Making Medical Visual Information Available on the WEB

This paper presents a new metadata model to describe and retrieve medical visual information, such images and their diagnoses, using the Web. The classes of this model allow describing medical images of different medical areas, including their properties, components and relationships. This model supports the international classification of diseases and related health problems (i.e. ICD-10) [1]. The MedISeek (Medical Image Seek) prototype presented here proposes a medical image sharing system based on web services, that allows authorized users to describe, store and retrieve medical images and their associated diagnostic information,based on the proposed metadata model. Thus, this paper proposes to include the image description, converted to RDF syntax, into a JPEG image and a persistent structure for relational databases to storage and retrieve this metadata, providing fast indexing and querying. A description of the prototype structure also is provided

Efficient and Scalable Image Segmentation Using Bag-of-Features and Stochastic Region Merging

This work presents an efficient and scalable texture segmentationalgorithm based on bag-of-features and stochastic region merging.The image is partitioned into blocks and processed independentlyto obtain regions, which are then merged to obtain the finalsegmentation. Experimental results shows the proposed methodachieves an overall speed improvement of at least 4.5x and requires6.5x less memory, while still improving segmentation accuracyfor large images

Sustainable withdrawal strategies for Brazilian REITs portfolios

This work compares sustainable cash withdrawal strategies for Brazilian REITs (Real Estate Investment Trusts) portfolios. It is assumed that cash withdrawals must be maintained for an indefinite period of time, while the capital invested in the REITs portfolio is adjusted for inflation. For simplicity, the capital is assumed to be invested in equal proportions in the REITs of the portfolio. The REITs market is relatively new in Brazil, and this study illustrates the performance of three strategies generating sustainable and steady cash flows for the investor based on a sample of Brazilian REITs. These REITs have publicly available data and have been operating continuously over the past 10 years. The findings of the study reveal that sustainable cash withdrawal rates are feasible for Brazilian REITs portfolios, however, a significant variability of cash withdrawal rates takes place among the individual portfolio REITs. The experimental results obtained for an equal-weight Brazilian REITs portfolio suggest that the three strategies investigated potentially can provide total annual returns that vary between 3.32\% and 3.38\% above the annual inflation and are sustainable during the period analyzed. This study also suggests that sustainable withdrawal for REITs portfolios is related to the cash flow risk faced by the individual REITs in the portfolio

Scalable image segmentation via decoupled sub-graph compression

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.patcog.2017.11.028 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Dealing with large images is an on-going challenge in image segmentation, where many of the current methods run into computational and/or memory complexity issues. This work presents a novel decoupled sub-graph compression (DSC) approach for efficient and scalable image segmentation. In DSC, the image is modeled as a region graph, which is then decoupled into small sub-graphs. The sub-graphs undergo a compression process, which simplifies the graph, reducing the number of vertices and edges, while keeping the overall graph structure. Finally, the compressed sub-graphs are re-coupled and re-compressed to form a final compressed graph representing the final image segmentation. Experimental results based on a dataset of high resolution images (1000 × 1500) show that the DSC method achieves better segmentation performance when compared to state-of-the-art segmentation methods (PRI=0.84 and F=0.61), while having significantly lower computational and memory complexity

Some Information Geometric Aspects of Cyber Security by Face Recognition

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-29, pub-electronic 2021-07-09Publication status: PublishedSecure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods

Representação e Classificação de Texturas da Íris Baseada na Transformada Ótima de Gabor

This paper proposes to investigate the application of the method developed by Manjunah and Ma [12], namely, the Optimal Gabor Wavelet Transform (OGWT), in the context of iris texture representation. The proposed method was tested in a widely known database of 1205 eye images [16]. In each one of these images, the iris region was segmented, and then represented in multiple scales using the OGWT; the íris texture patterns were represented by their statistics in the Wavelet domain, and compared using similarity metrics. The experimental results indicate that the proposed method obtains a correct iris recognition rate of 94,68%, even considering out of focus images and iris occlusions; a correct iris recognition rate of 100% is obtained excluding problematic images. The proposed method is flexible, and allows to fine tune the iris recognition criterion according to the accuracy level required by the application.Este trabalho propõe a aplicação e investigação do método apresentado por Manjunah e Ma [12] para análise de texturas e denominado de Transformada Wavelet Gabor Ótima (TWGO) na representação e discriminação de texturas da íris. O método proposto foi testado usando um banco de 1205 imagens de olhos referenciado na literatura [16]. Em cada uma destas imagens, a região da íris foi segmentada e após, representada em múltiplas escalas segundo a TWGO; os padrões de textura são então representados por estatísticas, e comparados entre si através de medidas de similaridade para fazer a identificação da íris. Os resultados de diversos testes são apresentados, e indicam que o método proposto produz elevadas taxas de acerto no reconhecimento da íris (94,68% de acerto considerando imagens fora de foco e com oclusões da íris, e 100% excluindo estas imagens problemáticas). O método proposto permite ajustar a sensibilidade do critério de similaridade entre texturas de acordo com o nível de segurança requerido pela aplicação. <!-- /* Font Definitions */ @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-1610611985 1107304683 0 0 159 0;} @font-face {font-family:Times; panose-1:2 2 6 3 5 4 5 2 3 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536859921 -1073711039 9 0 511 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; text-align:justify; text-indent:11.35pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times","serif"; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} p.abstract, li.abstract, div.abstract {mso-style-name:abstract; mso-style-unhide:no; mso-style-next:Normal; margin-top:30.0pt; margin-right:1.0cm; margin-bottom:6.0pt; margin-left:1.0cm; text-align:justify; mso-pagination:widow-orphan; font-size:9.0pt; mso-bidi-font-size:10.0pt; font-family:"Times","serif"; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} .MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> <!-- /* Font Definitions */ @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-1610611985 1107304683 0 0 159 0;} @font-face {font-family:Times; panose-1:2 2 6 3 5 4 5 2 3 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536859921 -1073711039 9 0 511 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; text-align:justify; text-indent:11.35pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times","serif"; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} p.abstract, li.abstract, div.abstract {mso-style-name:abstract; mso-style-unhide:no; mso-style-next:Normal; margin-top:30.0pt; margin-right:1.0cm; margin-bottom:6.0pt; margin-left:1.0cm; text-align:justify; mso-pagination:widow-orphan; font-size:9.0pt; mso-bidi-font-size:10.0pt; font-family:"Times","serif"; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} .MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 3.0cm 70.85pt 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;}&nbsp

Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour

This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs

Histeroscopia digital : um novo método diagnóstico do endométrio secretor

06

"A Nova Eletricidade: Aplica\c{c}\~oes, Riscos e Tend\^encias da IA Moderna -- "The New Electricity": Applications, Risks, and Trends in Current AI

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity. We analyze the risks that may come along with rapid technological progress and future trends in AI, an area that is on the path to becoming a general-purpose technology, just like electricity, which revolutionized society in the 19th and 20th centuries. A provocativa compara\c{c}\~ao entre IA e eletricidade, feita pelo cientista da computa\c{c}\~ao e empreendedor Andrew Ng, resume a profunda transforma\c{c}\~ao que os recentes avan\c{c}os em Intelig\^encia Artificial (IA) t\^em desencadeado no mundo. Este cap\'itulo apresenta uma vis\~ao geral pela paisagem em constante evolu\c{c}\~ao da IA. Sem pretens\~oes de exaurir o assunto, exploramos as aplica\c{c}\~oes que est\~ao redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos que acompanham o r\'apido progresso tecnol\'ogico e as tend\^encias futuras da IA, \'area que trilha o caminho para se tornar uma tecnologia de prop\'osito geral, assim como a eletricidade, que revolucionou a sociedade dos s\'eculos XIX e XX.Comment: In Portugues