Seed robustness of oriented relative fuzzy connectedness: core computation and its applications

Tavares, Anderson Carlos Moreira; Bejar, Hans Harley Ccacyahuillca; Miranda, Paulo A. V.

doi:10.1117/12.2254646

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…However, in order to generate a more challenging situation, we considered a larger radius of dilation for the external seeds (twice the value of the inner radius), resulting in an asymmetrical arrangement of seeds. In order to show the robustness coefficient, we considered in the evaluation only methods with known procedure to compute their cores: IRFC [23], RFC [24], OIFT [31], ORFC [33], or at least with a good lower bound estimation of their cores: RFC + GC [68], ORFC + GC [33], and ORFC Core + GC [36]. For RFC + GC we considered RC =…”

Section: Resultsmentioning

confidence: 99%

“…• Theoretical robustness analysis [12,13,36,37]: if two seeds separately produce the same result, then they are equivalent, and just one is enough for defining a region. By finding equivalence regions (subsets of equivalent seeds), called cores, and representing each one by just one seed, a compact seed set can be built.…”

Section: Motivationmentioning

confidence: 99%

“…• Theoretical analysis of the Oriented Relative Fuzzy Connectedness (ORFC) robustness and development of the algorithm that computes the ORFC cores, with proof of correctness [36];…”

Section: Goals and Contributionsmentioning

confidence: 99%

“…• Development of the hybrid method ORFC Core + GC, and comparative evaluation with other methods [36] (Chapter 5);…”

Section: Contributionsmentioning

confidence: 99%

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Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness

Tavares¹

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Image segmentation consists on its partition into relevant regions, such as to isolate the pixels belonging to desired objects in the image domain, which is an important step for computer vision, medical image processing, and other applications. Many times automatic segmentation generates results with imperfections. The user can correct them by editing manually, interactively or can simply discard the segmentation and try to automatically generate another result by a different method. Interactive methods combine benefits from manual and automatic ones, reducing user effort and using its high-level knowledge. In seed-based methods, to continue or repair a prior segmentation (presegmentation), avoiding the user to start from scratch, it is necessary to solve the Reverse Interactive Segmentation Problem (RISP), that is, how to automatically estimate the seeds that would generate it. In order to achieve this goal, we first divide the segmented object into its composing cores. Inside a core, two seeds separately always produce the same result, making one redundant. With this, only one seed per core is required. Cores leading to segmentations which are contained in the result of other cores are redundant and can also be discarded, further reducing the seed set, a process called Redundancy Analysis. A minimal set of seeds for presegmentation is generated and the problem of interactive repair can be solved by adding new seeds or removing seeds. Within the framework of the Image-Foresting Transform (IFT), new methods such as Oriented Image-Foresting Transform (OIFT) and Oriented Relative Fuzzy Connectedness (ORFC) were developed. However, there were no known algorithms for computing the core of these methods. This work develops such algorithms, with proof of correctness. The cores also give an indication of the degree of robustness of the methods on the positioning of the seeds. Therefore, a hybrid method that combines GraphCut and the ORFC cores, as well as the Robustness Coefficient (RC), have been developed. In this work, we present another developed solution to repair segmentations, which is based on IFT-SLIC, originally used to generate supervoxels. Experimental results analyze, compare and demonstrate the potential of these solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

“…• Theoretical analysis of the Oriented Relative Fuzzy Connectedness (ORFC) robustness and development of the algorithm that computes the ORFC cores, with proof of correctness [36];…”

Section: Goals and Contributionsmentioning

confidence: 99%

“…• Development of the hybrid method ORFC Core + GC, and comparative evaluation with other methods [36] (Chapter 5);…”

Section: Contributionsmentioning

confidence: 99%

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Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness

Tavares¹

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“…Para obter o próximo melhor corte, por argumentos análogos, poderíamos pensar em repetir o processo e selecionar t = arg max t∈V\{r,t } V (t), mas visto que todos os pixels dentro da região de robustez de t possuem o mesmo valor de energia [Tavares et al (2017a)], isto poderia resultar na seleção de um segundo pixel equivalente ao primeiro e possivelmente sendo seu vizinho.…”

Section: Definiçõesunclassified

Segmentação não supervisionada de imagens via Transformada Imagem-Floresta Orientada sujeita a restrições de alto nível

Bejar

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Seed Robustness of Oriented Image Foresting Transform: Core Computation and the Robustness Coefficient

Tavares

Bejar

Miranda

2017

Lecture Notes in Computer Science

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Seed robustness of oriented relative fuzzy connectedness: core computation and its applications

Cited by 4 publications

References 17 publications

Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness

Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness

Segmentação não supervisionada de imagens via Transformada Imagem-Floresta Orientada sujeita a restrições de alto nível

Seed Robustness of Oriented Image Foresting Transform: Core Computation and the Robustness Coefficient

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