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A Max-Tree Simplification Proposal and Applications for the Interactive Max-Tree Visualization Tool

A Max-Tree Simplification Proposal and Applications for the ...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Luís A. Tavares Roberto M. Souza Leticia Rittner Rubens C. Machado Roberto A. Lotufo School of Electrical and Computer Engineering - FEEC University of Campinas Brazil Federal Institute of Education Science and Technology of South of Minas Gerais - IFSULDEMINAS Pouso Alegre Brazil Universidade Estadual de Campinas Campinas SP BR Center for Information Technology Renato Archer - CTI Campinas Brazil

ISBN: (纸本)9781509035694;9781509035687

The max-tree is a data structure that represents hierarchically all connected components of an image. It is a powerful structure to perform image processing and analysis tasks. The interactive max-tree visualization tool was the first proposal of an interactive graphical representation for the max-tree, a tool that can be used to navigate, visualize and manipulate the max-tree nodes. In this paper, we propose a more meaningful simplification procedure for the max-tree, we extend the interactive max-tree visualization tool to work with 3D images and we propose the visualization of attributes, a new feature using the interactive max-tree that provides insight for the development of automatic methods.

关键词： image segmentation Data structures Three-dimensional displays Proposals Data visualization Navigation Sorting

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An Evaluation of the Volume on Surface (VoS) Approach

An Evaluation of the Volume on Surface (VoS) Approach

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Rafael Silva Santos Danilo Medeiros Eler Rogério Eduardo Garcia Ronaldo Celso Messias Correia Fac. de Cienc. e Tecnol. Univ. Estadual Paulista Prudente SP Brazil Fac. de Cienc. e Tecnol. Universidade Estadual Paulista Julio de Mesquita Filho Sao Paulo SP BR

ISBN: (纸本)9781509035694;9781509035687

Hybrid volume rendering algorithms combine techniques of different categories of volume visualization. Volume on Surface (VoS) is a hybrid volume rendering technique that maps volume information to isosurfaces, intending to accelerate the volume rendering process. In this paper, we introduce an improved version of the VoS technique. Furthermore, we conduct an evaluation of CUDA-based versions of both the original and the improved approach, comparing them with the conventional Ray Casting algorithm. In some results, our technique is more than eight times faster than Ray Casting and thirty times than original version. In addition to the main discussion, we investigate how a surface simplification influences the performance and the quality of the images rendered by our approach.

关键词： Rendering (computer graphics) image color analysis Casting graphics processing units Kernel Isosurfaces Transfer functions

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The Integration of GPU-based and Heterogeneous Devices Using HLA

The Integration of GPU-based and Heterogeneous Devices Using...

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brazilian symposium on Computing System Engineering (SBESC)

作者： Halamo G. R. Andrade Daniel Morais Thiago W. B. Silva Tiago P. Nascimento Alisson V. Brito Universidade Federal da Paraiba (UFPB) Joao Pessoa Brasil

ISBN: (纸本)9781509026548

The use of distributed components may offer many advantages in the most types of applications. It includes, for example, the easy use of disperse resources in a network, allowing a potential gain on the processing power and data loading. But, the major challenge is interoperability and communication management. In this aspect, the High Level Architecture (HLA) establishes specific policies for data exchanging and time management of communication in the integration of the simulation components. Thus, this paper aims to explain and analyse the feasibility to integrate devices based on heterogeneous architectures using distributed and parallel computing, multi-core CPU and GPU, with OpenCL technology. For that, the use of the IEEE 1516 standard, called High Level Architecture, is proposed targeting this integration and allowing the use of all computing power from available devices in a distributed system. In this study experiments also were done to demonstrate the approach of running a simple parallel image processing application under the conceived architecture.

关键词： computer architecture graphics processing units Parallel processing Computational modeling Standards Hardware Proposals

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Fast Saliency Detection Using Sparse Random Color Samples and Joint Upsampling

Fast Saliency Detection Using Sparse Random Color Samples an...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Maiko Min Ian Lie Gustavo Benvenutti Borba Hugo Vieira Neto Humberto Remigio Gamba Grad. Program in Electr. & Comput. Eng. Fed. Univ. of Technol.-Parana Curitiba Brazil Graduate Program in Biomedical Engineering Graduate Program in Electrical and Computer Engineering

ISBN: (纸本)9781509035694;9781509035687

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime.

关键词： image color analysis Runtime Visualization image edge detection Smoothing methods Computational modeling Noise measurement

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Identifying Style of 3D Shapes using Deep Metric Learning 16

Identifying Style of 3D Shapes using Deep Metric Learning

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Eurographics symposium on Geometry processing (SGP) / symposium on Solid and Physical Modeling (SPM) / Shape Modeling International (SMI) Conference

作者： Lim, Isaak Gehre, Anne Kobbelt, Leif Rhein Westfal TH Aachen Visual Comp Inst Aachen Germany

We present a method that expands on previous work in learning human perceived style similarity across objects with different structures and functionalities. Unlike previous approaches that tackle this problem with the help of hand-crafted geometric descriptors, we make use of recent advances in metric learning with neural networks (deep metric learning). This allows us to train the similarity metric on a shape collection directly, since any low- or high-level features needed to discriminate between different styles are identified by the neural network automatically. Furthermore, we avoid the issue of finding and comparing sub-elements of the shapes. We represent the shapes as rendered images and show how image tuples can be selected, generated and used efficiently for deep metric learning. We also tackle the problem of training our neural networks on relatively small datasets and show that we achieve style classification accuracy competitive with the state of the art. Finally, to reduce annotation effort we propose a method to incorporate heterogeneous data sources by adding annotated photos found online in order to expand or supplant parts of our training data.

关键词： Categories and Subject Descriptors (according to ACM CCS) I 3 5 [computer graphics]: Computational Geometry and Object Modeling style similarity deep metric learning

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Oriented image Foresting Transform Segmentation: Connectivity Constraints with Adjustable Width

Oriented Image Foresting Transform Segmentation: Connectivit...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Lucy A. C. Mansilla Paulo A. V. Miranda Department of Computer Science University of São Paulo (USP) São Paulo SP Brazil

ISBN: (纸本)9781509035694;9781509035687

In this work, we extend a novel seed-based segmentation algorithm, which provides global optimum solutions according to a graph-cut measure, subject to high-level boundary constraints: The simultaneously handling of boundary polarity and connectivity constraints. The proposed method incorporates the connectivity constraint in the Oriented image Foresting Transform (OIFT), ensuring the generation of connected objects, but such that the connection between its internal seeds is guaranteed to have a user-controllable minimum width. In other frameworks, such as the min-cut/max-flow algorithm, the connectivity constraint is known to lead to NP-hard problems. In contrast, our method conserves the low complexity of the OIFT algorithm. In the experiments, we show improved results for the segmentation of thin and elongated objects, for the same amount of user interaction. Our dataset of natural images with true segmentation is publicly available to the community.

关键词： image segmentation Transforms Approximation algorithms Optimized production technology Topology Complexity theory Labeling

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A Web-Based Tool for Semi-Automated Segmentation of Histopathological images Using Nonlinear Color Classifiers

A Web-Based Tool for Semi-Automated Segmentation of Histopat...

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Annual IEEE symposium on computer-Based Medical Systems

作者： Marcelo Dornbusch Lopes Antonio C. Sobieranski Aldo Von Wangenheim Eros Comunello INCoD - National Brazilian Institute for Digital Convergence / LAPIX - Image Processing and Computer Graphics Lab - Federal University of Santa Catarina Brazil Postgraduate Program in Applied Computing University of Itajai Valley - UNIVALI INCoD (Nat. Brazilian Inst. for Digital Convergence) LAPIX (Image Process. & Comput. Graphics Lab.) Universidade Federal de Santa Catarina Florianopolis SC BR

ISBN: (纸本)9781467390378

Histopathological staining is a technique widely used to highlight biological states or pharmacological activities in uman tissue. A quantitative analysis of the resulting images can produce biomarkers for diseases or even other specific conditions, thus providing valuable information for diagnosis and prognosis. Since biomarkers require measurements to be made in an objective and consistent way, software systems are employed to provide this quantitative analysis. For measurements to be reproducible, the same methods must be available across different laboratories. In this paper, we present a tool that allows users to perform quantitative analyses over the web, thus providing an efficient environment not only for individual cases to be evaluated, but also for users to share a common ground when making measurements. The classification method used by the tool to segment stained pixels is performed by a similarity function based on the polynomial version of the Mahalanobis distance, which is nonlinear and provides very robust classification for m-dimensional feature spaces. Furthermore, the similarity function can be generalized in the tool, so that images can be classified by reusing parameters of previous cases. The results of our web-based approach were compared with established ground-truth data sets, producing sensitivity, specificity and fitness values of 97.09%, 98.70%, and 97.90%.

关键词： image color analysis image segmentation Biomarkers Sensitivity Inspection Servers Statistical analysis

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A Meta-Learning Approach for Recommendation of image Segmentation Algorithms

A Meta-Learning Approach for Recommendation of Image Segment...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Gabriel F. C. Campos Sylvio Barbon Rafael G. Mantovani Department of Computer Science Londrina State University (UEL) Londrina PR Brazil Sciences Institute of Mathematics and Computers (ICMC) University of São Paulo (USP) Sao Carlos SP Brazil

ISBN: (纸本)9781509035694;9781509035687

There are many algorithms for image segmentation, but there is no optimal algorithm for all kind of image applications. To recommend an adequate algorithm for segmentation is a challenging task that requires knowledge about the problem and algorithms. Meta-learning has recently emerged from machine learning research field to solve the algorithm selection problem. This paper applies meta-learning to recommend segmentation algorithms based on meta-knowledge. We performed experiments in four different meta-databases representing various real world problems, recommending when three different segmentation techniques are adequate or not. A set of 44 features based on color, frequency domain, histogram, texture, contrast and image quality were extracted from images, obtaining enough discriminative power for the recommending task in different segmentation scenarios. Results show that Random Forest meta-models were able to recommend segmentation algorithms with high predictive performance.

关键词： image segmentation image color analysis Prediction algorithms Histograms Machine learning algorithms Standards Wounds

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Stochastic Hierarchical Watershed Cut Based on Disturbed Topographical Surface

Stochastic Hierarchical Watershed Cut Based on Disturbed Top...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Carlos Alberto F. Pimentel Filho Arnaldo de Albuquerque Araújo Jean Cousty Silvio Jamil F. Guimarães Laurent Najman Pontificia Universidade Catolica de Minas Gerais Belo Horizonte MG BR Audio-Visual Information Proc. Lab. (VIPLAB)Computer Science Department – ICEI – PUC Minas UPEMLV ESIEE Paris ENPC CNRS Laboratoire d'Informatique Gaspard-Monge UMR 8049 Noisy-le-Grand France

In this article we present a hierarchical stochastic image segmentation approach. This approach is based on a framework of edge-weighted graph for minimum spanning forest hierarchy. image regions, that are represented by trees in a forest, can be merged according to a certain rule in order to create a single tree that represents segments hierarchically. In this article, we propose to add a uniform random noise into the edge-weighted graph and then we build the hierarchy with several realizations of independent segmentations. At the end, we combine all the hierarchical segmentations into a single one. As we show in this article, adding noise into the edge weights improves the segmentation precision of larger image regions and for F-Measure of objects and parts.

关键词： image segmentation Surface topography image edge detection Stochastic processes Surface morphology Vegetation Surface treatment

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Interactive Medical image Segmentation by Statistical Seed Models

Interactive Medical Image Segmentation by Statistical Seed M...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Thiago Vallin Spina Samuel Botter Martins Alexandre Xavier Falcão Campinas University of Campinas SP Brazil

Interactive 3D object segmentation is an important and challenging activity in medical imaging, although it is tedious and error-prone to be done. Automatic segmentation methods aim to replace the user altogether, but require user interaction to produce training data sets of segmented masks and to make error corrections. We propose a complete framework for interactive medical image segmentation, which reduces user effort by automatically providing an initial segmentation result. We develop a Statistical Seed Model (SSM) to this end, that improves from seed sets selected by robot users when reconstructing masks of previously segmented images. The SSM outputs a seed set that may be used to automatically delineate a new test image. The seeds provide both an implicit object shape constraint and a flexible way of interactively correcting segmentation. We demonstrate that our framework decreases the amount of user interaction by a factor of three, when segmenting MR-images of the cerebellum.

关键词： image segmentation Training Biomedical imaging Robot kinematics image reconstruction Shape

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