Person:
Flores Vidal, Pablo Arcadio

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First Name
Pablo Arcadio
Last Name
Flores Vidal
URI
https://hdl.handle.net/20.500.14352/80733
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
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Author: Villarino, Guillermo × Item Type: Publication ×

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Now showing 1 - 2 of 2
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    A new approach to Color Edge Detection
    (Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), 2019) Flores Vidal, Pablo Arcadio; Gómez González, Daniel; Castro Cantalejo, Javier; Villarino, Guillermo; Montero, Javier
    Most edge detection algorithms deal only with grayscale images, and the way of adapting them to use with RGB images is an open problem. In this work, we explore different ways of aggregating the color information of a RGB image for edges extraction, and this is made by means of well-known edge detection algorithms. In this research, it is been used the set of images from Berkeley. In order to evaluate the algorithm’s performance, F measure is computed. The way that color information -the different channels- is aggregated is proved to be relevant for the edge detection task. Moreover, post-aggregation of channels performed significatively better than the classic approach (pre-aggregation of channels).
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    A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
    (International Journal of Computational Intelligence Systems, 2019) Flores Vidal, Pablo Arcadio; Villarino, Guillermo; Gómez, Daniel; Montero, Javier
    Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approach based on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.