A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation

Abstract

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.

Review of wavelet‐based unsupervised texture segmentation, advantage of adaptive wavelets

“…More recently, a parameterless method (i.e. which automatically detects the number of meaningful modes) was proposed in [44] and we will use this approach throughout this paper. Based on this set of boundaries and defining transition areas (illustrated by the shaded areas in Figure 4), the Littlewood-Paley filters are then easily defined in the Fourier domain bŷ…”

Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis

“…In this paper, μ is set to be 3. Then the discrete scale space representation of signal Fourier spectrum is defined as [27] ( ) ( ) ( ) ( )…”

Feature Extraction Enhancement Based on Parameterless Empirical Wavelet Transform: Application to Bearing Fault Diagnosis

“…Pan et al [42] introduced a modified EWT (MEWT) method via data-driven adaptive Fourier spectrum segmentation that is suitable for mechanical fault diagnosis and applied it successfully to bearing fault diagnosis. Gills and Heal [43] presented an improvement of the segmentation procedure using scale space representation (SSR) that enables parameterless empirical wavelet transform (PEWT) procedure. Hence, it can be observed from the presented literature survey that EWT-based procedures show a promising potential in fault diagnosis of rotating machinery.…”