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检索条件"任意字段=15th Brazilian Symposium on Computer Graphics and Image Processing"

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Improving Local Latent Fingerprint Representations Under Data Constraints

Improving Local Latent Fingerprint Representations Under Dat...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： André Nóbrega Ilan theodoro Pascual Figueroa Alexandre Falcão Institute of Computing Unicamp Campinas São Paulo Brazil Research and Development Griaule Campinas São Paulo Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Latent fingerprint identification is one of the most challenging tasks in fingerprint recognition due to poor image quality, small fingerprint areas, and significant deformations. State-of-the-art approaches typically treat the problem as a minutia classification task, requiring the establishment of weak minutiae correspondence labels and relying on deep minutiae descriptors trained on large private datasets. this paper proposes a novel method to develop efficient minutiae descriptors for latent fingerprint identification without using private datasets. Our approach utilizes self-supervised (contrastive) learning to design a similarity function using a Siamese network between pairs of aligned minutiae patches. We generate synthetic latent fingerprints from high-quality rolled and plain images in the public NIST SD 302 database to address the scarcity of real latent fingerprint databases. During the training, we employ the NTXent loss function to maximize the cosine similarity between the embeddings from a minutia patch and those from its synthetic augmented version. this removes the necessity for establishing weak minutiae correspondence labels. Experiments on the NIST SD27 database demonstrate that our method significantly improves identification performance, achieving a 6.59% increase in the hit rate compared to commercial software. this work highlights the potential of self-supervised learning and data augmentation techniques in advancing latent fingerprint identification systems.

关键词： Training image quality graphics Databases Deformation Training data Contrastive learning Fingerprint recognition NIST Software

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Analysis of Frequency Range Effect on the Detection of Voice Disorder Using Convolutional Neural Networks Trained on Spectogram images

Analysis of Frequency Range Effect on the Detection of Voice...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： José Alberto Souza Paulino Herman Martins Gomes Leonardo Vidal Batista Leonardo Wanderley Lopes Department of Computer Science Federal University of Campina Grande (UFCG) Department of Computer Integrated Voice Studies Laboratory Federal Univeristy of Paraiba (UFPB)

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Considering the current advancements in signal processing and machine learning (ML), non-invasive techniques for assessing vocal quality have become increasingly popular, especially with the use of spectrograms in acoustic analysis, which typically do not evaluate patterns in regions above 5kHz, either through visual inspection or using ML algorithms. this study aims to assess the relevance of different frequency ranges in classifying healthy and disordered voices using convolutional neural networks (CNN), as well as to investigate whether the combination of frequency ranges can improve classification results. To achieve this goal, spectrogram subsets were generated from 16 frequency ranges in two datasets, obtained through a bank of band-pass filters, and trained in CNN models with transfer learning. the study was conducted by first evaluating the relevance of each frequency range individually. then, the results of the 65,536 possible combinations obtained with the 16 frequency ranges were assessed. this analysis revealed that it is possible to characterize voice pathology patterns in frequency regions above 5kHz, but the interval between 1 to 1,462 Hz is substantially better in terms of descriptive capacity in spectrograms. Additionally, it was observed that high-frequency regions, when combined with other frequency ranges, produce better classification results, improving the test accuracy from 80.53% to 82.10% in the SVD dataset and from 78.11% to 82.12% in the AVFAD dataset.

关键词： Pathology Visualization Machine learning algorithms Databases Transfer learning Signal processing algorithms Feature extraction Vectors Convolutional neural networks Spectrogram

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DPR-V2S: A Deep Framework for Periocular Recognition in Surveillance Environments

DPR-V2S: A Deep Framework for Periocular Recognition in Surv...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Luiz Guilherme Fonseca Carreira David Menotti William Robson Schwartz Department of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil Department of Informatics Federal University of Paraná Curitiba Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

In recent years, biometrics systems have attracted much attention from the biometric community, especially periocular recognition. the periocular region is considered one of the most discriminative and reliable regions of the human face since it suffers less from aging and variations in facial expression. Many studies have shown its potential as a biometric trait that can be used for verification and identification tasks. However, these methods ignore periocular recognition performance between modalities: video-to-still (V2S) and still-to-video (S2V), and surveillance scenarios. therefore, this research field remains open and it needs to be exploited. Because of that, we decide to perform the periocular recognition in surveillance scenarios and between modalities, i.e., V2S. Our primary goal is to establish a new performance metric under these circumstances so that the periocular region's potential use under uncontrolled conditions and between modalities can be evaluated. thus, we propose a new approach called DPR-V2S to perform periocular recognition, where the probe data consists of a variable number of frames from a video and the target data consists of a still image. Our method uses information from each frame enhancing its utility in the verification and identification tasks. Results are reported for the Cox dataset and show that our method surpasses the baseline in all evaluation metrics. In the best scenario, our approach achieves 98% of TPR $({@}\text{FPR} =1e^{-1})$ , 4.60% of EER, 3.64 of decidability, and 87.84 % of Rank-5.

关键词： Measurement graphics image recognition Target recognition Surveillance Face recognition Aging Reliability Probes

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LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection

LiwTERM: A Lightweight Transformer-Based Model for Dermatolo...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Luis A. Souza André G. C. Pacheco Gabriel G. de Angelo thiago Oliveira-Santos Christoph Palm João P. Papa Graduate Program of Informatics Federal University of Espírito Santo Vitória Brazil OTH Regensburg Regensburg Medical Image Computing (ReMIC) Regensburg Germany Department of Computing São Paulo State Univesity São Paulo Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Skin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient's clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient's history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.

关键词： graphics Computational modeling computer architecture Transformers Skin Lesions Clinical diagnosis Reliability History Skin cancer

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FPGA Based Real-Time Tracking Approach with Validation of Precision and Performance

FPGA Based Real-Time Tracking Approach with Validation of Pr...

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Euromicro symposium on Digital System Design

作者： Alexander Bochem Kenneth B. Kent Rainer Herpers Faculty of Computer Science University of New Brunswick Fredericton Canada Department of Computer Science University of Applied Sciences Bonn-Rhein-Sieg Sankt Augustin Germany

this paper presents the implementation and validation of a tracking approach for image processing in hardware. It compares the implementation for the addressed problem on a Field Programmable Gate Array (FPGA) with a software implementation for a General Purpose Processor (GPP) architecture. For both solutions the implementation costs for their development is an important aspect in the validation. this research project is motivated by the MI6 project of the computer Vision research group, which is located at the Bonn-Rhein-Sieg University of Applied Sciences. the intent of the MI6 project is the tracking of a user in an immersive environment. In this research work the development and validation of a detection system for BLOBs on a Cyclone II FPGA from Altera has been implemented. the analysis of the hardware solution showed a similar precision for the tracking as the software approach. One problem is the large increase of allocated resources when extending the system to process more objects. the implementation of the tracking approach in hardware required much more effort than the software solution. the design of high level problems in hardware for this case are more expensive than the software implementation. the results of the implementation indicate that a mixed hardware/software solution would provide optimal results.

关键词： Software Hardware Target tracking Data visualization Field programmable gate arrays Euclidean distance Libraries

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An Adjustable Error Measure for image Segmentation Evaluation

An Adjustable Error Measure for Image Segmentation Evaluatio...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Oscar Cuadros Linares Glenda Botelho Francisco Rodrigues João Batista Neto Instituto de Ciências Matemáticas e de Computação (ICMC) University of São Paulo (USP) São Carlos Brazil Universidade Federal do Tocantins (UFT) Palmas-To Brazil

Due to the subjective nature of the segmentation process, quantitative evaluation of image segmentation methods is still a difficult task. Humans perceive image objects in different ways. Consequently, human segmentations may come in different levels of refinement, ie, under- and over-segmentations. Popular segmentation error measures in the literature (Arbelaez and OCE) are supervised methods (also called empirical discrepancy methods), in which error is computed by comparing objects in segmentations with a reference (ground-truth) image produced by humans. Since reference images can be many, the key issue for a segmentation error measure is to be consistent in the presence of both under- and over-segmentation. In general, the term consistency refers to the ability of the error measure to be low, when comparing similar segmentations, or high, when faced with different segmentations, while capturing under-or over-segmentations. In this paper we propose a new object-based empirical discrepancy error measure, called Adjustable Object-based Measure (AOM). We introduce a penalty parameter which gives the method the ability to be more (or less) responsive in the presence of over-segmentation. Hence, we extend the notion of consistency so as to include the application's need in the process. Some applications require segmentation to be extremely accurate, hence under-or over-segmentation should be well penalised. Others, do not. By changing the penalty parameter, AOM can deliver more consistent results not only in reference to the under-or over-segmentation issue alone, but also according to the nature of the application. We compare our method with Arbelaez (used as standard measure in the benchmark of Berkeley Segmentation image Dataset) and OCE. Our results show that AOM not only is more consistent in the presence of over-segmentation, but is also faster to compute. Unlike Arbelaez and OCE, AOM also satisfies the metric axiom of symmetry.

关键词： image segmentation Measurement uncertainty Current measurement Standards Indexes Electronic mail

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Space-Time Urban Explorer: A Visual Tool for Exploring Spatiotemporal Crime and Patrolling Data

Space-Time Urban Explorer: A Visual Tool for Exploring Spati...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Tiago Paulino Santos João Matheus Siqueira Souza thales Vieira Luis Gustavo Nonato Institute of Computing Federal University of Alagoas (UFAL) Maceio Brazil Institute of Mathematics and Computer Sciences University of Sao Paulo (ICMC-USP) Sao Carlos Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices' locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. these techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. the effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.

关键词： Dimensionality reduction Law enforcement Visual analytics Prevention and mitigation Decision making Data visualization Public security Spatiotemporal phenomena Data mining Socioeconomics

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Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark

Toward Enhancing Vehicle Color Recognition in Adverse Condit...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Gabriel E. Lima Rayson Laroca Eduardo Santos Eduil Nascimento David Menotti Department of Informatics Federal University of Paraná Curitiba Brazil Postgraduate Program in Informatics Pontifical Catholic University of Paraná Curitiba Brazil Department of Technological Development and Quality Paraná Military Police Curitiba Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Vehicle information recognition is crucial in various practical domains, particularly in criminal investigations. Vehicle Color Recognition (VCR) has garnered significant research interest because color is a visually distinguishable attribute of vehicles and is less affected by partial occlusion and changes in viewpoint. Despite the success of existing methods for this task, the relatively low complexity of the datasets used in the literature has been largely overlooked. this research addresses this gap by compiling a new dataset representing a more challenging VCR scenario. the images - sourced from six license plate recognition datasets - are categorized into eleven colors, and their annotations were validated using official vehicle registration information. We evaluate the performance of four deep learning models on a widely adopted dataset and our proposed dataset to establish a benchmark. the results demonstrate that our dataset poses greater difficulty for the tested models and highlights scenarios that require further exploration in VCR. Remarkably, nighttime scenes account for a significant portion of the errors made by the best-performing model. this research provides a foundation for future studies on VCR, while also offering valuable insights for the field of fine-grained vehicle classification.

关键词： Deep learning image recognition image color analysis Lighting Benchmark testing Multitasking Pattern recognition Reliability Video recording License plate recognition

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Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation

Tightening Classification Boundaries in Open Set Domain Adap...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Lucas Fernando Alvarenga e Silva Nicu Sebe Jurandy Almeida Institute of Computing University of Campinas (UNICAMP) Brazil Department of Information Engineering and Computer Science University of Trento (UniTN) Italy Department of Computing Federal University of são Carlos (UFSCar) Brazil

Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data. However, when those methods are applied to non-controllable environments, many different factors can degrade the model's expected performance, such as unlabeled datasets with different levels of domain shift and category shift. Particularly, when both issues occur at the same time, we tackle this challenging setup as Open Set Domain Adaptation (OSDA) problem. In general, existing OSDA approaches focus their efforts only on aligning known classes or, if they already extract possible negative instances, use them as a new category learned with supervision during the course of training. We propose a novel way to improve OSDA approaches by extracting a high-confidence set of unknown instances and using it as a hard constraint to tighten the classification boundaries of OSDA methods. Especially, we adopt a new loss constraint evaluated in three different means, (1) directly with the pristine negative instances; (2) with randomly transformed negatives using data augmentation techniques; and (3) with synthetically generated negatives containing adversarial features. We assessed all approaches in an extensive set of experiments based on OVANet, where we could observe consistent improvements for two public benchmarks, the Office-31 and Office-Home datasets, yielding absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and 5.8% for Accuracy and 4.7% for H-Score on Office-Home.

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Feature Fusion for Graph Convolutional Networks in Semi-Supervised image Classification

Feature Fusion for Graph Convolutional Networks in Semi-Supe...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Marina Chagas Bulach Gapski Lucas Pascotti Valem Daniel Carlos Guimarães Pedronette Department of Statistics Applied Mathematics and Computing São Paulo State University (UNESP) Rio Claro Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

In recent years, the volume of multimedia data has been rapidly increasing across various applications. Consequently, classification methods capable of handling scenarios with limited labeled data (e.g., semi-supervised, weakly supervised) have become critically important, particularly because acquiring labeled data is often expensive and time-consuming. Regarding image data, feature extraction approaches are commonly employed in many tasks. Feature extraction involves identifying and extracting key characteristics or patterns, such as edges, textures, shapes, and colors. Nowadays, most extractors consider deep learning strategies, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViT). With various feature extractors available in the literature, there is a wide diversity of features that can be considered. the features extracted from an image depend on the application, the extractor used, and its configuration. therefore, combining different extractors can be a promising strategy to exploit complementary information. Graph Convolutional Networks (GCNs) are fundamental and promising strategies in the scenario of semi-supervised image classification, being able to leverage labeled and unlabeled data, and exploiting the graph structures that offer valuable information. this work proposes an approach for GCNs in scenarios where labeled data is scarce, combining sets of features and graphs considering different extraction approaches. Among the main contributions, the experimental results reveal that these combinations and the use of manifold learning to process these graphs improve the classification results in most cases.

关键词： Graph convolutional networks Shape image color analysis image edge detection Semisupervised learning Feature extraction Transformers Manifold learning Data mining image classification

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