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Your search keyword '"de Faria, Sergio M. M."' showing total 48 results
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48 results on '"de Faria, Sergio M. M."'

1. Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering

Author

Pereira, Pedro M. M., Fonseca-Pinto, Rui, Paiva, Rui Pedro, Tavora, Luis M. N., Assuncao, Pedro A. A., and de Faria, Sergio M. M.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing
Abstract

Segmentation is a key stage in dermoscopic image processing, where the accuracy of the border line that defines skin lesions is of utmost importance for subsequent algorithms (e.g., classification) and computer-aided early diagnosis of serious medical conditions. This paper proposes a novel segmentation method based on Local Binary Patterns (LBP), where LBP and K-Means clustering are combined to achieve a detailed delineation in dermoscopic images. In comparison with usual dermatologist-like segmentation (i.e., the available ground-truth), the proposed method is capable of finding more realistic borders of skin lesions, i.e., with much more detail. The results also exhibit reduced variability amongst different performance measures and they are consistent across different images. The proposed method can be applied for cell-based like segmentation adapted to the lesion border growing specificities. Hence, the method is suitable to follow the growth dynamics associated with the lesion border geometry in skin melanocytic images., Comment: submitted to MIPRO DC 2019

Published
2019

5. Efficient Depth-Based Coding

Author

Debono, Carl James, Domański, Marek, de Faria, Sérgio M. M., Klimaszewski, Krzysztof, Lucas, Luís F. R., Rodrigues, Nuno M. M., Wegner, Krzysztof, Assunção, Pedro Amado, editor, and Gotchev, Atanas, editor

Published
2019
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6. Emerging Imaging Technologies: Trends and Challenges

Author

Domański, Marek, Grajek, Tomasz, Conti, Caroline, Debono, Carl James, de Faria, Sérgio M. M., Kovacs, Peter, Lucas, Luís F. R., Nunes, Paulo, Perra, Cristian, Rodrigues, Nuno M. M., Sjöström, Mårten, Soares, Luís Ducla, Stankiewicz, Olgierd, Assunção, Pedro Amado, editor, and Gotchev, Atanas, editor

Published
2019
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7. Optimizing GPU Code for CPU Execution Using OpenCL and Vectorization: A Case Study on Image Coding

Author

Pereira, Pedro M. M., Domingues, Patricio, Rodrigues, Nuno M. M., Falcao, Gabriel, de Faria, Sergio M. M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Carretero, Jesus, editor, Garcia-Blas, Javier, editor, Ko, Ryan K.L., editor, Mueller, Peter, editor, and Nakano, Koji, editor

Published
2016
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8. Optimizing Memory Usage and Accesses on CUDA-Based Recurrent Pattern Matching Image Compression

Author

Domingues, Patricio, Silva, João, Ribeiro, Tiago, Rodrigues, Nuno M. M., De Carvalho, Murilo B., De Faria, Sérgio M. M., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A. C., editor, Torre, Carmelo, editor, Rocha, Jorge Gustavo, editor, Falcão, Maria Irene, editor, Taniar, David, editor, Apduhan, Bernady O., editor, and Gervasi, Osvaldo, editor

Published
2014
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11. H.264/AVC Based Video Coding Using Multiscale Recurrent Patterns: First Results

Author

Rodrigues, Nuno M. M., da Silva, Eduardo A. B., de Carvalho, Murilo B., de Faria, Sérgio M. M., da Silva, Vitor M. M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Atzori, Luigi, editor, Giusto, Daniel D., editor, Leonardi, Riccardo, editor, and Pereira, Fernando, editor

Published
2006
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22. Light Field Image Dataset of Skin Lesions

Author

de Faria, Sergio M. M., primary, Henrique, Martinha, additional, Filipe, Jose N., additional, Pereira, Pedro M. M., additional, Tavora, Luis M. N., additional, Assuncao, Pedro A. A., additional, Santos, Miguel O., additional, Fonseca-Pinto, Rui, additional, Santiago, Felicidade, additional, and Dominguez, Victoria, additional

Published
2019
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37. Compression of touchless multiview fingerprints

Author

Francisco, Nelson C., primary, Zaghetto, Alexandre, additional, Macchiavello, Bruno, additional, da Silva, Eduardo A. B., additional, Lima-Marques, Mamede, additional, Rodrigues, Nuno M. M., additional, and de Faria, Sergio M. M., additional

Published
2011
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44. Lost block reconstruction in depth maps using color image contours.

Author

Marcelino, S., Assuncao, P., de Faria, Sergio M. M., and Soares, S.

Abstract

This paper presents a method to recover lost blocks in depth maps affected by data loss in 3D image/video communications over error prone networks. The proposed method relies on the color image for accurate reconstruction of the lost contour segments in the corresponding depth map areas. Such reconstructed depth map contours are then used as boundaries at different depth planes to recover the missing depth values through weighted interpolation. The method performance is evaluated by the objective quality (PSNR) of the synthesised views. Images decoded with the reconstructed depth maps are compared with those of the reference method. The proposed method exhibits PSNR gains up to 1.49dB higher than the reference one, and a better performance is consistently achieved for different 3D image content. [ABSTRACT FROM PUBLISHER]

Published
2012
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48. Light Field Image Dataset of Skin Lesions.

Author

de Faria SMM, Henrique M, Filipe JN, Pereira PMM, Tavora LMN, Assuncao PAA, Santos MO, Fonseca-Pinto R, Santiago F, and Dominguez V

Subjects
Algorithms, Humans, Dermoscopy methods, Machine Learning, Skin Diseases diagnostic imaging
Abstract

Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches. This paper presents a contribution for the research community, in the form of a publicly available light field image dataset of skin lesions (named SKINL2 v1.0). This dataset contains 250 light fields, captured with a focused plenoptic camera and classified into eight clinical categories, according to the type of lesion. Each light field is comprised of 81 different views of the same lesion. The database also includes the dermatoscopic image of each lesion. A representative subset of 17 central view images of the light fields is further characterised in terms of spatial information (SI), colourfulness (CF) and compressibility. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies.

Published
2019
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