@InProceedings{DiazQuJiBeHaFePl:2015:FrSoAu,
               author = "Diaz, Pedro Marco Achanccaray and Quirita, Victor Andres Ayma and 
                         Jimenez, Luis Ignacio and Bernabe, Sergio and Happ, Patrick Nigri 
                         and Feitosa, Raul Queiroz and Plaza, Antonio",
                title = "SPT 3.0: A free software for automatic segmentation parameters 
                         tuning",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5578--5581",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper presents a free software tool, named Segmentation 
                         Parameter Tuner 3 (SPT 3.0), designed for automatic tuning of 
                         segmentation parameters based on a number of optimization 
                         algorithms using different quality metrics as fitness functions. 
                         For a segmentation algorithm to produce segments that correspond 
                         in some way to meaningful image objects, its parameters must be 
                         properly tuned. Conventionally, it involves a long time consuming 
                         series of trials-and-errors. Some initiatives towards designing 
                         methods for automatic segmentation parameter tuning rely on a 
                         stochastic optimization method. Basically, it searches the 
                         parameter space for the values that maximize the level of 
                         agreement between a set of reference segments, which are 
                         delineated manually by a human operator, and the segmentation 
                         outcome. This level of agreement is quantified by a metric which 
                         compares the segmentation outcome with the reference segments 
                         given by the user. As our target is to maximize the level of 
                         agreement represent by this metric, it becomes an optimization 
                         problem where the metric would be the fitness function. In this 
                         version, SPT 3.0 offers many features such as: six segmentation 
                         algorithms, which are able to work with Optical, Hyperspectral 
                         and/or Synthetic Aperture Data (SAR) images (including parallel 
                         GPU-based implementations for two of them), four alternative 
                         optimization methods (Differential Evolution, Nelder-Mead, among 
                         others) and seven different fitness functions (Hoover Index, Shape 
                         Index, among others) are available, which assess the segmentation 
                         outcome.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1127",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4ECR",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4ECR",
           targetfile = "p1127.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "09 maio 2024"
}