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

作者： Duarte, Kaue T. N. Gobbi, David G. Sidhu, Abhijot S. McCreary, Cheryl R. Saad, Feryal Das, Nita Smith, Eric E. Frayne, Richard Univ Calgary Hotchkiss Brain Inst Radiol & Clin Neurosci Calgary AB T2N IN4 Canada Foothills Med Ctr Calgary Image Proc & Anal Ctr Calgary AB T2N 2T9 Canada Univ Calgary Biomed Engn Grad Program Calgary AB T2N IN4 Canada Foothills Med Ctr Seaman Family MR Res Ctr Calgary AB T2N 2T9 Canada

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

White-matter hyperintensity (WMH) is associated with many disorders where it is suggestive of underlying cerebrovascular, small-vessel disease pathology. However, its role in Alzheimer's disease (AD), mixed, and vascular dementia remains an open area of research. the fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) imaging sequence is commonly used to visualize WMH because it provides good image contrast, not only between WMH and normal tissue, but also between WMH and cerebrospinal fluid. Manual segmentation of WMH lesions in brain volumes, on a slice-by-slice basis, is time-consuming with high inter-rater variability, however, this process remains the broadly accepted gold standard. In this study, variations of 2D, 2.5D and 3D U-shaped convolutional neural networks (U-Net CNNs) were used to perform semantic segmentation on FLAIR images. We evaluated these models in brain volumes obtained from 186 individuals from one of three disease classes: healthy normal (N = 94), mild cognitive impairment (N = 55), and AD (N = 37). Four common architectures (VGG16, VGG19, ResNet and EfficientNetB0) were employed as feature extractors. Results were assessed across the whole brain and by brain region (frontal, occipital, parietal, temporal lobes plus the insula) to identify differences in performance. In general, the predicted WMH volumes had an F-measure score >95% on the test data compared to manual segmentation. this work identified that 2.5D with either VGG16 or VGG19 was the most suitable configuration when segmenting WMH. WMH segmentation performance and measured volume was found to vary between regions and disease classes. U-Net CNN architectures have good performance and may provide valuable insights about the white matter pathology.

关键词： Training Pathology Volume measurement Manuals Brain modeling Feature extraction Convolutional neural networks

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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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Distributed Intelligent Video Surveillance for Early Armed Robbery Detection Based on Deep Learning

Distributed Intelligent Video Surveillance for Early Armed R...

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brazilian symposium on computer graphics and image processing (sibgrapi)

作者： Sergio Fernandez-Testa Edwin Salcedo Centro de Investigación Desarrollo e Innovación en Ingeniería Mecatrónica Universidad Católica Boliviana “San Pablo” La Paz Bolivia

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Low employment rates in Latin America have contributed to a substantial rise in crime, prompting the emergence of new criminal tactics. For instance, “express robbery” has become a common crime committed by armed thieves, in which they drive motorcycles and assault people in public in a matter of seconds. Recent research has approached the problem by embedding weapon detectors in surveillance cameras; however, these systems are prone to false positives if no counterpart confirms the event. In light of this, we present a distributed IoT system that integrates a computer vision pipeline and object detection capabilities into multiple end-devices, constantly monitoring for the presence of firearms and sharp weapons. Once a weapon is detected, the end-device sends a series of frames to a cloud server that implements a 3DCNN to classify the scene as either a robbery or a normal situation, thus minimizing false positives. the deep learning process to train and deploy weapon detection models uses a custom dataset with 16,799 images of firearms and sharp weapons. the best-performing model, YOLOv5s, optimized using TensorRT, achieved a final mAP of 0.87 running at 4.43 FPS. Additionally, the 3DCNN demonstrated 0.88 accuracy in detecting abnormal situations. Extensive experiments validate that the proposed system significantly reduces false positives while autonomously monitoring multiple locations in real-time.

关键词： YOLO Deep learning Training computer vision Accuracy Tensors Weapons Cameras Video surveillance Servers

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A 2D Deep Boltzmann Machine for Robust and Fast Vehicle Classification 30

A 2D Deep Boltzmann Machine for Robust and Fast Vehicle Clas...

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

作者： Silva Santos, Daniel Felipe de Souza, Gustavo Botelho Marana, Aparecido Nilceu Sao Paulo State Univ UNESP Sao Paulo Brazil UFSCar Fed Univ Sao Carlos Sao Carlos SP Brazil

ISBN: (纸本)9781538622193

the visual and automatic classification of vehicles plays an important role in the Transport Area. Besides of security issues, the monitoring of the type of traffic in streets and highways, as well the traffic dynamics over time, allows the optimization of use and of resources related to such public infrastructure. In this work we propose a novel method, called 2D-DBM, for robust and efficient automatic vehicle classification through color images based on a DBM (Deep Boltzmann Machine) combined with bilinear projections. While the DBM training allows a robust initialization of discriminative MLP (Multilayer Perceptron) neural network parameters, the bilinear projection technique can scale down the MLP dimensions, obtaining efficiency while preserving accuracy. the proposed method was assessed on the BIT-Vehicle database, a challenging dataset consisting of frontal images of vehicles collected in a real traffic environment, and compared with a CNN (Convolutional Neural Network) and a traditional DBM (without bilinear projection). the obtained results show that, while keeping the accuracy, the new method significantly reduced the network size and the processing time.

关键词： Vehicle Classification Traffic Control image Analysis Deep Boltzmann Machines Bilinear Projection

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Combining wavelets and watersheds for robust multiscale image segmentation

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image AND VISION COMPUTING 2007年第1期25卷 24-33页

作者： Jung, Claudio Rosito Univ Vale Rio dos Sinos PIPCA Grad Sch Appl Comp BR-93022000 Sao Leopoldo RS Brazil

this paper proposes a new segmentation technique that combines multiresolution wavelet decompositions with the watershed transform. the wavelet transform is applied to the intensity image, producing detail and approximation coefficients. Gradient magnitudes of the approximation image at the coarsest resolution are computed, and an adaptive threshold is used to remove small gradient magnitudes. the watershed transform is then applied, and the segmented image is projected up to higher resolutions using inverse wavelet transforms. Typically, if a low resolution is chosen for the initial segmentation, large relevant objects will be captured;on the other hand, a higher initial resolution will lead to smaller (and more detailed) segmented objects. the low-pass filtering involved in the wavelet decomposition provides robust segmentation results for noisy images, even when the amount of noise is very large. (c) 2006 Elsevier B.V. All rights reserved.

关键词： segmentation watersheds wavelets multiresolution denoising region merging

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A parallel application for 3D reconstruction of coronal loops using image morphing

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image AND VISION COMPUTING 2007年第1期25卷 95-102页

作者： Faria, Lilian N. Mascarenhas, Nelson D. A. Moron, Celio E. Saito, Jos H. Rosa, Reinaldo R. Sawant, Hanumant S. Univ Fed Sao Carlos Dept Comp BR-13565905 Sao Carlos SP Brazil INPE DAS Inst Nacl Pesquisas Espaciais BR-12201970 Sao Jose Dos Campos Brazil INPE LAC Div Astrofis Lab Associado Comp & Matemat Aplicada BR-12201970 Sao Jose Dos Campos Brazil

this paper presents an approach for 3D reconstruction of X-ray tomographic images of coronal loops, observed on the solar atmosphere by the Japanese satellite Yohkoh. In this approach, the intermediate cross-sections images of the magnetic loop are generated with image morphing controlled by a Bezier curve in arc shape. Due to the high computational costs involved in the processing and visualization of solar images, a parallel application for 3D reconstruction of a coronal loop was implemented to execute in the Atlas parallel system. (c) 2006 Elsevier B.V. All rights reserved.

关键词： coronal loops 3D reconstruction image processing image morphing parallel system

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image restoration using digital inpainting and noise removal

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image AND VISION COMPUTING 2007年第1期25卷 61-69页

作者： Barcelos, Celia A. Zorzo Batista, Marcos Aurelio Univ Fed Goias Dept Comp BR-75704020 Catalao Go Brazil Univ Fed Uberlandia Fac Math BR-38400902 Uberlandia MG Brazil

Inpainting and denoising are two important tasks in the field of image processing with broad applications in image and vision analysis. In this paper. we present a new approach for image restoration. Our method simultaneously fills in missing, corrupted, or undesirable information while it removes noise. the denoising is performed by the smoothing equation working inside and outside of the inpainting domain but in completely different ways. Inside the inpainting domain, the smoothing is carried out by the Mean Curvature Flow, while the smoothing of the outside of the inpainting domain is carried out in a way as to encourage smoothing within a region and discourage smoothing across boundaries. Besides smoothing, the approach here presented permits the transportation of available information from the outside towards the inside of the inpainting domain. this combination permits the simultaneous use of filling-in and differentiated smoothing of different regions of an image. the experimental results show the effective performance of the combination of these two procedures in restoring scratched photos, disocclusion (or removal of entire objects from the image) in vision analysis and text removal from images. (c) 2006 Elsevier B.V. All rights reserved.

关键词： inpaint image processing noise removal edge detection diffusion equation transport equation

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Proceedings 16th brazilian symposium on computer graphics and image processing (sibgrapi 2003)

Proceedings 16th Brazilian Symposium on Computer Graphics an...

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brazilian symposium on computer graphics and image processing (sibgrapi)

the following topics are dealt with: computer graphics; image segmentation; geometric modeling; rendering; graphic simulation; image retrieval; image coloring; video summarization; image motion analysis; data visualiz... 详细信息

关键词： Rendering (computer graphics) Computational geometry Visualization Surface fitting image processing Access control

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Fully automatic coloring of grayscale images

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image AND VISION COMPUTING 2007年第1期25卷 50-60页

作者： Vieira, Luiz Filipe M. do Nascimento, Erickson R. Fernandes, Fernando A., Jr. Carceroni, Rodrigo L. Araujo, Arnaldo de A. Univ Fed Minas Gerais Dept Ciencia Comp BR-31270010 Belo Horizonte MG Brazil

this paper introduces a methodology for adding color to grayscale images in a way that is completely automatic. Towards this goal, we build on a technique that was recently developed to transfer colors from a user-selected source image to a target grayscale image. More specifically, in order to eliminate the need for manual selection of the source image, we use content-based image retrieval methods to find suitable source images in an image database. To assess the merit of our methodology, we performed a survey where volunteers were asked to rate the plausibility of the colorings generated automatically for grayscale images. In most cases, automatically-colored images were rated either as totally plausible or as mostly plausible. (c) 2006 Elsevier B.V. All rights reserved.

关键词： image coloring color transfer content-based image retrieval

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Stochastic exploration and active learning for image retrieval

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image AND VISION COMPUTING 2007年第1期25卷 14-23页

作者： Cord, Matthieu Gosselin, Philippe H. Philipp-Foliguet, Sylvie Univ Cergy Pontoise ETIS CNRS UMR 8051 F-95014 Cergy Pontoise France

this paper deals with content-based image retrieval. When the user is looking for large categories, statistical classification techniques are efficient as soon as the training set is large enough. We introduce a two-step-exploration, classification-interactive strategy designed for category retrieval. the first step aims at getting a useful initial training set for the classification step. A stochastic image selection process is used instead of the usual strategy based on a similarity score ranking. this process is dedicated to explore the database in order to collect examples as various as possible of the searched category. the second step aims at providing the best classification between relevant and irrelevant images. Based on SVM, the classification applies an active learning strategy through user interaction. A quality assessment is carried out on the ANN and COREL databases in order to compare and validate our approach. (c) 2006 Elsevier B.V. All rights reserved.

关键词： image processing content-based image retrieval exploration of image database classification active learning

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