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AEIMPS: Deep Autoencoder for Image Retargeting Quality Assessment 用于图像重定向质量评估的深度自动编码器

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23263

Levi C. Carvalho, Saulo A. F. Oliveira

引用次数: 0

Epileptic seizure detection with Convolutional Neural Networks and the Continuous Wavelet Transform 基于卷积神经网络和连续小波变换的癫痫发作检测

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23264

Carlos E. R. Cardoso, A. Díaz, Aline P Pansani, Emerson Ntikawa, D. Colugnati, P. Braga, Cláudio Quintino, Marcos Aureliano

{"title":"Epileptic seizure detection with Convolutional Neural Networks and the Continuous Wavelet Transform","authors":"Carlos E. R. Cardoso, A. Díaz, Aline P Pansani, Emerson Ntikawa, D. Colugnati, P. Braga, Cláudio Quintino, Marcos Aureliano","doi":"10.5753/sibgrapi.est.2022.23264","DOIUrl":"https://doi.org/10.5753/sibgrapi.est.2022.23264","url":null,"abstract":"The study of epileptic seizure often involves animal models to simulate the human behavior. Such models demand monitoring the evolution of the animal behavior continuously. Detecting seizure in this setup remains a challenge, because it typically requires trained personnel to annotate video sequences looking for the timestamps of seizure events. Deep Learning methods can help to solve this task in a more automatic and efficient manner due to their capacity of retrieving patterns from data. In this work, we conducted a pilot study to detect epileptic seizure from the images of small rodents using Convolutional Neural Networks (CNN) and the Continuous Wavelet Transform (CWT). We used the Social LEAP Estimates Animal Poses (SLEAP) framework for animal recognition to extract the morphological skeleton. Then, our CWT-CNN method used information of the frequency, magnitude and temporal evolution of head and thorax displacements to classify the animal behavior. The results showed a mean accuracy of 82.7%in the classification of epileptic seizure events.","PeriodicalId":182158,"journal":{"name":"Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116257440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Python Framework for Objective Visual Quality Assessment 用于客观视觉质量评估的Python框架

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23271

Caio L. Saigg, Bruno S. S. Dias, Andr Costa, Mylène C. Q. Farias, H. Martinez

引用次数: 0

Avoiding Overfitting: new algorithms to improve generalization in Convolutional Neural Networks 避免过拟合:改进卷积神经网络泛化的新算法

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23255

C. F. G. Santos, J. P. Papa

{"title":"Avoiding Overfitting: new algorithms to improve generalization in Convolutional Neural Networks","authors":"C. F. G. Santos, J. P. Papa","doi":"10.5753/sibgrapi.est.2022.23255","DOIUrl":"https://doi.org/10.5753/sibgrapi.est.2022.23255","url":null,"abstract":"Deep Learning has achieved state-of-the-art results in several domains, such as im- age processing, natural language processing, and audio processing. To accomplish such results, it uses neural networks with several processing layers along with a massive amount of labeled information. One particular family of Deep Learning is the Convolutional Neural Networks (CNNs), which work using convolutional layers de- rived from the digital signal processing area, being very helpful to detect relevant features in unstructured data, such as audio and pictures. One way to improve results on CNN is to use regularization algorithms, which aim to make the training process harder but generate models that generalize better for inference when used in applications. The present work contributes to the area of regularization methods for CNNs, proposing more methods for use in different image processing tasks. This thesis presents a collection of works developed by the author during the research period, which were published or submitted until the present time, presenting: (i) a survey, listing recent regularization works and highlighting the solutions and problems of the area; (ii) a neuron dropping method to use in the tensors generated during CNNs training; (iii) a variation of the mentioned method, changing the dropping rules, targeting different features of the tensor; and (iv) a label regularization algorithm used in different image processing problems.","PeriodicalId":182158,"journal":{"name":"Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130878709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Deep Learning Utilized for Person Recognition Based on the Biometric Features of the Periocular Region 基于眼周区域生物特征的深度学习用于人物识别

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23270

Leonardo Ferreira Nascimeno, Jones Mendonça de Souza

引用次数: 0

Predicting oil field production using the Random Forest algorithm 利用随机森林算法预测油田产量

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23277

Isabel F. A. Gonçalves, Thiago M. D. Silva, Abelardo Barreto, S. Pesco

{"title":"Predicting oil field production using the Random Forest algorithm","authors":"Isabel F. A. Gonçalves, Thiago M. D. Silva, Abelardo Barreto, S. Pesco","doi":"10.5753/sibgrapi.est.2022.23277","DOIUrl":"https://doi.org/10.5753/sibgrapi.est.2022.23277","url":null,"abstract":"Precisely forecasting oil field performance is essential in oil reservoir planning and management. Nevertheless, forecasting oil production is a complex nonlinear problem due to all geophysical and petrophysical properties that may result in different effects with a bit of change. All decisions to be made during an exploitation project needs to be made considering different efficient algorithms to simulate data, providing robust scenarios to lead to the best deductions. To reduce the uncertainty in the simulation process, researchers have efficiently introduced machine learning algorithms for solving reservoir engineering problems because they can extract the maximum information from the dataset. Accordingly, this paper proposes using a Random Forest model to predict the daily oil production of an offshore reservoir. In this study, the oil rate production is considered a time series and was pre-processed and restructured to fit a supervised learning problem. We use the Random Forest model to forecast a one-time step, which is an extension of decision tree learning, widely used in regression and classification problems for supervised machine learning. For testing the robustness of the proposed model, we use the Volve oil field dataset as a case study to conduct the experiments. The results indicate that the Random Forest model could adequately estimate the one-time step of the oil field production.","PeriodicalId":182158,"journal":{"name":"Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129105309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

On Model Complexity Reduction in Instance-Based Learners 基于实例的学习器模型复杂度降低研究

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23253

Saulo A. F. Oliveira, A. R. Rocha Neto, J. Gomes

{"title":"On Model Complexity Reduction in Instance-Based Learners","authors":"Saulo A. F. Oliveira, A. R. Rocha Neto, J. Gomes","doi":"10.5753/sibgrapi.est.2022.23253","DOIUrl":"https://doi.org/10.5753/sibgrapi.est.2022.23253","url":null,"abstract":"Instance-based learners habitually adopt instance selection techniques to reduce complexity and avoid overfitting. Such learners’ most recent and well-known formulations seek to impose some sparsity in their training and prediction structure alongside regularization to meet such a result. Due to the variety of such instance-based learners, we will draw attention to the Least-Squares Support Vector Machines and Minimal Learning Machines because they embody additional information beyond the stored instances to perform predictions. Later, this thesis proposes variants constraining candidate solutions within a specific functional space where we avoid overfitting and reduce model complexity. The central core of such variants is related to penalizing samples with a specific condition during learning. For regressors, we adopted strategies based on random and observed linearity conditions related to the data. At the same time, we borrowed definitions from the computer vision field for classification tasks to derive a concept we call the classcorner relationship (in which we designed an instance selection algorithm). In the Least-Squares Support Vector Machines context, this thesis follows the pruning fashion by adopting the samples that share such a class-corner relationship. As for the Minimal Learning Machine model, this thesis introduces a new proposal called the Lightweight Minimal Learning Machine, a faster model for out-of-sample prediction due to the reduced number of computations inherent in the original proposal’s multilateration process. Another remarkable feature is that it derives a unique solution when other formulations rely on overdetermined systems.","PeriodicalId":182158,"journal":{"name":"Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117115609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creation of Materials from Tabular BRDFs 从表格brdf中创建材料

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23257

Mislene Da Silva Nunes, Gastão Florêncio Miranda Jr., Beatriz Trinchão Andrade

{"title":"Creation of Materials from Tabular BRDFs","authors":"Mislene Da Silva Nunes, Gastão Florêncio Miranda Jr., Beatriz Trinchão Andrade","doi":"10.5753/sibgrapi.est.2022.23257","DOIUrl":"https://doi.org/10.5753/sibgrapi.est.2022.23257","url":null,"abstract":"Rendering materials with a realistic appearance requires considering how they interact with the light. Bidirectional Reflectance Distribution Functions (BRDFs) are often used to achieve this goal. There are different ways to represent materials from BRDFs, which include tabular BRDFs, analytical models, and linear combinations of a BRDF database. In the last decade, the search for more realism in rendering increased the interest in using tabular BRDFs. However, this approach requires a long acquisition process and high storage space. This master dissertation proposes a pipeline to create new materials from a tabular BRDF database. During this process, we also explored two related topics: we compiled and proposed techniques to evaluate BRDFs, and developed an approach to preprocess and cluster a BRDF database. These researches presented insights and contributions that are useful for contexts other than ours and provided analysis that reinforced our choice of techniques to reach our goal. As a final result, our method creates new materials with realism and consistency.","PeriodicalId":182158,"journal":{"name":"Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122689001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estudo e Comparação de Técnicas de Compressão de Imagens Baseadas em Transformadas Discretas 基于离散变换的图像压缩技术的研究与比较

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23275

A. A. Brito-filho, Kenji Nose-Filho

引用次数: 0

Rede Neural Convolucional para Detecção de Pedestres Realizando Travessias de Risco 用于危险十字路口行人检测的卷积神经网络

Anais Estendidos do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022) Pub Date : 2022-10-24 DOI: 10.5753/sibgrapi.est.2022.23276

M. L. D. Santos, C. Mauricio, V. N. D. Santos, F. F. F. Peres

引用次数: 0

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