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Improving Spatial Feature Representation from Aerial Scenes by Using Convolutional Networks 28

Improving Spatial Feature Representation from Aerial Scenes ...

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Nogueira, Keiller Miranda, Waner O. dos Santos, Jefersson A. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781467379625

the performance of image classification is highly dependent on the quality of extracted features. Concerning high resolution remote image images, encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models to classify them. Even though many visual descriptors have been proposed or successfully used to encode spatial features of remote sensing images, some applications, using this sort of images, demand more specific description techniques. Deep Learning, an emergent machine learning approach based on neural networks, is capable of learning specific features and classifiers at the same time and adjust at each step, in real time, to better fit the need of each problem. For several task, such image classification, it has achieved very good results, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data. In this paper, we propose a novel network capable of learning specific spatial features from remote sensing images, with any pre-processing step or descriptor evaluation, and classify them. Specifically, automatic feature learning task aims at discovering hierarchical structures from the raw data, leading to a more representative information. this task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for urban planning, crop and forest management and climate modelling. the propose convolutional neural network has six layers: three convolutional, two fully-connected and one classifier layer. So, the five first layers are responsible to extract visual features while the last one is responsible to classify the images. We conducted a systematic evaluation of the proposed method using two datasets: (i) the popular aerial image dataset UCMerced Land-use and, (ii) a multispectral high-resolution scenes of the brazilian Coffee Scenes. the experiments show that the pro

关键词： Deep Learning Remote Sensing Feature Learning image Classification Machine Learning High-resolution images

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Using Decision Trees to Accelerate the H.266/VVC-to-AV1 Video Transcoding 36

Using Decision Trees to Accelerate the H.266/VVC-to-AV1 Vide...

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36th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Camargo, Caroline Borges, Alex Zatt, Bruno Porto, Marcelo Correa, Guilherme Fed Univ Pelotas UFPel Grad Program Comp PPGC Video Technol Res Grp ViTech Pelotas RS Brazil

ISBN: (纸本)9798350338737;9798350338720

H.266/VVC is the current state-of-the-art video coding format, launched in 2020. Despite its high capability to compress video, including ultra-high definition content, royalty costs may prevent its wide adoption. the AOMedia Video 1 (AV1) format is an alternative with competitive coding efficiency, besides being a royalty-free format. However, migrating legacy content from one format to another is a costly task, which requires long processing times. this work presents a solution for accelerating the H.266/VVC-to-AV1 transcoding based on machine learning. Twelve decision tree models trained with data gathered during the H.266/VVC decoding and the AV1 encoding processes are proposed and implemented in the libaom reference software, leading to a complexity reduction of 12.60% at the cost of coding efficiency losses of 1.81% on average. To the best of the authors' knowledge, this is the first H.266/VVC-to-AV1 transcoding acceleration solution published in the literature.

关键词： image coding

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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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Improving Face Detection Performance by Skin Detection Post-processing 30

Improving Face Detection Performance by Skin Detection Post-...

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

作者： Lucena, Oeslle Oliveira, Italo de P. Veloso, Luciana Pereira, Eanes Univ Campinas UNICAMP Campinas Brazil Fed Univ Campina Grande UFCG Campina Grande Brazil

ISBN: (纸本)9781538622193

Face detection is already incorporated in many biometrics and surveillance applications. therefore, the reduction of false detections is a priority in those systems. However, face detection is still challenging. Many factors, such as pose variation and complex backgrounds, contribute to false detections. Besides, the fidelity of a true detection, measured by precision rate, is a concern in content-based information retrieval. Following those issues, combinations of methods are developed focusing on balancing the trade-off between hit-rate and miss-rate. In this paper, we present an approach that improves face detection based on a post-processing of skin features. Our method enhanced the performance of weak detectors using a straightforward and low complex skin percentage threshold constraint. Furthermore, we also present a statistical analysis comparing our approach and two face detectors, under two different conditions for skin detection training, using a robust dataset for testing. the experimental results showed a significant drop in the number of false positives, reducing in 53%, while the precision rate was elevated in almost 5% when the Viola-Jones approach was used as face detector.

关键词： Face detection Skin detection Performance Improvement Post-processing

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Fine-grained Cars Recognition using Deep Convolutional Neural Networks 35

Fine-grained Cars Recognition using Deep Convolutional Neura...

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

作者： Oliveira, Franklin Macena, Arianne Kamel, Otavio Souza, Wesley Freitas, Nicksson Vinuto, Tiago Univ Fed Pernambuco Comp Ctr Recife Brazil SiDi Recife Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Population growth and the high concentration of vehicles on urban roads have negatively impacted urban mobility and the global environment since the primary transportation modes occupy a lot of space on the streets and are one of the main polluting gas emitters. In this context of inefficient urban mobility and unsustainability, the Intelligent Transportation Systems (ITS) aims to solve or minimize urban traffic issues. ITS are also widely used in applications focused on traffic safety, such as vehicle recognition related to a traffic or law violation. For this task, the fine-grained vehicle classification technique is mainly used by computer vision and deep learning advances. However, identifying vehicles by the model can be problematic because the same vehicle can be easily misclassified when observed from different perspectives, with different colours, or by similar models. Knowing these inherent issues from vehicle recognition tasks, Deep Convolutional Neural Networks (DCNNs) are commonly used due to their ability to extract features from images. In that regard, the goal of this paper is to evaluate some state of art DCNNs architectures, conducting experiments with three different datasets to identify which architectures have the best performance metrics in the refined car classification task within ITS context.

关键词： Training computer vision Computational modeling Urban areas computer architecture Feature extraction Safety

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An Investigation of 2D Keypoints Detection on Challenging Scenarios Using Depthwise Separable Convolutions: A Hand Pose Estimation Case Study 34

An Investigation of 2D Keypoints Detection on Challenging Sc...

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

作者： Costa, Willams Figueiredo, Lucas Teixeira, Joao Marcelo Lima, Joao Paulo Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil

ISBN: (纸本)9781665423540

2D keypoints detection is a computer vision task applicable to several fields such as hand, face, and body tracking, which provides useful information for spatial analytics, gestural interactions, and augmented reality applications. this work investigates the usage of depthwise separable convolutions (an optimized convolution operation) to speed up the inference time on a largely used architecture for 2D keypoints estimation. We evaluate the impacts on the precision and performance of such optimization on a hand pose estimation task. We also extend the evaluation towards simulated challenging scenarios of defocused lens, motion blur, occlusions, and noisy images to understand how these stress situations affect both the original and the optimized architectures. We show that the execution time can be improved on average by 12.8% with an accuracy compromise of less than 1 pixel (mean EPE). the experiments on challenging scenarios revealed that the model powered by depthwise separable convolutions is most fit for the occlusion cases and noisy environments while suffering more on the motion blur simulated scenarios.

关键词： graphics computer vision Pose estimation computer architecture Noise measurement Task analysis Stress

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3D Modeling by Scanning Physical Modifications 28

3D Modeling by Scanning Physical Modifications

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Hattab, Ammar Taubin, Gabriel Brown Univ Sch Engn Providence RI 02912 USA

ISBN: (纸本)9781467379625

3D shape design tends to be a long and tedious process, with the design of a detailed 3D part usually requiring multiple revisions. Fabricating physical prototypes using low cost 3D fabrication technologies at intermediate stages of the design process is now a common practice, which helps the designer discover errors, and to incrementally refine the design. Most often, implementing the required changes directly in the computer model, within the 3D modeling software, is more difficult and time consuming than modifying the physical model directly using hand cutting, caving and sculpting tools, power tools, or machine tools. When one of the two models is modified, the changes need to be transferred to the other model, a process we refer to as synchronization. Changes made to the computer model can be transferred to the physical model by 3d printing a new physical model. In this paper, we address the problem of synchronizing the computer model to changes made in the physical model by 3D scanning the modified physical model, automatically detecting the changes, and updating the computer model. the proposed process comprises algorithms to: 1) register each 3D scan with a previous 3D scan and/or with the 3D representation used by the 3D modeling software;2) detect the changes (subtractive and/or additive);and 3) perform the changes on the 3D computer model.

关键词： 3D registration 3D reverse engineering 3D modeling tangible interface human-computer interaction

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Multispectral image Segmentation With Dimensionality Reduction Using Autoencoders 36

Multispectral Image Segmentation With Dimensionality Reducti...

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36th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Albuquerque, Eliton Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre Brazil

ISBN: (纸本)9798350338737;9798350338720

Autoencoder (AE) implementations through neural networks have achieved impressive results on dimensionality reduction tasks, such as multispectral (MS) imagery compression. Dimensionality reduction algorithms are necessary when dealing with large multispectral datasets, since the data captured by multiple levels of narrow spectral wavelengths causes high processing and storage costs, particularly when such highly dimensional MS data are used as input to deep learning networks. Traditional data compression techniques like Principal Component Analysis (PCA) are popular in remote sensing applications. However, its implementation on MS data may make the data unusable for computer vision (CV) tasks such as image segmentation, especially when applying severe compression. On the other hand, AEs provide great generalization capabilities over complex data, especially when combined with other CV pipelines. For the relevant problem of semantic segmentation, the results are considerably degraded when using dimensionality-reduced images with PCA. When using vanilla autoencoders trained with the traditional MSE loss, the segmentation results improve over PCA but are still considerably behind the one obtained with uncompressed data, which indicates a potential domain shift. In this work, we show that training an AE using a combination of the MSE loss and an additional proxy loss based on a pre-trained segmentation module can significantly improve the AE restoration process, alleviating the accuracy drop of semantic segmentation even for strong compression rates. Our code is available at https://***/elitonfilho/pca.

关键词： Semantic Segmentation

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brazilian Road's Animals (BRA): An image Dataset of Most Commonly Run Over Animals 35

Brazilian Road's Animals (BRA): An Image Dataset of Most Com...

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

作者： Ferrante, Gabriel S. Nakamura, Luis H. V. Andrade, Fernando R. H. Rocha Filho, Geraldo P. De Grande, Robson E. Meneguette, Rodolfo I. Univ Sao Paulo Inst Sci Math & Comp Sci Sao Carlos Brazil Fed Inst Sao Paulo Dept Informat Catanduva Brazil Univ Brasilia Dept Comp Sci Brasilia Brazil Brock Univ Dept Comp Sci St Catharines ON Canada

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

In Brazil, about 475 million animals, including small and large species, are killed on the roads every year. Although this problem with wildlife can be considered recurrent, it involves significant investments and resources whose governments cannot afford to allocate to develop new mechanisms and technologies to identify, monitor, and guarantee the safety of animals in high-risk areas. In the field of computer vision, there are already datasets about animals in the literature, but not focusing on animals that suffer most accidents on highways. this work thus aims to present a new dataset of labelled images to address this gap, the BRA-Dataset. the labeling concerns five medium and large mammals that die the most on brazilian roads, which can serve as a training dataset for future animal detection models on highways. the images were obtained using the proposed acquisition approach with a manual cleaning process;the dataset contain 1823 images in total and labelled in the YOLO Darknet and PASCAL VOC format. Also was effectuated experimentation on BRA-Dataset for validation of its performance on YOLO models training and tests. the results showed a high average precision achieved over the dataset in its first version without data augmentation or application process of improvement.

关键词： Training computer vision Animals Biological system modeling Roads Data models Safety

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SynLibras: A Disentangled Deep Generative Model for brazilian Sign Language Synthesis 35

SynLibras: A Disentangled Deep Generative Model for Brazilia...

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

作者： Silveira, Wellington Alaniz, Andrew Hurtado, Marina da Silva, Bernardo Castello de Bem, Rodrigo Fed Univ Rio Grande FURG Ctr Computat Sci C3 Rio Grande Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Recent advances regarding deep generative models have strengthened a realm of approaches in which discriminative and generative tasks are tackled jointly in an analysis-by-synthesis manner. In this category, variational autoencoders (VAEs) and generative adversarial networks (GANs) aim for learning latent data representations from which sampling of synthetic images may be performed. However, sampling in such models normally does not allow for independent control of diverse factors of variation. Despite general efforts to overcome this issue, deep generative models tailored for sign language with disentangled factors of variation are yet not vastly explored in the literature. In this work, we introduce the SynLibras, a novel model that allows for disentangling appearance and gestural communication (i.e. body, hands and face poses) on image synthesis. Our model is capable of performing cross-language pose-transfer while maintaining the appearance of the source signer. We perform experiments on the RWth-PHOENIX-Weather dataset and evaluation using the PSNR and the SSIM metrics. To our knowledge, the SynLibras is the first method for brazilian sign language (Libras) synthesis in images. We compare our model with the EDN, a well-known general pose-transfer method, achieving better results on Libras synthesis. Finally, we also introduce the SynLibras-Pose, a dataset with annotated poses of Libras signers performing single words.

关键词： Measurement graphics Analytical models image synthesis Gesture recognition Assistive technologies Generative adversarial networks

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