation employing Cambridge gesture and Human-computer Interaction datasets.

关键词： Gesture recognition Feature extraction Hidden Markov models image recognition Eigenvalues and eigenfunctions Covariance matrices Complexity theory

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Exploiting Convolutional Neural Networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images 30

Exploiting Convolutional Neural Networks and preprocessing t...

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30th SIBGRAPI Conference on graphics, Patterns and images Tutorials (SIBGRAPI-T)

作者： Rodrigues, Larissa Ferreira Naldi, Murilo Coelho Mari, Joao Fernando UFV Inst Ciencias Exatas & Tecnol Caixa Postal 22 BR-38810000 Rio Paranaiba MG Brazil

ISBN: (纸本)9781538622193

Autoimmune diseases are the third cause of mortality in the world. the identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2) is a conventional method to support the diagnosis of such diseases. In the present work, three popular Convolutional Neural Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and GoogLeNet. We also assess the impact of six different pre-processing strategies on the performance of these CNNs. Additionally, data augmentation based on the rotation of the training set images after the pre-processing strategies was evaluated. Our work is the first to consider AlexNet and GoogLeNet models for the proposed analysis and classification of HEp-2 cells images, besides the LeNet-5. Experimental results allow to conclude that neither pre-processing strategies were essential to improve accuracy values of the CNNs. However, when data augmentation is considered, contrast enhancement followed by data centralization is significant in order to achieve good results. Additionally, our results were compared with results from other state-of-art papers. Our best results were achieved by GoogLeNet architecture trained with images with no preprocessing and no data augmentation, resulting in 98.17% of accuracy, which outperforms the results presented in other works in literature.

关键词： Convolutional neural networks HEp-2 cells staining patterns classification LeNet-5 AlexNet GoogLeNet pre-processing data augmentation

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An Approach to Iris Contact Lens Detection based on Deep image Representations 28

An Approach to Iris Contact Lens Detection based on Deep Ima...

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Silva, Pedro Luz, Eduardo Baeta, Rafael Menotti, David Pedrini, Helio Falcao, Alexandre Xavier Fed Univ Ouro Preto UFOP Dept Comp BR-35400000 Ouro Preto MG Brazil Univ Campinas UNICAMP Inst Comp BR-13083852 Campinas SP Brazil

ISBN: (纸本)9781467379625

Spoofing detection is a challenging task in biometric systems, when differentiating illegitimate users from genuine ones. Although iris scans are far more inclusive than fingerprints, and also more precise for person authentication, iris recognition systems are vulnerable to spoofing via textured cosmetic contact lenses. Iris spoofing detection is also referred to as liveness detection (binary classification of fake and real images). In this work, we focus on a three-class detection problem: images with textured (colored) contact lenses, soft contact lenses, and no lenses. Our approach uses a convolutional network to build a deep image representation and an additional fully-connected single layer with softmax regression for classification. Experiments are conducted in comparison with a state-of-the-art approach (SOTA) on two public iris image databases for contact lens detection: 2013 Notre Dame and IIIT-Delhi. Our approach can achieve a 30% performance gain over SOTA on the former database (from 80% to 86%) and comparable results on the latter. Since IIIT-Delhi does not provide segmented iris images and, differently from SOTA, our approach does not segment the iris yet, we conclude that these are very promising results.

关键词： Iris Biometrics Contact Lens Detection Deep Learning Convolutional Networks

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image restoration using digital inpainting and noise removal

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image AND VISION COMPUTING 2007年第1期25卷 61-69页

作者： Barcelos, Celia A. Zorzo Batista, Marcos Aurelio Univ Fed Goias Dept Comp BR-75704020 Catalao Go Brazil Univ Fed Uberlandia Fac Math BR-38400902 Uberlandia MG Brazil

Inpainting and denoising are two important tasks in the field of image processing with broad applications in image and vision analysis. In this paper. we present a new approach for image restoration. Our method simultaneously fills in missing, corrupted, or undesirable information while it removes noise. the denoising is performed by the smoothing equation working inside and outside of the inpainting domain but in completely different ways. Inside the inpainting domain, the smoothing is carried out by the Mean Curvature Flow, while the smoothing of the outside of the inpainting domain is carried out in a way as to encourage smoothing within a region and discourage smoothing across boundaries. Besides smoothing, the approach here presented permits the transportation of available information from the outside towards the inside of the inpainting domain. this combination permits the simultaneous use of filling-in and differentiated smoothing of different regions of an image. the experimental results show the effective performance of the combination of these two procedures in restoring scratched photos, disocclusion (or removal of entire objects from the image) in vision analysis and text removal from images. (c) 2006 Elsevier B.V. All rights reserved.

关键词： inpaint image processing noise removal edge detection diffusion equation transport equation

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Stochastic exploration and active learning for image retrieval

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image AND VISION COMPUTING 2007年第1期25卷 14-23页

作者： Cord, Matthieu Gosselin, Philippe H. Philipp-Foliguet, Sylvie Univ Cergy Pontoise ETIS CNRS UMR 8051 F-95014 Cergy Pontoise France

this paper deals with content-based image retrieval. When the user is looking for large categories, statistical classification techniques are efficient as soon as the training set is large enough. We introduce a two-step-exploration, classification-interactive strategy designed for category retrieval. the first step aims at getting a useful initial training set for the classification step. A stochastic image selection process is used instead of the usual strategy based on a similarity score ranking. this process is dedicated to explore the database in order to collect examples as various as possible of the searched category. the second step aims at providing the best classification between relevant and irrelevant images. Based on SVM, the classification applies an active learning strategy through user interaction. A quality assessment is carried out on the ANN and COREL databases in order to compare and validate our approach. (c) 2006 Elsevier B.V. All rights reserved.

关键词： image processing content-based image retrieval exploration of image database classification active learning

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A Comparative Analysis of Deep Learning Techniques for Sub-tropical Crop Types Recognition from Multitemporal Optical/SAR image Sequences 30

A Comparative Analysis of Deep Learning Techniques for Sub-t...

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30th SIBGRAPI Conference on graphics, Patterns and images Tutorials (SIBGRAPI-T)

作者： Castro, Jose Bermudez Feitosa, Raul Queiroz La Rosa, Laura Cue Diaz, Pedro Achanccaray Sanches, Ieda Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil Univ Estado Rio De Janeiro Rio De Janeiro Brazil Natl Inst Space Res Sao Jose Dos Campos Brazil

ISBN: (纸本)9781538622193

Remote Sensing (RS) data have been increasingly applied to assess agricultural yield, production and crop condition. In tropical areas, crop dynamics are complex due to multiple agricultural practices such as irrigation, non-tillage, crop rotation and multiple harvest per year. Spatial and temporal information can improve the performance in land-cover and crop type classification tasks. In this context Deep Learning (DL) have emerged as a powerful state-of-the-art technique in the RS community. this work presents a comparative analysis of traditional and DL (supervised and unsupervised) approaches for crop classification on sequences of multitemporal optical and SAR images. three different approaches are compared: the image stacking approach, which is used as baseline, and two DL based approaches using Autoencoders (AEs) and Convolutional Neural Networks (CNNs). Experiments were carried out in two datasets from two different municipalities in Brazil, Ipua in Sao Paulo state and Campo Verde in Mato Grosso state. It is shown that CNN and AE outperformed the traditional approach based on image stacking in terms of Overall Accuracy and Class Accuracy.

关键词： Crop Recognition Multitemporal images Autoencoders Convolutional Neural Networks

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Variable resolution 4-K meshes 13

Variable resolution 4-K meshes

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13th brazilian symposium on computer graphics and image processing, SIBGRAPI 2000

作者： Velho, Luiz Gomes, Jonas IMPA-Instituto de Matemática Pura e Aplicada Brazil

ISBN: (纸本)0769508782

In this paper we introduce variable resoultion 4-k meshes, a powerful structure for the representation of geometric objects at multiple levels of detail. It combines most properties of other related descriptions with several advantages, such as more flexibility and greater expressive power. the main unique feature of the 4-k mesh structure lies in its variable resolution capability, which is crucial for adaptive computation. We also give an overview of the different methods for constructing the hierarchical 4-k mesh representation, as well as the basic algorithms necessary to incorporate it in modeling and graphics applications. © 2000 IEEE.

关键词： computer graphics

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A 2D Deep Boltzmann Machine for Robust and Fast Vehicle Classification 30

A 2D Deep Boltzmann Machine for Robust and Fast Vehicle Clas...

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30th SIBGRAPI Conference on graphics, Patterns and images Tutorials (SIBGRAPI-T)

作者： Silva Santos, Daniel Felipe de Souza, Gustavo Botelho Marana, Aparecido Nilceu Sao Paulo State Univ UNESP Sao Paulo Brazil UFSCar Fed Univ Sao Carlos Sao Carlos SP Brazil

ISBN: (纸本)9781538622193

the visual and automatic classification of vehicles plays an important role in the Transport Area. Besides of security issues, the monitoring of the type of traffic in streets and highways, as well the traffic dynamics over time, allows the optimization of use and of resources related to such public infrastructure. In this work we propose a novel method, called 2D-DBM, for robust and efficient automatic vehicle classification through color images based on a DBM (Deep Boltzmann Machine) combined with bilinear projections. While the DBM training allows a robust initialization of discriminative MLP (Multilayer Perceptron) neural network parameters, the bilinear projection technique can scale down the MLP dimensions, obtaining efficiency while preserving accuracy. the proposed method was assessed on the BIT-Vehicle database, a challenging dataset consisting of frontal images of vehicles collected in a real traffic environment, and compared with a CNN (Convolutional Neural Network) and a traditional DBM (without bilinear projection). the obtained results show that, while keeping the accuracy, the new method significantly reduced the network size and the processing time.

关键词： Vehicle Classification Traffic Control image Analysis Deep Boltzmann Machines Bilinear Projection

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Representation Learning for image Retrieval through 3D CNN and Manifold Ranking 34

Representation Learning for Image Retrieval through 3D CNN a...

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34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： de Almeida, Lucas Barbosa Pereira-Ferrero, Vanessa Helena Valem, Lucas Pascotti Almeida, Jurandy Guimaraes Pedronette, Daniel Carlos Sao Paulo State Univ UNESP Dept Stat Appl Math & Comp DEMAC Rio Claro Brazil Fed Univ Sao Paulo UNIFESP Inst Sci & Technol Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781665423540

Despite of the substantial success of Convolutional Neural Networks (CNNs) on many recognition and representation tasks, such models are very reliant on huge amount of data to allow effective training. In order to improve the generalization ability of CNNs, several approaches have been proposed, including variations of data augmentation strategies. With the goal of achieving more effective retrieval results on unsupervised learning scenarios, we propose a representation learning approach which exploits a rank-based formulation to build a more comprehensive data representation. the proposed model uses 2D and 3D CNNs trained by transfer learning and fuse both representations through a rank-based formulation based on manifold learning algorithms. Our approach was evaluated on an unsupervised image retrieval scenario applied to action recognition datasets. the experimental results indicated that significant effectiveness gains can be obtained on various datasets, reaching +56.93% of relative gains on MAP scores.

关键词： Representation learning Training Manifolds Solid modeling three-dimensional displays image retrieval Transfer learning

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