Pseudo Edges Research Articles

Edge detection algorithm based on valley lines and watershed

The purpose of edge detection algorithm is to highlight the edge in the image, which is also the premise and key of image segmentation. However, the traditional edge detection algorithm will amplify the influence of noise in the process of derivative operation, and the noisy edges will often appear, which is not conducive to the accurate extraction of edge. To solve this problem, we propose an edge detection algorithm combining valley lines and watershed algorithm. In this algorithm, the bilateral filter is used to replace the Gaussian filter to achieve edge preserving and denoising. The rewritten Canny operator is used to calculate the low value points and get the valley lines of the image. This process can achieve the first denoising. Then, the valley lines are used to replace the discrete points of water injection and the watershed algorithm is used to calculate the segmentation results of the image. At this time, there are many pseudo edges around the main boundary, and the pseudo edges are deleted by setting the threshold, so as to achieve the effect of secondary denoising. The experimental results show that our method has good performance in noise resistance and edge continuity compared with other and traditional edge detection algorithms.

Robust Edge Detection Based on Anisotropic Mathematical Morphology and Scale Multiplication in NSCT Domain

A novel edge detection method based on anisotropic mathematical morphology and scale multiplication in nonsubsampled contourlet transform (NSCT) domain is proposed to obtain a superior and robust performance under heavy noise. One preliminary result is obtained using anisotropic morphological gradient of the low-frequency component, yielding a single-pixel response with few pseudo edges. Due to the great ability of NSCT to localize distributed discontinuities such as edges, scale multiplication results of high-frequency components can get rid of a large amount of noise and produce well-localized edge candidates. The final result is a fusion of the detection results of low-frequency component and high-frequency components. Detailed experiments compared with other state-of-the-art methods demonstrate that the proposed method has a superior performance of edge detection and is quite robust even under heavy noise.

A Novel Edge Detection Approach Based on Soft Morphological Operations

Edge detection and target segmentation is difficult due to noise existing in an image. A novel edge detection method is proposed based on soft morphological operations in this paper. Because soft morphological operations can remove noise while preserving image details, which can be used to construct morphological edge detection operators with high robustness and better edge effect. Experimental results show that, comparing with the existing edge detection operators, the novel edge detection method can get better edge effect while removing pseudo edges.

Extended shapes for the combinatorial design of RNA sequences

It is known that for two given secondary structures (defined by position of base pairings) an RNA string can easily be found that can fold into both structures. For more than two secondary structures this is not necessarily possible. In this paper, we introduce pseudo edges that are used to forbid that certain base pairs can bind and therefore can be used to define the properties of possible RNA secondary structures. We study the complexity of the problem to design an RNA sequence that can fold into different secondary structures each of them is described by a set of required and forbidden base pairs. We refine the NP-completeness results of Clote et al. (2005) and show an analogous NP-completeness result for the realisation problem concerning the removal of (pseudo) edges. We also present a polynomial time method for checking the realisability of extended shape graphs. Furthermore, we empirically analyse the influence of pseudo edges on the realisability for sets of random RNA sequences and for sets of aptamers.