A Novel Multiscale Edge Detection Approach Based on Nonsubsampled Contourlet Transform and Edge Tracking

Abstract

Edge detection is a fundamental task in many computer vision applications. In this paper, we propose a novel multiscale edge detection approach based on the nonsubsampled contourlet transform (NSCT): a fully shift-invariant, multiscale, and multidirection transform. Indeed, unlike traditional wavelets, contourlets have the ability to fully capture directional and other geometrical features for images with edges. Firstly, compute the NSCT of the input image. Secondly, theK-means clustering algorithm is applied to each level of the NSCT for distinguishing noises from edges. Thirdly, we select the edge point candidates of the input image by identifying the NSCT modulus maximum at each scale. Finally, the edge tracking algorithm from coarser to finer is proposed to improve robustness against spurious responses and accuracy in the location of the edges. Experimental results show that the proposed method achieves better edge detection performance compared with the typical methods. Furthermore, the proposed method also works well for noisy images.

Colour image encryption technique using differential evolution in non‐subsampled contourlet transform domain

“…3a. An efficient reconstruction condition can be defined as [45] H 0 (z)G 0 (z) + H 1 (z)G 1 (z) = 1. Here, G 0 (z) and G 1 (z) represent low-and high-pass reconstruction filters, respectively.…”

“…NSCT is designed using non‐subsampled pyramids (NSP) and non‐subsampled DFBs to achieve shift‐invariant feature (see Fig. 2) [45].…”

“…Time series plot of sequence z Structure of non-sampled filter bank (NSFB) (a) NSFB structure that implements the NSCT, (b) Idealised frequency partitioning obtained with NSFB structure[45] …”

Partial pose estimation of 3D rigid object system using outer box method

“…Previous study presented various techniques on CAD image database development [5] using .stl data, image data acquisition for surface inspection [6], pre-processing techniques on image registration [7][8][9][10][11][12][13][14][15][16][17][18], features extraction such as Vote-based 3D shape recognition and registration [19], edges extraction [20], reflection symmetric [21], DoG-based detector presented by deriving scale-invariant mesh features for image registration [22], Local Procustes Regression (LPR) [23], Estimation-by-Completion (EbC) [24] and Customized three-dimensional template matching [25][26][27] In this paper, the study focus on object description, scaling and image registration method. The contribution of this paper two fold.…”

An Improved NMS-Based Adaptive Edge Detection Method and Its FPGA Implementation

“…Due to the broad application fields, such as in industry, spaceflight, medicine, and military [1][2][3], the study on edge detection became a hot issue in image processing. At present, the researches on edge detection are mainly focused on mathematical morphology methods [4,5] and gradient methods [6,7]. The morphology based edge detection methods can effectively detect edges; however, complex iterative operations in these methods are the main obstacle for real-time applications.…”