Anchoring Bolt Detection Based on Morphological Filtering and Variational Modal Decomposition

The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the variational modal decomposition(VMD)method is introduced into the bolt detection signal analysis.On the basis of morphological filtering(MF)and the VMD method,a VMD?combined MF principle is established into a bolt detection signal analysis method(MF?VMD).MF?VMD is used to analyze the vibration and actual bolt detection signals of the simulation.Results show that MF?VMD effectively separates intrinsic mode function,even under strong interference.In comparison with conventional VMD method,the proposed method can remove noise interference.An intrinsic mode function of the field detection signal can be effectively identified by reflecting the signal at the bottom of the bolt.

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.

Extracting Moving Objects Based on Morphological Motion Filter

This paper presents a new approach to the extraction of a moving object from video sequence. The method is based on morphological motion filter using connected operator and a proposed new filtering criterion. The morphological motion filter aims to detect motion which is distinct from that of the background, and thereby locates independently moving physical objects in the scenes. Experiments show that the algorithm can extract object from moving backgrounds efficiently.

Some Problems of 2D Morphological and Median Filters

In this paper we mainly discussed some problems of 2D morpnological and median filters .The differences between 1D and 2D morphological and median filters arc also described. It can be seen that many propcrties of ID finers arc invalid for 2D filters. Som cxamples and cxpcriments are gived to show these problems.

Robust Video Text Detection with Morphological Filtering Enhanced MSER

Video text detection is a challenging problem, since video image background is generally complex and its subtitles often have the problems of color bleeding, fuzzy boundaries and low contrast due to video lossy compression and low resolution. In this paper, we propose a robust framework to solve these problems. Firstly, we exploit gradient amplitude map （GAM） to enhance the edge of an input image, which can overcome the problems of color bleeding and fuzzy boundaries. Secondly, a two-direction morphological filtering is developed to filter background noise and enhance the contrast between background and text. Thirdly, maximally stable extremal region （MSER） is applied to detect text regions with two extreme colors, and we use the mean intensity of the regions as the graph cuts＇ label set, and the Euclidean distance of three channels in HSI color space as the graph cuts smooth term, to get optimal segmentations. Finally, we group them into text lines using the geometric characteristics of the text, and then corner detection, multi-frame verification, and some heuristic rules are used to eliminate non-text regions. We test our scheme with some challenging videos, and the results prove that our text detection framework is more robust than previous methods.

Fabric Defect Detection Using Independent Component Analysis and Phase Congruency

A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.

Robust Filtration Techniques in Geometrical Metrology and Their Comparison

Filtration is one of the core elements of analysis tools in geometrical metrology. Filtration techniques are progressing along with the advancement of manufacturing technology. Modern filtration techniques are required to be robust against outliers, applicable to surfaces with complex geometry and reliable in whole range of measurement data. A comparison study is conducted to evaluate commonly used robust filtration techniques in the field of geometrical metrology, including the two-stage Gaussian filter, the robust Gaussian regression filter, the robust spline filter and morphological filters. They are compared in terms of four aspects： functionality, mathematical computation, capability and characterization parameters. As a result, this study offers metrologists a guideline to choose the appropriate filter for various applications.

Real-time Face Detection using Skin Color Model

This paper presents a new face detection approach to real-time applications, which is based on the skin color model and the morphological filtering. First the non-skin color pixels of the input image are removed based on the skin color model in the YC rC b chrominance space, from which we extract candidate human face regions. Then a mathematical morphological filter is used to remove noisy regions and fill the holes in the candidate skin color regions. We adopt the similarity between the human face features and the candidate face regions to locate the face regions in the original image. We have implemented the algorithm in our smart media system. The experiment results show that this system is effective in real-time applications.