Person:
Antunes dos Santos, Felipe

Loading...
Profile Picture

Email Address

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Antunes dos Santos

First Name

Felipe

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

ORCID

0000-0003-3298-9215

person.page.upna

812045

Name

Search Results

Now showing 1 - 2 of 2
  • PublicationOpen Access
    A framework for active contour initialization with application to liver segmentation in MRI
    (Springer, 2022) Mir Torres, Arnau; Antunes dos Santos, Felipe; Fernández Fernández, Francisco Javier; López Molina, Carlos; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Object segmentation is a prominent low-level task in image processing and computer vision. A technique of special relevance within segmentation algorithms is active contour modeling. An active contour is a closed contour on an image which can be evolved to progressively fit the silhouette of certain area or object. Active contours shall be initialized as a closed contour at some position of the image, further evolving to precisely fit to the silhouette of the object of interest. While the evolution of the contour has been deeply studied in literature [5, 11], the study of strategies to define the initial location of the contour is rather absent from it. Typically, such contour is created as a small closed curve around an inner position in the object. However, literature contains no general-purpose algorithms to determine those inner positions, or to quantify their fitness. In fact, such points are frequently set manually by human experts, hence turning the segmentation process into a semi-supervised one. In this work, we present a method to find inner points in relevant object using spatial-tonal fuzzy clustering. Our proposal intends to detect dominant clusters of bright pixels, which are further used to identify candidate points or regions around which active contours can be initialized.
  • PublicationOpen Access
    Content-aware image smoothing based on fuzzy clustering
    (Springer, 2022) Antunes dos Santos, Felipe; López Molina, Carlos; Mir Fuentes, Arnau; Mendióroz Iriarte, Maite; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    Literature contains a large variety of content-aware smoothing methods. As opposed to classical smoothing methods, content-aware ones intend to regularize the image while avoiding the loss of relevant visual information. In this work, we propose a novel approach to contentaware image smoothing based on fuzzy clustering, specifically the Spatial Fuzzy c-Means (SFCM) algorithm. We develop the proposal and put it to the test in the context of automatic analysis of immunohistochemistry imagery for neural tissue analysis.