Multi-scale Edge Detection Algorithm Research Articles

A method for measuring ice thickness of wind turbine blades based on edge detection

In recent years, with the successive development of mountain wind power resources in the high humidity regions, the problem of ice covering on wind turbine blades has become increasingly prominent. Wind turbine blade icing will affect blade aerodynamic performance and reduce power generation. In this paper, a method for measuring the ice thickness of wind turbine blades based on edge detection was proposed. As the icing images of blades have characteristics like low contrast, unclearness and blurred edges, the images were preprocessed by methods like defogging, gray-scale transformation, histogram correction, Gaussian filtering, and Laplace edge enhancement. Aiming at the characteristics of wind turbine icing images with many noises and many small edges, a multiscale wavelet edge detection algorithm was used to detect the blade icing images. Based on the pixel coordinates of the ice edge and the edge of the blade, through the pre-calibration, a method for calculating the ice thickness of the leading edge of the blade based on the pixel coordinate difference /real length ratio was proposed. Multiple measurement tests were performed in the natural icing environment. The average ice thickness measurement error value of short distance (0.5 m)/long distance (35 m) is 0.31 mm/8.9 mm, and the average ice thickness measurement error rate is 2.62% /6.0%.

Mathematical Model and microCT-Based Kinematic Analysis of the Rostrum Mouthparts in Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae).

To uncover the chewing mechanism of Cyrtotrachelus buqueti Guer, a mathematical model was created and a kinematic analysis of its rostrum mouthparts was conducted for, to our knowledge, the first time. To reduce noise and improve the quality of scanning electron micrographs of the weevil's mouthparts, nonlocal means and integral nonlocal means algorithms were proposed. Additionally, based on a comparison and analysis of five classical edge detection algorithms, a multiscale edge detection algorithm based on the B-spline wavelet was used to obtain the boundaries of structural features. The least squares method was used to analyze the data of the mouthparts to fit the mathematical model and fitted curves were obtained using Gaussian equations. The results show that curvature and concave-convex variations of the weevil's mouthparts can highlight fluctuations in friction effects when it chews bamboo shoots, which is helpful in preventing debris from bamboo shoots or other debris from sticking to the mouthpart surfaces. Moreover, this paper highlights the utility of micro-computed tomography (microCT) for three-dimensional (3D) reconstruction and a flowchart is suggested. The reconstructed slices were 9.0 μm thick and an accurate 3D rendered model was obtained from a series of microCT slices. Finally, a real model of the rostrum mouthparts was analyzed using finite-element analysis. The results provide a biological template for the design of a novel bionic drilling mechanism.

Edge detection based on Retinex theory and wavelet multiscale product for mine images.

The application of visual technology to mine robots has become a hot topic in the development of coal mine automatic production. Key techniques of robot control are the feature recognition of sampled videos and the perception of complex surroundings. However, it is difficult for features in underground images with dark hue and low target discrimination to be recognized and extracted, especially for reasons of the nonuniform illumination and heavy dust concentration in mines. Hence, an edge detection algorithm based on the Retinex theory and wavelet multiscale product is proposed in this paper for low-light-level mine image feature extraction, which employs a modified multiscale Retinex method to deal with the low frequency subplot after the wavelet decomposition, an improved fuzzy enhancement approach to handle high frequency components, and finally a revised multiscale product edge detection algorithm to obtain the ultima edge image. Compared with a variety of algorithms by detecting edges of both normal illuminated and underground images, experimental results show that with characteristics of high real-time performance and detection accuracy, the proposed algorithm can exactly meet the needs of surrounding environment perception for mine robots, which applies well to image edge detection in low illumination mines.

Multiscale edge detection and parametric shape modeling for boundary delineation in optoacoustic images.

In this article, we present a novel scheme for segmenting the image boundary (with the background) in optoacoustic small animal in vivo imaging systems. The method utilizes a multiscale edge detection algorithm to generate a binary edge map. A scale dependent morphological operation is employed to clean spurious edges. Thereafter, an ellipse is fitted to the edge map through constrained parametric transformations and iterative goodness of fit calculations. The method delimits the tissue edges through the curve fitting model, which has shown high levels of accuracy. Thus, this method enables segmentation of optoacoutic images with minimal human intervention, by eliminating need of scale selection for multiscale processing and seed point determination for contour mapping.

Visual C++ Call MATLAB Multi-Scale Edge Detection

This article describes the Visual C++ call MATLAB engine detailed steps to achieve the VC-based interface to call MATLAB field geological position of multi-scale edge detection algorithm. The results show that, the development cycle of program and the workload is reduced, but also has a good Visual C++design of MATLAB user interface and convenient calculation and drawing ability. The program for the development of geophysical analysis software provides an effective way of high reliability, low development cycle.

AUTOMATIC DETECTION AND TRACKING OF CORONAL MASS EJECTIONS. II. MULTISCALE FILTERING OF CORONAGRAPH IMAGES

Studying CMEs in coronagraph data can be challenging due to their diffuse structure and transient nature, and user-specific biases may be introduced through visual inspection of the images. The large amount of data available from the SOHO, STEREO, and future coronagraph missions, also makes manual cataloguing of CMEs tedious, and so a robust method of detection and analysis is required. This has led to the development of automated CME detection and cata- loguing packages such as CACTus, SEEDS and ARTEMIS. Here we present the development of a new CORIMP (coronal image processing) CME detection and tracking technique that overcomes many of the drawbacks of current catalogues. It works by first employing the dynamic CME separation technique outlined in a companion paper, and then characterising CME structure via a multiscale edge-detection algorithm. The detections are chained through time to determine the CME kinematics and morphological changes as it propagates across the plane-of-sky. The effectiveness of the method is demonstrated by its application to a selection of SOHO/LASCO and STEREO/SECCHI images, as well as to synthetic coronagraph images created from a model corona with a variety of CMEs. The algorithms described in this article are being applied to the whole LASCO and SECCHI datasets, and a catalogue of results will soon be available to the public.

A Study of Decadal Coastal Changes on Western Taiwan Using a Time Series of ERS Satellite SAR Images

In this paper, coastal line changes were monitored and analyzed from a sequence of ERS-1/2 SAR images covering the years 1996 to 2005, totaling 44 images for each year. Waterlines were extracted using a multi-scale edge detection algorithm, and further refined by means of morphology. Substantial analysis was carried out in conjunction with ground survey and sonar bathymetric mapping. In addition, tidal records were used to ensure all the shore lines been calibrated to the same tidal level. Results showed that Waisanting Sandbar, a north-southward sandbar, experienced significant accretion and erosion, moving southward about 700 meters during a 10-year period, and shrinking to just one third of its 1996 size. The surrounding coastal waters and the estuary of the Peikang River receded substantially, moving inward toward the coastal flat. The water channel became even more heavily deposited as a result. Finally, Haifengdao Sandbar, another sandbar, moved southward about 1.5 km, although its size remained the same from 1996 to 2005. It also showed a clear tendency to receding inward. We conclude that satellite remote sensing by SAR, aided by ground tidal data, bathymetric maps, and optical images, provides an effective and efficient tool for understanding coastal processes over large areas of coverage and long time spans.

NaXi Pictographs Edge Detection Using Lifting Wavelet Transform

This paper presents a lifting wavelet based edge detection algorithm for NaXi Pictographs, effectively reducing noises while improving the precision, which makes up the drawbacks of traditional edge detection. It is proved by emulation that compared with Laplace operator, Sobel operator, Canny operator, and multi-scale wavelet edge detection algorithm, this algorithm has better detection effect.

Merging system for multiscale edge detection

We present a multiscale edge detection algorithm whose aim is to detect edges whatever their slope. Our work is based on a generalization of the Canny-Deriche filter, characterized by a more realistic edge than the traditional step shape edge. The filter impulse response is used to generate a multiscale edge detection scheme. For the merging of the edge information, we use a geometrical classifier developed in our laboratory. The segmentation system thus set up, after the training phase, does not require any adjustment or depend on any parameter. The main original property of this algorithm is that it leads to a binary edge image without any threshold setting. The quality of the results is inferior to that for classical multiscale merging approaches; nevertheless, this system, studied for real-time functioning, presents satisfactory performance for well-contrasted images and excellent performance for noisy images.

A multiscale edge detection algorithm based on wavelet domain vector hidden Markov tree model

The wavelet analysis is an efficient tool for the detection of image edges. Based on the wavelet analysis, we present an unsupervised learning algorithm to detect image edges in this paper. A wavelet domain vector hidden Markov tree (WD-VHMT) is employed in our algorithm to model the statistical properties of multiscale and multidirectional (subband) wavelet coefficients of an image. With this model, each wavelet coefficient is viewed as an observation of its hidden state and the hidden state indicates if the wavelet coefficient belongs to an edge. The WD-VHMT model can be learned by an expectation–maximization algorithm. After the model is learned, we employ an extended Viterbi algorithm to uncover the hidden state sequences according to the maximum a posterior estimation. The experiment results of the edge detection for several images are provided to evaluate our algorithm.

A multiscale edge detection algorithm based on wavelet domain vector hidden Markov tree model

The wavelet analysis is an efficient tool for the detection of image edges. Based on the wavelet analysis, we present an unsupervised learning algorithm to detect image edges in this paper. A wavelet domain vector hidden Markov tree (WD-VHMT) is employed in our algorithm to model the statistical properties of multiscale and multidirectional (subband) wavelet coefficients of an image. With this model, each wavelet coefficient is viewed as an observation of its hidden state and the hidden state indicates if the wavelet coefficient belongs to an edge. The WD-VHMT model can be learned by an expectation–maximization algorithm. After the model is learned, we employ an extended Viterbi algorithm to uncover the hidden state sequences according to the maximum a posterior estimation. The experiment results of the edge detection for several images are provided to evaluate our algorithm.

A multi-scale morphological approach to SAR image edge detection

This paper introduces a multi-scale morphological edge detection algorithm to extract SAR image edge which suffers seriously from noise. Combining the basic theme of morphology with that of multi-scale analysis, the algorithm presents the outstanding characteristics of accuracy and robustness. Comparative Experiments reveal its fine performance.

A multiscale approach to automatic medical image segmentation using self-organizing map

In this paper, a new medical image classification scheme is proposed using self-organizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a multiscale SOM classifier is trained using this training set. In this new scheme, the SOM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SOM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.

A CCD/CMOS focal-plane array edge detection processor implementing the multiscale veto algorithm

A prototype 32/spl times/32 array processor fabricated in 2-/spl mu/m charge coupled devices (CCD)/CMOS technology implementing the multiscale veto edge detection algorithm is presented. In this algorithm, differences between pixel values are computed in the original image, as well as after applying a series of smoothing filters of varying spatial scales. An edge exists between two pixels only if the magnitude of their difference is greater than a given threshold for all levels of smoothing tested. This algorithm maps particularly well to implementation as a focal plane processor as it requires only nearest neighbor communication. The CCD array performs the functions of image acquisition, charge loading and removal, and image smoothing. Analog circuits between each pair of pixels in the array compute the absolute value of difference between neighboring values and compare it to a global threshold. These circuits have been designed to allow reliable discrimination of differences from /spl sim/3.1% to /spl sim/10.1% of full scale range and thus meet the performance requirements of many machine vision applications.