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Differential representations for mesh processing

Differential representations for mesh processing

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18th brazilian symposium on computer graphics and image processing (SIBGRAPI 2005)

作者： Sorkine, Olga Tel Aviv Univ Sch Comp Sci IL-69978 Tel Aviv Israel

Surface representation and processing is one of the key topics in computer graphics and geometric modeling, since it greatly affects the range of possible applications. In this paper we will present recent advances in geometry processing that are related to the Laplacian processing framework and differential representations. this framework is based on linear operators defined oil polygonal meshes, and furnishes a variety of processing applications, such as shape approximation and compact representation, mesh editing, watermarking and morphing. the core of the framework is the definition of differential coordinates and new bases for efficient mesh geometry representation, based on the mesh Laplacian operator.

关键词： discrete Laplacian surface representation detail preservation geometry compression mesh editing

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Diagnosing Leukemia in Blood Smear images Using an Ensemble of Classifiers and Pre-trained Convolutional Neural Networks 30

Diagnosing Leukemia in Blood Smear Images Using an Ensemble ...

引用

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

作者： Vogado, Luis H. S. Veras, Rodrigo de M. S. Andrade, Alan R. de Araujo, Flavio H. D. e Silva, Romuere R. V. Aires, Kelson R. T. Univ Fed Piaui Dept Comp Sci Teresina Brazil

ISBN: (纸本)9781538622193

Leukemia is a worldwide disease. In this paper we demonstrate that it is possible to build an automated, efficient and rapid leukemia diagnosis system. We demonstrate that it is possible to improve the precision of current techniques from the literature using the description power of well-known Convolutional Neural Networks (CNNs). We extract features from a blood smear image using pre-trained CNNs in order to obtain an unique image description. Many feature selection techniques were evaluated and we chose PCA to select the features that are in the final descriptor. To classify the images on healthy and pathological we created an ensemble of classifiers with three individual classification algorithms (Support Vector Machine, Multilayer Perceptron and Random Forest). In the tests we obtained an accuracy rate of 100%. Besides the high accuracy rate, the tests showed that our approach requires less processing time than the methods analyzed in this paper, considering the fact that our approach does not use segmentation to obtain specific cell regions from the blood smear image.

关键词： computer-aided diagnosis convolutional neural networks leukemia transfer learning

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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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Analyzing the Effects of Dimensionality Reduction for Unsupervised Domain Adaptation 34

Analyzing the Effects of Dimensionality Reduction for Unsupe...

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

作者： Lopes Junior, Renato Sergio Schwartz, William Robson Fed Univ Minas Gerais UFMG Dept Comp Sci Smart Sense Lab Belo Horizonte MG Brazil

ISBN: (纸本)9781665423540

Deep neural networks are extensively used for solving a variety of computer vision problems. However, in order for these networks to obtain good results, a large amount of data is necessary for training. In image classification, this training data consists of images and labels that indicate the class portrayed by each image. Obtaining this large labeled dataset is very time and resource consuming. therefore, domain adaptation methods allow different, but semantic-related, datasets that are already labeled to be used during training, thus eliminating the labeling cost. In this work, the effects of embedding dimensionality reduction in a state-of-the-art domain adaptation method are analyzed. Furthermore, we experiment with a different approach that use the available data from all domains to compute the confidence of pseudo-labeled samples. We show through experiments in commonly used datasets that, in fact, the proposed modifications led to better results in the target domain in some scenarios.

关键词： Training Dimensionality reduction graphics Deep learning computer vision Costs Training data

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Simultaneously Estimation of Super-Resolution images and Depth Maps from Low Resolution Sensors 28

Simultaneously Estimation of Super-Resolution Images and Dep...

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Mesquita, Daniel B. Nascimento, Erickson R. Campos, Mario F. M. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781467379625

the emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is necessary. With the purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, in this work we propose a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining low-resolution intensity images and depth maps in an optimization process, our methodology creates new images and depth maps of higher resolution and, at the same time, minimizes issues related with the absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images and depth maps acquired with low resolution, commercially available sensors.

关键词： Super-resolution convex optimization RGB-D data 3D reconstruction computer vision

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Neighbor Embedding Projection and Rank-based Manifold Learning for image Retrieval 37

Neighbor Embedding Projection and Rank-based Manifold Learni...

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37th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Kawai, Vinicius Atsushi Sato Leticio, Gustavo Rosseto Valem, Lucas Pascotti Pedronette, Daniel Carlos Guimardes State Univ Sao Paulo UNESP Dept Stat Appl Math & Comp Rio Claro Brazil

ISBN: (纸本)9798350376043;9798350376036

Despite the impressive advances in image understanding approaches, defining similarity among images remains a challenging task, crucial for many applications such as classification and retrieval. Mainly supported by Convolution Neural Networks (CNNs) and Transformer-based models, image representation techniques are the main reason for the advances. On the other hand, comparisons are mostly computed based on traditional pairwise measures, such as the Euclidean distance, while contextual similarity approaches can lead to effective results in defining similarity between points in high-dimensional spaces. this paper introduces a novel approach to contextual similarity by combining two techniques: neighbor embedding projection methods and rank-based manifold learning. High-dimensional features are projected in a 2D space used for efficiently ranking computation. Subsequently, manifold learning methods are exploited for a re-ranking step. An experimental evaluation conducted on different datasets and visual features indicates that the proposed approach leads to significant gains in comparison to the original feature representations and the neighbor embedding method in isolation.

关键词： Contrastive Learning

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Unsupervised Dual-Layer Aggregation for Feature Fusion on image Retrieval Tasks 37

Unsupervised Dual-Layer Aggregation for Feature Fusion on Im...

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37th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Moreno, Ademir, Jr. Guimaraes Pedronette, Daniel Carlos Sao Paulo State Univ UNESP Dept Stat Appl Math & Comp Rio Claro Brazil

ISBN: (纸本)9798350376043;9798350376036

the revolutionary advances in image representation have led to impressive progress in many image understanding-related tasks, primarily supported by Convolutional Neural Networks (CNN) and, more recently, by Transformer models. Despite such advances, assessing the similarity among images for retrieval in unsupervised scenarios remains a challenging task, mostly grounded on traditional pairwise measures, such as the Euclidean distance. the scenario is even more challenging when different visual features are available, requiring the selection and fusion of features without any label information. In this paper, we propose an Unsupervised Dual-Layer Aggregation (UDLA) method, based on contextual similarity approaches for selecting and fusing CNN and Transformer-based visual features trained through transfer learning. In the first layer, the selected features are fused in pairs focused on precision. A sub-set of pairs is selected for a second layer aggregation focused on recall. An experimental evaluation conducted in different public datasets showed the effectiveness of the proposed approach, which achieved results significantly superior to the best-isolated feature and also superior to a recent fusion approach considered as baseline.

关键词： Convolutional neural networks

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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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Self-Supervised image Re-Ranking based on Hypergraphs and Graph Convolutional Networks 37

Self-Supervised Image Re-Ranking based on Hypergraphs and Gr...

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37th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Lopes, Leonardo Tadeu Valem, Lucas Pascotti Pedronette, Daniel Carlos Guimardes Sao Paulo State Univ UNESP Dept Stat Appl Math & Comp Rio Claro Brazil

ISBN: (纸本)9798350376043;9798350376036

image retrieval approaches typically involve two fundamental stages: visual content representation and similarity measurement. Traditional methods rely on pairwise dissimilarity metrics, such as Euclidean distance, which overlook the global structure of datasets. Aiming to address this limitation, various unsupervised post-processing approaches have been developed to redefine similarity measures. Diffusion processes and rank-based methods compute a more effective similarity by considering the relationships among images and the overall dataset structure. However, neither approach is capable of defining novel image representations. this paper aims to overcome this limitation by proposing a novel self-supervised image re-ranking method. the proposed method exploits a hypergraph model, clustering strategies, and Graph Convolutional Networks (GCNs). Initially, an unsupervised rank-based manifold learning method computes global similarities to define small and reliable clusters, which are used as soft labels for training a semi-supervised GCN model. this GCN undergoes a two-stage training process: an initial classification-focused stage followed by a retrieval-focused stage. the final GCN embeddings are employed for retrieval tasks using the cosine similarity. An experimental evaluation conducted on four public datasets with three different visual features indicates that the proposed approach outperforms traditional and recent rank-based methods.

关键词： Self-supervised learning

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Randomrewardmechanisms (Loot Boxes): Behavioral Economics As A Regulatory Solution In brazilian Law 17

Randomrewardmechanisms (Loot Boxes): Behavioral Economics As...

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17th International Conference on Interfaces and Human computer Interaction 2023, IHCI 2023, the 17th International Conference on computer graphics, Visualization, computer Vision and image processing 2023, CGVCVIP 2023 and 16th International Conference on Game and Entertainment Technologies 2023, GET 2023

作者： Fantini, Laiane Maris Caetano PUC Minas. Centro DTIBR MG Belo Horizonte Brazil

ISBN: (纸本)9789898704498

the paper seeks to reflect on ways to create an effective regulation by understanding the aspects of random reward mechanisms in video games, also known as loot boxes. Players acquire digital assets of a game through microtransactions, and pays with in-game currency or real money. Among these digital assets, several games also offer loot boxes, which are systems of "surprise boxes" with a certain price but whose reward is unknown to the player (and is only revealed when the box is purchased and opened). A simple way of selling a digital asset has serious implications for players, mainly due to the similarity with gambling in making the reward relegated to chance. Also, adults and children players are encouraged to exhibit addictive behaviors, spending time and money without any control. brazilian law does not have enough apparatus to protect consumers from these practices and banning loot boxes from the market does not seem to be the solution, as similar mechanisms could arise even worse. this way of marketing digital assets hides the real transaction costs and makes consumers make decisions based on heuristics, and that's why the Behavioral Economic theory can offer better solutions to protect players. © 2023 Proceedings of the International Conferences on Interfaces and Human computer Interaction 2023, IHCI 2023;computer graphics, Visualization, computer Vision and image processing 2023, CGVCVIP 2023;and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

关键词： Human computer interaction

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