On the geodesic paths approach to color image filtering

Szczepański, Marek; Smołka, Bogdan; Plataniotis, K.N.; Venetsanopoulos, A.N.

doi:10.1016/s0165-1684(03)00058-6

Cited by 40 publications

(35 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was firstly introduced to computer vision and image processing for texture discrimination in [24], and later it has been applied to other problems in image processing such as image enhancement [25], image filtering [26], and image segmentation [27]. In geometry processing, it was firstly used for mesh denoising by Sun et al in [28], and then it appears in application of mesh segmentation in [29].…”

Section: B Fcm and Random Walksmentioning

confidence: 99%

3D Shapes Co-segmentation by Combining Fuzzy C-Means with Random Walks

Zhang

Sun

Song

et al. 2013

2013 International Conference on Computer-Aided Design and Computer Graphics

View full text Add to dashboard Cite

Co-segmentation of 3D shapes has been receiving increasing attention, and treated as clustering problem in a descriptor space by a few unsupervised approaches to achieve proper co-segmentation of shapes with large variability. However, most of the existing algorithms are performed on segment level and heavily dependent on the per-object segmentation. Accordingly, we propose a co-segmentation method based on combination of Fuzzy C-Means (FCM) and Random Walks together. The novelty of our method is twofold. As an efficient soft clustering algorithm, FCM is firstly used to cluster directly all the facets in the set in terms of their shape descriptors. The clusters of facets are created as candidates of the consistent parts of shapes. Random Walks model is then incorporated into the iterations of FCM clustering to adjust the assignment of facets in each candidate according to the minima rule of shape segmentation. The results of co-segmentation are refined through the iterations of FCM until its convergence conditions are satisfied. Experiments prove that the method proposed in this paper can not only get more stable results without per-object segmentation, but also improve the accuracy of cosegmentation.

show abstract

Section: B Fcm and Random Walksmentioning

confidence: 99%

3D Shapes Co-segmentation by Combining Fuzzy C-Means with Random Walks

Zhang

Sun

Song

et al. 2013

2013 International Conference on Computer-Aided Design and Computer Graphics

View full text Add to dashboard Cite

show abstract

“…The first work using random walks in computer vision is in the application of texture discrimination [3], and recently has been applied to image segmentation [4]. In the field of image processing, random walk has been used to image enhancement [5] and filtering [6]. The use of random walks in geometry processing was recently proposed by Sun et al in [7], [8] for mesh denoising, and after that it appears an application to mesh segmentation in [9].…”

Section: Previous and Related Workmentioning

confidence: 99%

Random Walks for Vector Field Denoising

Paixão

Lage

Petronetto

et al. 2009

2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

View full text Add to dashboard Cite

Figure 1. A simple discontinuous vector field (left) pertubed with a gaussian additive noise (middle left). The gaussian filter (middle right) blurs the interface, while the random walk (right) preserves it.Abstract-In recent years, several devices allow to directly measure real vector fields, leading to a better understanding of fundamental phenomena such as fluid simulation or brain water movement. This turns vector field visualization and analysis important tools for many applications in engineering and in medicine. However, real data is generally corrupted by noise, puzzling the understanding provided by those tools. Those tools thus need a denoising step as preprocessing, although usual denoising removes discontinuities, which are fundamental for vector field analysis. This paper proposes a novel method for vector field denoising based on random walks which preserve those discontinuities. It works in a meshless setting; it is fast, simple to implement, and shows a better performance than the traditional gaussian denoising technique.

show abstract

“…For a given path of length η, the number of simple paths Ω can be easily computed. Table 1.1 depicts the number of possible paths corresponding to the DPA and FDPA filters, [115,116,106,104]. The complexity of the DPA and FDPA filters can be determined as follows, [115,116] Thus the total number of operations needed to implement the filters is…”

Section: Computational Complexity and Fast Filter Designmentioning

confidence: 99%