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Semi-regular 4-8 refinement and box spline surfaces 13

Semi-regular 4-8 refinement and box spline surfaces

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

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

ISBN: (纸本)0769508782

In this paper we introduce a new mesh refinement method for subdivision surfaces. It generates a semiregular 4-direction hierarchical structure from control meshes representing 2D manifolds of arbitrary topology. the main advantage of this structure is that it allows the extraction of conforming variable-resolution meshes based on spatially varying adaptation functions. We also present a smoothing method designed to work in conjunction with our semi-regular refinement. It produces generalized four direction Box spline surfaces of class C1. Together the refinement and smoothing operators result in a subdivision scheme that is very effective in multiresolution applications. © 2000 IEEE.

关键词： three dimensional computer graphics

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A wavelet decomposition scheme and compression method for streamline-based vector field visualizations

A wavelet decomposition scheme and compression method for st...

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14th brazilian symposium on computer graphics and image processing

作者： Marion-Poty, V Lefer, W LIL F-62228 Calais France Univ Pau & Pays Adour LIUPPA F-64013 Pau France

Flow visualization has been an active research field for several years and streamlines have proved to be an effective representation of two-dimensional steady vector fields. Online simulation of complex dynamic systems, computed on remote powerful computers are becoming more common. Efficient steering of the computational process requires the analysis of simulation results in real time. Visualization offers the possibility of at-a-glance analysis of the data but requires being able to download visualizations efficiently. Here we propose a wavelet model and compression scheme for streamline-based images of 2D steady vector fields. this method allows us to decrease the size of the data by a factor of 2.5 for a visualization quality comparable to the original image, and the compression factor increases to 10 if we accept small deformations. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： image compression

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Automated Sperm Head Morphology Classification with Deep Convolutional Neural Networks 35

Automated Sperm Head Morphology Classification with Deep Con...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Calijorne Soares, Marco Antonio Mourao Falci, Daniel Henrique Alves Farnezi, Marco Flavio Moreira Farnezi, Hana Carolina Parreiras, Fernando Silva Boechat Gomide, Joao Victor FUMEC Univ Grad Program Informat Syst & Knowledge Management Belo Horizonte Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Background and Objective: the morphological analysis of sperm cells is considered a tool in human fertility prognosis. However, this process is manual, time-consuming and dependent on professional expertise. From a computational perspective, this is a challenging problem due to the high inter-category similarity between the objects of interest and the amount of data available. In this paper, we propose a Convolutional Neural Network model to automate morphology analysis of human sperm heads. Methods: We performed K-Fold cross-validation experiments over two publicly available datasets and assessed the performance of the proposed approach using Accuracy, Precision, Recall and F1-Score. We also compared the proposed model with well-known Convolutional architectures and previous approaches on the same task. Results: Experimental evaluation showed that our approach achieved a macro-averaged F1-score of 0.95 while our best model attained an accuracy of 97.7%. the error analysis revealed a balanced classifier over different sperm head classes. Conclusions: We proved that the proposed approach outperformed the previous state-of-the-art results on this task.

关键词： Measurement Head image recognition Computational modeling Morphology computer architecture Manuals

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Procedural generation of 3D canyons 27

Procedural generation of 3D canyons

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27th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： De Carli, Daniel Michelon Pozzer, Cesar Tadeu Schetinger, Victor Bevilacqua, Fernando Univ Fed Santa Maria BR-97119900 Santa Maria RS Brazil Univ Fed Fronteira Sul Chapeco Brazil

ISBN: (纸本)9781479942602

this paper presents a non-assisted method for procedural generation of 3D canyon scenes. Our approach combines techniques of computer graphics, computer vision, image processing and graph search algorithms. Our main contribution is a generation approach that uses noise-generated heightmaps that are carefully transformed and manipulated by data clustering (through the Mean Shift algorithm) and image operations in order to mimic the observed geological features of real canyons. Several parameters are used to guide and constrain the generation of terrains, canyon features, course and shape of rivers, plain areas, soft slope regions, cliffs and plateaus.

关键词： Procedural terrain modeling procedural content clustering mean shift path-finding Perlin noise image synthesis games

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ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation 34

ChessMix: Spatial Context Data Augmentation for Remote Sensi...

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

作者： Pereira, Matheus Barros dos Santos, Jefersson Alex Univ Fed Minas Gerais Dept Comp Sci BR-31270901 Belo Horizonte MG Brazil

ISBN: (纸本)9781665423540

Labeling semantic segmentation datasets is a costly and laborious process if compared with tasks like image classification and object detection. this is especially true for remote sensing applications that not only work with extremely high spatial resolution data but also commonly require the knowledge of experts of the area to perform the manual labeling. Data augmentation techniques help to improve deep learning models under the circumstance of few and imbalanced labeled samples. In this work, we propose a novel data augmentation method focused on exploring the spatial context of remote sensing semantic segmentation. this method, ChessMix, creates new synthetic images from the existing training set by mixing transformed mini-patches across the dataset in a chessboard-like grid. ChessMix prioritizes patches with more examples of the rarest classes to alleviate the imbalance problems. the results in three diverse well-known remote sensing datasets show that this is a promising approach that helps to improve the networks' performance, working especially well in datasets with few available data. the results also show that ChessMix is capable of improving the segmentation of objects with few labeled pixels when compared to the most common data augmentation methods widely used.

关键词： Training image segmentation Semantics Object detection Manuals Spatial databases Labeling

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Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling 34

Iterative Pseudo-Labeling with Deep Feature Annotation and C...

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

作者： Benato, Barbara C. Telea, Alexandra C. Falcao, Alexandre X. Univ Estadual Campinas Inst Comp Lab Image Data Sci Campinas Brazil Univ Utrecht Fac Sci Dept Informat & Comp Sci Utrecht Netherlands

ISBN: (纸本)9781665423540

Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-prone, notably when it needs to be done by experts. To address this issue, increased attention has been devoted to techniques that propagate uncertain labels (also called pseudo labels) to large amounts of unsupervised samples and use them for training the model. However, these techniques still need hundreds of supervised samples per class in the training set and a validation set with extra supervised samples to tune the model. We improve a recent iterative pseudo-labeling technique, Deep Feature Annotation (DeepFA), by selecting the most confident unsupervised samples to iteratively train a deep neural network. Our confidence-based sampling strategy relies on only dozens of annotated training samples per class with no validation set, considerably reducing user effort in data annotation. We first ascertain the best configuration for the baseline - a self-trained deep neural network - and then evaluate our confidence DeepFA for different confidence thresholds. Experiments on six datasets show that DeepFA already outperforms the self-trained baseline, but confidence DeepFA can considerably outperform the original DeepFA and the baseline.

关键词： Training Deep learning graphics Annotations Manuals

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brazilian symposium of computer Graphic and image processing

Brazilian Symposium of Computer Graphic and Image Processing

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15th brazilian symposium on computer graphics and image processing, SIBGRAPI 2002

ISBN: (纸本)076951846X

the proceedings contain 87 papers. the topics discussed include: a semi-automatic surface reconstruction framework based on T-surfaces and isosurface extraction methods;DSVOL II - a distributed visualization and sonification application communicating via an XML-based protocol;visualizing inner structures in multimodal volume data;evaluating an adaptive windowing scheme in speckle noise MAP filtering;multispectral image data fusion using projections onto convex sets techniques;texture feature neural classifier for remote sensing image retrieval systems;filtering sparse data with 3D tensorial structuring elements;combining approximate geometry with view-dependent texture mapping - a hybrid approach to 3D video teleconferencing;improvement and invariance analysis of Zernike moments using as a region-based shape descriptor;automatic method for assessment of Telangiectasia degreeing by mathematical morphology;and approximating parametric curves with strip trees using affine arithmetic.

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Adaptive filters for image processing based on artificial neural networks 13

Adaptive filters for image processing based on artificial ne...

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

作者： Bittencourt, João Ricardo Osório, Fernando Santos UNISINOS - Universidade do Vale do Rio dos Sinos Centro de Ciências Exatas e Tecnológicas Curso de Informática Caixa Postal 275 São Leopoldo RS93022-000 Brazil

this paper presents our research in image processing (filters used for edges detection, color transformation, and distortion correction) using Artificial Neural Networks. We have developed a tool, Neuron Color, in ord... 详细信息

ISBN: (纸本)0769508782

关键词： Neural networks

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Autoencoder Satellite image Matching for UAV Geolocation in Long-Range High-Altitude Missions 37

Autoencoder Satellite Image Matching for UAV Geolocation in ...

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

作者： Cordova, Lucas B., V Brido, Stephanie L. Oliveira, Felipe G. Guerra, Rodrigo S. Drews-Jr, Paulo L. J. Univ Fed Rio Grande FURG Ctr Ciencias Computacionais C3 Rio Grande RS Brazil Univ Fed Amazonas UFAM Inst Ciencias Exatas & Tecnol ICET Itacoatiara AM Brazil

ISBN: (纸本)9798350376043;9798350376036

Vision-based geolocation is a promising way to overcome the vulnerabilities of Global Navigation Satellite System (GNSS) methods, which are subject to signal degradation, intentional interference, and environmental obstacles. this paper presents a novel approach to Unmanned Aerial Vehicle (UAV) geolocation in long-range and high-altitude missions using satellite imagery. Our method is based on the matching of encoded vector representations in embedded space, demonstrating robust performance to changes in vegetation and landscape. the neural network is used to encode satellite images of a reference map into embedding representations. image matching is performed in this embedded space using cross-correlation. We evaluated the accuracy and processing time of the proposed model by querying images along a 200 km northbound path at high altitude, covering an area larger than twenty thousand square kilometers. We also evaluated the network's generalization capability on an unknown map. Reference and query images are sourced from satellite images captured at different acquisition times to evaluate robustness due to appearance variations. the results demonstrate that the method can achieve up to 96.83% accuracy on a known map, while experiments on an unknown map averaged 90% accuracy. the processing time to match encoded images is 0.05 ms. these findings suggest the feasibility of integrating the method into more complex vision-based geolocation systems.

关键词： Satellite imagery

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Simplifying Horizon Picking Using Single-Class Semantic Segmentation Networks 34

Simplifying Horizon Picking Using Single-Class Semantic Segm...

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

作者： Calhes, Danilo Kobayashi, Felipe K. Mattos, Andrea Britto Macedo, Maysa M. G. Oliveira, Dario A. B. IBM Corp Rua Tutoia 1157 Sao Paulo Brazil Fed Univ ABC Ave Estados 5001 Santo Andre SP Brazil IBM Res Rua Tutoia 1157 Sao Paulo Brazil

ISBN: (纸本)9781665423540

Seismic image processing plays a significant role in geological exploration as it conditions much of the interpretation performance. the interpretation process comprises several tasks, and Horizon Picking is one of the most time-consuming. thereat, several works proposed methods for picking horizons automatically, mostly focusing on increasing the accuracy of data-driven approaches, by employing, for instance, semantic segmentation networks. However, these works often rely on a training process that requires several annotated samples, which are known to be scarce in the seismic domain, due to the overwhelming effort associated with manually picking several horizons in a seismic cube. this paper aims to evaluate the simplification of the labeling process required for training, by using training samples composed of disconnected horizons tokens, therefore relaxing the requirement of annotating the full set of horizons from each training sample, as commonly observed in previous works employing semantic segmentation networks. We assessed two state-of-art neural networks for general-purpose domains (PSP-Net and Deeplab V3+) using public seismic data (Netherlands F3 Block dataset). Our results report a minor impact in the performance using our proposed incomplete token training scheme compared to the complete one, moreover, we report that these networks outperform the current state-of-art for horizon picking from small training sets. thus, our approach proves to be advantageous for the interpreter, given that using partial results instead of providing a full annotation can reduce the user effort during the labeling process required for training the models.

关键词： Training graphics image segmentation Annotations Geology Semantics Neural networks

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