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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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Ensemble of Semantic Segmentation Models for Oral Epithelial Dysplasia images

Ensemble of Semantic Segmentation Models for Oral Epithelial...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Adriano B. Silva Thaína A. A. Tosta Leandro A. Neves Alessandro S. Martins Paulo R. De Faria Marcelo Z. Do Nascimento Faculty of Computer Science Federal University of Uberlândia Science and Technology Institute Federal University of São Paulo Department of Computer Science and Statistics (DCCE) São Paulo State University Federal Institute of Triângulo Mineiro Department of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Early detection of potentially malignant disorders such as oral epithelial dysplasia (OED) is important for preventing oral cancer. Semantic segmentation of nuclei in histopathological images provides relevant insights for pathologists. CNN-based methods have shown promise in improving histological lesion detection and segmentation processes, but achieving results with significant values in terms accuracy metrics remains a challenging task. This paper presents an ensemble approach to enhance the performance of semantic segmentation for nuclei in OED histopathology images. Six CNN models were employed, and their outputs were associated using three ensemble strategies: simple averaging, weighted averaging, and majority voting. To further enhance model robustness, a data augmentation stage was assessed. The proposed ensemble, combined with an image augmentation strategy, achieved accuracy and Dice coefficient values of 93.41 % and 0.88, respectively, on OED images. Analysis of the OED grades showed values ranging from 91.14% to 95.24 % and 0.87 to 0.90 for accuracy and Dice coefficient, respectively. These values show an improvement over the CNN segmentation models. The analysis of segmentation performance with the OED grade images is another significant contribution of this study that addresses a gap in the literature. A validation stage on three publicly available datasets demonstrated that our approach is on par with state-of-the-art methods.

关键词： Instance segmentation image segmentation Solid modeling Accuracy Semantic segmentation Data augmentation Robustness Lesions Ensemble learning Cancer

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New Approach Generative AI Melanoma Data Fusion for Classification in Dermoscopic images with Large Language Model

New Approach Generative AI Melanoma Data Fusion for Classifi...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Adriell Gomes Marques Marcus Vinicius Candido de Figueiredo José Jerovane da Costa Nascimento Cidcley Teixeira de Souza Carlos Mauricio Jaborandy de Mattos Dourado Junior Victor Hugo C. de Albuquerque Luís Fabrício de Freitas Souzal Instituto Federal de Eduçãcao Ciência e Tecnologia do Ceará Fortaleza CE Brazil Laboratório de Inovação em Sistemas de Inteligéncia Artificial (LISIA) Juazeiro do Norte / Fortaleza CE Universidade Federal do Ceará Fortaleza CE Brazil Universidade Federal do Cariri Juazeiro do Norte CE Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Skin cancer is a disease that causes thousands of deaths each year. Early diagnosis and monitoring the progression of the disease are crucial factors for the treatment and health indicators of a society. This study presents an innovative approach for the detection, segmentation, and classification of melanomas in dermoscopic images using advanced computer Vision and Artificial Intelligence (AI) methods. Specifically, it applies Large Language Model (LLM) solutions for pre-diagnosis results through generative AI. This work explores combinations of methods for melanoma detection and segmentation based on the YOLO and SAM architectures, achieving 99% accuracy, surpassing various studies in the literature. The classification phase is based on a pipeline integrating feature extraction and selecting the best combination for melanoma region classification, achieving an accuracy of 86.0%, also outperforming different studies in the literature.

关键词： YOLO image segmentation Accuracy Generative AI Malignant tumors Large language models Transfer learning Pipelines Feature extraction Diseases

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Presenter-Centric image Collection and Annotation: Enhancing Accessibility for the Visually Impaired

Presenter-Centric Image Collection and Annotation: Enhancing...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Luísa Ferreira Daniel Fernandes Fabio Cerqueira Marcos Ribeiro Michel Silva Department of Informatics Universidade Federal de Viçosa - UFV Viçosa Brazil Department of Production Engineering Universidade Federal Fluminense - UFF Petrópolis Brazil

With the recent COVID-19 pandemic, our daily lives and workplace became more dependent on technologies, e.g., the intensive use of video conferences. While a highly connected world enables remote working, it also raises new barriers for an already excluded community, the visually impaired people. Low or no eyesight prevents those people from capturing visual infor-mation, which makes it difficult to understand the overall context of a remote presentation. Most Artificial Intelligence methods are specific to sighted-people data domains due to the scarcity of datasets for the visually impaired. In this paper, we propose an approach to collect data automatically and a protocol to annotate this data specifically for this audience, aiming to support the development of Assistive Technology systems. We demonstrate the viability of the proposed methods by creating a dataset and evaluating its quality, diversity, and representativeness through analytical methods and machine learning models.

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Multispectral image Segmentation With Dimensionality Reduction Using Autoencoders

Multispectral Image Segmentation With Dimensionality Reducti...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Eliton Albuquerque Claudio R. Jung Institute of Informatics Federal University of Rio Grande do Sul

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.

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AMQGaussian: Efficient 3D Gaussian Representation with Asymmetric Mixed-precision Quantization

AMQGaussian: Efficient 3D Gaussian Representation with Asymm...

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International symposium on Parallel and Distributed processing with Applications, ISPA

作者： Yong Kou Qing Ye Yanci Zhang Xia Yuan Jinlong He College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

3D Gaussian Splatting (3DGS) has recently gained increasing attention in novel-view scene synthesis. However, it requires millions of 3D Gaussian spheres to achieve high-quality rendered images, leading to substantial GPU resource demands for training and rendering. This paper has developed an efficient framework to address the challenges faced by 3DGS. 1) We propose an asymmetric mixed-precision quantization strategy to efficiently quantize and dequantize the parameters of 3D Gaussian Spheres (excluding position and opacity) and utilized the Straight Through Estimator method to address the issue of non-backpropagatable gradients post-quantization. This approach significantly reduces GPU memory usage and model storage space. 2) Using statistical methods, we identify optimal gradient thresholds to enhance the densification algorithm of 3D Gaussian spheres, thereby further improving performance. 3) To address the speed bottlenecks in parallel processing of low-precision data using CUDA, we introduce a fast atomic operation method, increasing training speed tenfold. Overall, we validate the effectiveness of our framework across various datasets. It reduces GPU usage by 50% during training, decreases GPU usage threefold during rendering, halves the model storage space and maintains image quality comparable to 3DGS.

关键词： Training image quality Solid modeling Three-dimensional displays Quantization (signal) Statistical analysis graphics processing units Parallel processing Rendering (computer graphics) image reconstruction

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A Study on the Performance of WebAssembly Versus JavaScript in an SVG Editor Environment

A Study on the Performance of WebAssembly Versus JavaScript ...

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Parallel Computing and Distributed Systems (PCDS), International symposium on

作者： Jason Jeremy Wijadi Bryn Ghiffar Ida Bagus Kerthyayana Manuaba Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Scalable Vector graphics (SVG) is an XML-based image format for two-dimensional diagrams. Made up of vectors, SVG enable image and behavior manipulation through a versatile XML-based format that can be created and edited with any text editor and many graphical editing software. With the advent of the web, SVG editing applications have become increasingly available to the public. However, in order to be able to serve users the ability to edit vector graphics in real-time, the performance of the editor plays a significant role in ensuring a smooth user experience. The goal of this study is in comparing the performance of WebAssembly and JavaScript for an SVG editor environment. Our results show that the WebAssembly implementation outperforms JavaScript in terms of execution time and memory usage.

关键词： graphics Parallel processing Vectors User experience Software Real-time systems

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Heuristics to reduce linear combinations of activation functions to improve image classification

Heuristics to reduce linear combinations of activation funct...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Rogério Ferreira de Moraes Raphael dos S. Evangelista Andre Luiz da S. Pereira Yanexis Pupo Toledo Leandro A. F. Fernandes Luis Marti Universidade Federal Fluminense (UFF) Niterói Brazil Inria Chile Research Center Las Condes Chile

image classification is one of the classical problems in computer vision, and CNNs (Convolutional Neural Networks) are widely used for this task. However, the choice of a CNN can vary depending on the chosen dataset. In this context, we have trainable activation functions that are crucial in CNNs and adapt to the data. One technique for constructing these functions is to write them as a linear combination of other activation functions, where the coefficients of this combination are learned during training. However, if we have a large number of activation functions to combine, the computational cost can be very high, and manually testing and choosing these functions may be impractical, depending on the number of available activation functions. To alleviate the difficulty of choosing which activation functions should be part of the linear combination, we propose two heuristics: Linear Combination Approximator by Coefficients (LCAC) and Major and Uniform Coefficient Extractor (MUCE). Our heuristics provide an efficient selection of a subset of activation functions so that their results are better or equivalent to the linear combination that uses all 34 available activation functions in our experiments (C34), considering the image classification problem. Compared to the C34 function, the LCAC function was better or equivalent in 62.5%, and the MUCE function in 87.5% of the conducted experiments.

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A High-Throughput Lossless image Compression Engine Optimized for Compression Ratio

A High-Throughput Lossless Image Compression Engine Optimize...

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IEEE International symposium on Circuits and Systems (ISCAS)

作者： Siqi Cai Yuzhou Chen Wenhui Zhang Zeyuan Jin Gang Wang Hao Chen Guanghui He Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

image compression is an essential technique for graphics processing units to support high-resolution video and high-quality 3D rendering. Many works sacrifice compression ratio (CR) or image quality in order to achieve a higher throughput or lower latency. In this paper, a lossless high-throughput compression-decompression engine optimized for CR is proposed. At the algorithm level, the engine utilizes pixel inter-channel correlation and positional correlation to jointly improve CR. At the hardware level, the diagonal-level parallel decoding (DLPD) is used to obtain a high throughput (44.1 Gbytes/sec). The proposed engine is designed and validated on the JPEG AIC-3 dataset. The average CR on the dataset is 2.23, an improvement of 0.35 over the state-of-the-art high-throughput work with a 24.2 % increase in throughput.

关键词： image quality image coding Correlation Three-dimensional displays Transform coding Throughput Rendering (computer graphics)

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Project "Wall facing Automatic image Identification Laboratory" – W.A.L.(L)

Project "Wall facing Automatic Image Identification Laborato...

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2023 Eurographics Workshop on graphics and Cultural Heritage, GCH 2023

作者： Buscemi, F. Gallo, G. Figuera, M. Atani, Y. Gholizade Lo Duca, A. CNR Institute of Heritage Sciences Catania Italy Dipartimento di Matematica e Informatica Università di Catania Italy CNR Istituto di Informatica e Telematica Pisa Italy

ISBN: (纸本)9783038682172

The advantages of digitalization of cultural heritage artifacts go beyond the basic documentation of the archeological findings contexts and offer a tool to gain new scientific insights. W.A.L.(L) is an on-going project whose results show that the interdisciplinary collaboration between archeologists, data and computer scientists, carried on with rigorous methods of data collection, processing, organization and analysis may offer great opportunities for archeological data sharing and understanding. © ISVR,2023 *** rights reserved

关键词： Data handling

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