@InProceedings{AlmeidaPaArKiMaGu:2021:DeImGr,
               author = "Almeida, Raquel and Patroc{\'{\i}}nio Junior, Zenilton K. G. and 
                         Ara{\'u}jo, Arnaldo de Albuquerque and Kijak, Ewa and Malinowski, 
                         Simon and Guimar{\~a}es, Silvio Jamil F.",
          affiliation = "PUC Minas and Universit{\'e} de Rennes 1, Brazil and France and 
                         PUC Minas, Belo Horizonte, Brazil and Universidade Federal de 
                         Minas Gerais, Belo Horizonte, Brazil and Universit{\'e} de Rennes 
                         1, Rennes, France and Universit{\'e} de Rennes 1, Rennes, France 
                         and PUC Minas, Belo Horizonte, Brazil",
                title = "Descriptive Image Gradient from Edge-Weighted Image Graph and 
                         Random Forests",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image gradient, Random forest, graph, segmentation.",
             abstract = "Creating an image gradient is a transformation process that aims 
                         to enhance desirable properties of an image, whilst leaving aside 
                         noise and non-descriptive characteristics. Many algorithms in 
                         image processing rely on a good image gradient to perform properly 
                         on tasks such as edge detection and segmentation. In this work, we 
                         propose a novel method to create a very descriptive image gradient 
                         using edge-weighted graphs as a structured input for the random 
                         forest algorithm. On the one side, the spatial connectivity of the 
                         image pixels gives us a structured representation of a grid graph, 
                         creating a particular transformed space close to the spatial 
                         domain of the images, but strengthened with relational aspects. On 
                         the other side, random forest is a fast, simple and scalable 
                         machine learning method, suited to work with high-dimensional and 
                         small samples of data. The local variation representation of the 
                         edge-weighted graph, aggregated with the random forest implicit 
                         regularization process, serves as a gradient operator delimited by 
                         the graph adjacency relation in which noises are mitigated and 
                         desirable characteristics reinforced. In this work, we discuss the 
                         graph structure, machine learning on graphs and the random forest 
                         operating on graphs for image processing. We tested the created 
                         gradients on the hierarchical watershed algorithm, a segmentation 
                         method that is dependent on the input gradient. The segmentation 
                         results obtained from the proposed method demonstrated to be 
                         superior compared to other popular gradients methods.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00053",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00053",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CU3E2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CU3E2",
           targetfile = "PaperID31.pdf",
        urlaccessdate = "2024, May 06"
}