Changes between Version 20 and Version 21 of GSoC/2017

Feb 3, 2017, 1:25:45 AM (4 years ago)



  • GSoC/2017

    v20 v21  
    224224* Co-mentors: Māris Nartišs, ?
     226=== Implement cutline generation ===
     228Finding 'meaningful' cutline in an scene could be very interesting for tiling images and process segmentation in parallel. Instead of using arbitrary linear tiling, the segmented tiles could then be merged without post-processing of segments touching the tile's border. The proposition is to implement a method described in [1] for cutline generation (see [2] for more references).
     229The procedure is composed of the following main steps:
     2301) Apply a High-pass filter - the "Prewitt South" filter ( to produce an edge image;
     2312) Apply a low-pass filter (mean for example) on the previous result;
     2323) Find the starting and finishing points (just based on pixel values in the first/last column/row);
     2334) Create an adjacency matrix on the result of 2) and use weights to give higher costs for specific directions (and force the cutline in to be quite linear between starting and finishing points);
     2345) Use the Dijkstra's algorithm ('s_algorithm) to find the optimal cutline with a lowest cost between the starting and finishing point
     236If this algorithm would be implemented during the GSoC, it could be further adapted for huge enhancement of i.image.mosaic to enable mosaicking of adjacent scenes using cutline and become a real alternative to proprietary software ( As the proposed solution for cutline is working on a single image, It should be adapted to take into account the pixels values of two images so.
     238[1] Soares, Anderson Reis, Thales Sehn Körting, et Leila Maria Garcia Fonseca. 2016. « Improvements of the divide and segment method for parallel image segmentation ». Brazilian Journal of Cartography 68 (6).
     239[2] Körting, Thales Sehn, Emiliano Ferreira Castejon, et Leila Maria Garcia Fonseca. 2013. « The Divide and Segment Method for Parallel Image Segmentation ». In Advanced Concepts for Intelligent Vision Systems, 504 15. Springer, Cham. doi:10.1007/978-3-319-02895-8_45.
     241* Language requirements: C/C++ ?
     242* Mentor: ?
     243* With support from: Taïs Grippa
    226245== Tips for students ==