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CAD Shape Grammar: Procedural generation for Massive CAD Model 30

CAD Shape Grammar: Procedural generation for Massive CAD Mod...

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

作者： dos Santos, Wallas H. S. Ivson, Paulo Raposo, Alberto Barbosa Pontificia Univ Catolica Rio de Janeiro Dept Informat Rio De Janeiro RJ Brazil Pontificia Univ Catolica Rio de Janeiro Inst Tecgraf Rio De Janeiro RJ Brazil

ISBN: (纸本)9781538622193

This work presents a procedural modeling technique based on shape grammars for representing and rendering massive 3D CAD models in real time. Procedural modeling is an appealing approach to quickly generate massive scenes while maintaining compact representation. Until now, procedural modeling has not been explored in the domain of large industrial projects. Traditional procedural modeling techniques generate parameterized random scenes. In order to achieve an accurate representation for pre-existing 3D CAD scenes, we propose a specialized Shape grammar. We used common geometric primitives found in real 3D CAD models to create a compact model representation. In addition, we describe an efficient rendering algorithm to draw CAD Shape Grammars in real time. We evaluated both performance and memory consumption of our proposed technique using real-world CAD models. Results indicate not only high rendering performance, but a significant reduction in the memory required to represent massive 3D CAD models.

关键词： Solid modeling Grammar Shape Three-dimensional displays Rendering (computer graphics) graphics processing units Production

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A Practical Review on Medical image Registration: From Rigid to Deep Learning Based Approaches

A Practical Review on Medical Image Registration: From Rigid...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Natan Andrade Fabio Augusto Faria Fábio Augusto Menocci Cappabianco GIBIS - Instituto de Ciência e Tecnologia Universidade Federal de São Paulo São José dos Campos SP Brazil

The large variety of medical image modalities (e.g. Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography) acquired from the same body region of a patient together with recent advances in computer architectures with faster and larger CPUs and GPUs allows a new, exciting, and unexplored world for image registration area. A precise and accurate registration of images makes possible understanding the etiology of diseases, improving surgery planning and execution, detecting otherwise unnoticed health problem signals, and mapping functionalities of the brain. The goal of this paper is to present a review of the state-of-the-art in medical image registration starting from the preprocessing steps, covering the most popular methodologies of the literature and finish with the more recent advances and perspectives from the application of Deep Learning architectures.

关键词： Magnetic resonance imaging image registration Strain Computed tomography Deformable models Medical diagnostic imaging

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A Robust Restricted Boltzmann Machine for Binary image Denoising 30

A Robust Restricted Boltzmann Machine for Binary Image Denoi...

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

作者： Pires, Rafael Levada, Alexandre L. M. Souza, Gustavo B. Pereira, Luis A. M. Santos, Daniel F. S. Papa, Joao P. Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil Univ Estadual Campinas Inst Comp Campinas SP Brazil Sao Paulo State Univ Dept Comp Bauru SP Brazil

ISBN: (纸本)9781538622193

During the image acquisition process, some level of noise is usually added to the real data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be processed in order to attenuate its noise without loosing details. Machine learning approaches have been successfully used for image denoising. Among such approaches, Restricted Boltzmann Machine (RBM) is one of the most used technique for this purpose. Here, we propose to enhance the RBM performance on image denoising by adding a posterior supervision before its final denoising step. To this purpose, we propose a simple but effective approach that performs a fine-tuning in the RBM model. Experiments on public datasets corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach with respect to some state-of-the-art image denoising approaches.

关键词： Training Noise measurement image denoising Noise reduction Biological neural networks Gaussian noise Neurons

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Multi-Object Segmentation by Hierarchical Layered Oriented image Foresting Transform 30

Multi-Object Segmentation by Hierarchical Layered Oriented I...

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

作者： Castaneda Leon, Leissi M. Miranda, Paulo A. V. Univ Sao Paulo Inst Math & Stat Sao Paulo SP Brazil

ISBN: (纸本)9781538622193

This paper introduces a new method for multi-object segmentation in images, named as Hierarchical Layered Oriented image Foresting Transform (HLOIFT). As input, we have an image, a tree of relations between image objects, with the individual high-level priors of each object coded in its nodes, and the objects' seeds. Each node of the tree defines a weighted digraph, named as layer. The layers are then integrated by the geometric interactions, such as inclusion and exclusion relations, extracted from the given tree into a unique weighted digraph, named as hierarchical layered digraph. A single energy optimization is performed in the hierarchical layered weighted digraph by Oriented image Foresting Transform (OIFT) leading to globally optimal results satisfying all the high-level priors. We evaluate our framework in the multi-object segmentation of medical and synthetic images, obtaining results comparable to the state-of-the-art methods, but with low computational complexity. Compared to multi-object segmentation by min-cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios.

关键词： image segmentation Transforms Optimization Vegetation Object segmentation Biomedical imaging

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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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Occlusion aware reduced angular candidates based light field depth estimation from an epipolar plane image 16

Occlusion aware reduced angular candidates based light field...

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16th image processing: Algorithms and Systems symposium 2018

作者： Mun, Ji-Hun Ho, Yo-Sung 123 Cheomdangwagi-ro Buk-gu Gwangju61005 Korea Republic of

Light field images are represented by capturing a densely projected rays from object to camera sensor. In this paper we propose a novel method for depth estimation from light field epipolar plane image (EPI). Contrary to the conventionally used depth estimation method such as stereo matching, the depth map is generated without high computational complexity via EPI. In order to obtain an accurate depth value from EPI, optimal angular value has to be founded for each pixels. If we consider all the angular candidate value for optimal angle value selection, that cause high computational complexity. Instead we consider all candidate value, reduce the angular candidate by analyzing the EPI patterns. In addition, to improve the quality of estimated depth map from EPI, occlusion area is handled before computing a matching cost. As a pre-processing, average and variance value are computed within specific window size to detect and replace the occlusion area. To validate the efficiency of our algorithm, we experiment with computer graphics and also dense light field data set. The experiment results show that our algorithm achieve better performance than conventionally used depth estimation methods. © 2018 Society for Imaging Science and Technology.

关键词： Computational complexity

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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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Geometric Data Analysis Based on Manifold Learning with Applications for image Understanding 30

Geometric Data Analysis Based on Manifold Learning with Appl...

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

作者： Miranda Junior, Gastao Florencio Thomaz, Carlos Eduardo Giraldi, Gilson Antonio Univ Fed Sergipe Dept Math Aracaju Brazil FEI Dept Elect Engn Sao Bernardo Do Campo Brazil Natl Lab Sci Comp LNCC Dept Math & Comput Methods Petropolis Brazil

ISBN: (纸本)9781538606193

Nowadays, pattern recognition, computer vision, signal processing and medical image analysis, require the managing of large amount of multidimensional image databases, possibly sampled from nonlinear manifolds. The complex tasks involved in the analysis of such massive data lead to a strong demand for nonlinear methods for dimensionality reduction to achieve efficient representation for information extraction. In this avenue, manifold learning has been applied to embed nonlinear image data in lower dimensional spaces for subsequent analysis. The result allows a geometric interpretation of image spaces with relevant consequences for data topology, computation of image similarity, discriminant analysis/classification tasks and, more recently, for deep learning issues. In this paper, we firstly review Riemannian manifolds that compose the mathematical background in this field. Such background offers the support to set up a data model that embeds usual linear subspace learning and discriminant analysis results in local structures built from samples drawn from some unknown distribution. Afterwards, we discuss topological issues in data preparation for manifold learning algorithms as well as the determination of manifold dimension. Then, we survey dimensionality reduction techniques with particular attention to Riemannian manifold learning. Besides, we discuss the application of concepts in discrete and polyhedral geometry for synthesis and data clustering over the recovered Riemannian manifold with emphasis in face images in the computational experiments. Next, we discuss promising perspectives of manifold learning and related topics for image analysis, classification and relationships with deep learning methods. Specifically, we discuss the application of foliation theory, discriminant analysis and kernel methods in curved spaces. Besides, we take differential geometry in manifolds as a paradigm to discuss deep generative models and metric learning algorithms.

关键词： manifold learning statistical learning Riemannian manifolds image analysis deep 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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