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| Author(s): |
Bruno Klava
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Paulo. |
| Institution: | Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) |
| Defense date: | 2014-10-08 |
| Examining board members: |
Nina Sumiko Tomita Hirata;
Roberto de Alencar Lotufo;
Paulo Andre Vechiatto de Miranda;
Carlos Hitoshi Morimoto;
Alexandre Noma
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| Advisor: | Nina Sumiko Tomita Hirata |
| Abstract | |
Segmentation is an important step in nearly all tasks involving digital image processing. Due to the variety of images and segmentation needs, automation of segmentation is not a trivial task. In many situations, interactive approaches in which the user can intervene to guide the segmentation process, are quite useful. Watershed transformation based approaches are suitable for interactive image segmentation: the watershed from markers allows the user to mark the regions of interest in the image; the hierarchical watershed generates a hierarchy of partitions of the image being analyzed, hierarchy in which the user can easily navigate and select a particular partition (segmentation). In a previous work, we have proposed a method that integrates the two approaches so that the user can combine the strong points of these two forms of interaction interchangeably. Despite the versatility obtained by integrating the two approaches, the built hierarchies hardly contain interesting partitions and the interaction effort needed to obtain a desired outcome can be very high. In this thesis we propose a method, based on machine learning, that uses images previously segmented to try to adapt a given hierarchy so that it contains partitions closer to the partition of interest. In the machine learning formulation, different image features are associated to the possible region contours, and these are classified as ones that must or must not be present in the final partition by a previously trained support vector machine. The given hierarchy is adapted to contain a partition that is consistent with the obtained classification. This approach is particularly interesting in scenarios where batches of similar images or sequences of images, such as frames in video sequences or cuts produced by imaging diagnosis procedures, need to be segmented. In such cases, it is expected that for each new image to be segmented, the interaction effort required to achieve the desired segmentation is reduced relative to the effort that would be required when using the original hierarchy. In order to do not depend on experiments with users in assessing the reduction in interaction effort, we propose and use an interaction model that simulates human users in the context of hierarchical segmentation. Simulations of this model were compared with interaction sequences observed in experiments with humans users. Experiments with different bacthes and image sequences show that the method is able to reduce the interaction effort. (AU) | |
| FAPESP's process: | 09/16852-0 - Image partition hierarchies construction integrating machine learning techniques to the hierarchical watershed |
| Grantee: | Bruno Klava |
| Support Opportunities: | Scholarships in Brazil - Doctorate |