Interactive segmentation of non-star-shaped contours by dynamic programming

Jiang, Xiaoyi; Groíe, Andree; Rothaus, Kai

doi:10.1016/j.patcog.2011.03.010

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…The two most common techniques are discussed in 3.2 , 3.4 . Another way of DAG creation is performed by the RACK algorithm proposed by Jiang et al [50] . In this work a DAG is built based on some skeleton points, set by the user, depicting the skeleton of an object of interest.…”

Section: Problems and Solutionsmentioning

confidence: 99%

Dynamic Programming Based Segmentation in Biomedical Imaging

Ungru

Jiang

2017

Computational and Structural Biotechnology Journal

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Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedical images often suffer from noisy data and fuzzy edges. Therefore, there is a need for robust methods for contour and line detection. Dynamic programming is a popular technique that satisfies these requirements in many ways. This work gives a brief overview over approaches and applications that utilize dynamic programming to solve problems in the challenging field of biomedical imaging.

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Section: Problems and Solutionsmentioning

confidence: 99%

Dynamic Programming Based Segmentation in Biomedical Imaging

Ungru

Jiang

2017

Computational and Structural Biotechnology Journal

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“…The other part-based models improve the performance of detection by adding the spatial relationship among the parts. Especially, the pictorial structure model and the -fan model perform fast detection via dynamic programming [20] , [21] , since the appearance of generalized distance transform (GDT) [22] greatly reduces the time complexity of the dynamic programming. However, they does not allow cycles in the spatial relationship [20] .…”

Section: Introductionmentioning

confidence: 99%

A Part-Based Probabilistic Model for Object Detection with Occlusion

Zhang

Zhao

et al. 2014

PLoS ONE

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The part-based method has been a fast rising framework for object detection. It is attracting more and more attention for its detection precision and partial robustness to the occlusion. However, little research has been focused on the problem of occlusion overlapping of the part regions, which can reduce the performance of the system. This paper proposes a part-based probabilistic model and the corresponding inference algorithm for the problem of the part occlusion. The model is based on the Bayesian theory integrally and aims to be robust to the large occlusion. In the stage of the model construction, all of the parts constitute the vertex set of a fully connected graph, and a binary variable is assigned to each part to indicate its occlusion status. In addition, we introduce a penalty term to regularize the argument space of the objective function. Thus, the part detection is formulated as an optimization problem, which is divided into two alternative procedures: the outer inference and the inner inference. A stochastic tentative method is employed in the outer inference to determine the occlusion status for each part. In the inner inference, the gradient descent algorithm is employed to find the optimal positions of the parts, in term of the current occlusion status. Experiments were carried out on the Caltech database. The results demonstrated that the proposed method achieves a strong robustness to the occlusion.

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