Multiscale Structuring Elements Research Articles

A graph-based top-down visual attention model for lockwire detection via multiscale top-hat transformation

As a locking device, lockwire is widely used in industrial fields that produces high levels of vibration. Lockwire detection is vital for ensuring mechanical stability. However, the existing methods are inapplicable to detect all types of small-size lockwires in complex backgrounds. In this paper, we construct a graph-based top-down visual attention model via the multiscale top-hat transformation to pick out lockwires from various complex backgrounds. Since lockwires typically exhibit separated bright spot-like structures at different scales in images, we firstly construct multiscale anisotropic Gaussian structuring elements to obtain the top-hat feature map. Based on a novel connectivity function, an undirected graph is then constructed. Afterwards, we propose an improved shortest path algorithm to remove prominent complex background components and extract lockwire candidates by minimizing the redefined cost function. Taking full advantages of imaging characteristics of lockwires, we design three saliency metrics to strengthen the saliency of lockwires while weakening backgrounds. Finally, a global top-down saliency map is produced to detect lockwires from complex backgrounds by combining three saliency maps. The experimental results show that our proposed method achieves superior performance compared to state-of-the-art methods.

Adaptive Morphological Reconstruction for Seeded Image Segmentation

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.

An infrared small target detection based on multi-feature difference decision coupling Top-Hat transform

In order to improve the detection accuracy of dim target in infrared image with complex background, an infrared small target detection algorithm based on multi-feature tightness difference decision coupling improved Top-Hat transform. Firstly, multi-scale structure elements was obtained by segmenting the single structural element, and the gray change map was defined according to the gray difference between the small target and its surrounding background, and the target decision factor was constructed by calculating the mean and variance of this map, so the new Top-Hat transformation was formed by embedding it and multi-scale structure elements into classical Top-Hat transform. Then the multi-feature tightness difference model was established to extract candidate regions that contain the real weak and small targets were extracted by combining the gray intensity, contrast and structure information. Finally, the pipeline filtering pattern was introduced to eliminate the suspicious objects in the candidate region and keep the real dim target. The experimental data show that this algorithm had higher detection precision to completely check out the dim target with better ROC curve under the complex background.

Structural displacement and strain monitoring based on the edge detection operator

To decrease the cost of instrumentation for the strain and displacement monitoring method that uses sensors as well as considers the structural health monitoring challenges in sensor installation, it is necessary to develop a machine vision-based monitoring method. For this method, the most important step is the accurate extraction of the image feature. In this article, the edge detection operator based on multi-scale structure elements and the compound mathematical morphological operator is proposed to provide improved image feature extraction. The proposed method can not only achieve an improved filtering effect and anti-noise ability but can also detect the edge more accurately. Furthermore, the required image features (vertex of a square calibration board and centroid of a circular target) can be accurately extracted using the extracted image edge information. For validation, the monitoring tests for the structural local mean strain and in-plane displacement were designed accordingly. Through analysis of the error between the measured and calculated values of the structural strain and displacement, the feasibility and effectiveness of the proposed edge detection operator are verified.

Microscopy mineral image enhancement through center operator construction.

Enhancing a microscopy mineral image to produce clear and rich details is important for mineral analysis. To enhance effectively, an algorithm utilizing the constructed center operator is proposed in this paper. First, the center operator is constructed from the opening and closing based toggle operator. Second, the mineral image features are extracted by using the constructed center operator. Third, the multiscale mineral features are extracted through multiscale morphological theory using the multiscale structuring elements. Fourth, the final features for mineral image enhancement are calculated from the extracted multiscale features based on the weight strategy. Finally, the mineral image is effectively enhanced through importing the final features into the original mineral image. Experimental results on various microscopy mineral images verified that the proposed algorithm performed well for enhancement and had competing performance compared with some existing algorithms.

Image analysis through feature extraction by using top-hat transform-based morphological contrast operator

Image decomposition and reconstruction is an important way for image analysis. To be effective for image decomposition and reconstruction, a method using extracted features through top-hat transform-based morphological contrast operator (MCOTH) is proposed in this paper. First, the morphological contrast operator constructed using the top-hat transforms is discussed. Then, extracting the bright and dark image features in the result of MCOTH is given. Based on the extracted bright and dark image features, the original images are decomposed into multiscale complete decompositions using multiscale structuring elements. After processing the decomposed images following different application purposes, the final result image can be reconstructed from the processed decomposition images. To verify the effectiveness of the proposed image analysis method through image decomposition and reconstruction, the application of image enhancement and fusion are discussed. The experimental results show that because the proposed image decomposition and reconstruction method reasonably decomposes the original image into complete decomposition with useful image features at different scales, the useful image features could be easily used for different applications. After the useful image features are processed, the final result image could be reconstructed. Moreover, different types of images are used in the experiments of image enhancement and fusion, and the results are effective. Therefore, the proposed image decomposition and reconstruction method in this paper are effective methods for image analysis and could be widely used in different applications.

Image enhancement through contrast enlargement using the image regions extracted by multiscale top-hat by reconstruction

An image enhancement algorithm based on multiscale top-hat by reconstruction is proposed in this paper. Firstly, multiscale top-hat by reconstruction using multiscale structuring elements is discussed. Then, multiscale bright and black image regions are extracted. Thirdly, useful image regions for image enhancement are obtained from the extracted multiscale bright and black image regions. Finally, after a base image is calculated from the results of the opening and closing by reconstruction operations, the original image is enhanced through combing the useful image regions into the base image. Experimental results on different types of images show that the proposed algorithm is efficient.

Top-hat by reconstruction operators based multi-scale multi-structuring element method for multiple linear feature detection with simple post-processing

To efficiently extract all the possible linear features in image, a multi-scale multi-structuring element top-hat by reconstruction operator based algorithm with simple post-processing is proposed in this paper. Multi-scale top-hat by reconstruction operator using multi-scale structuring elements is discussed, firstly. Also, through importing multi-structuring elements with linear shapes at different directions, multi-scale multi-structuring element top-hat by reconstruction operator for linear feature extraction is shown. By using the multi-scales of multi-structuring elements, the method of extracting all the possible linear feature regions in an image is proposed. After extracting the linear feature regions, the final detected linear features, which are expressed as lines with different shapes and lengths, are obtained through image binarisation and refinement. Experimental results on different types of images show that, the proposed algorithm is efficient for linear feature detection and could be widely used in different applications related to multiple linear feature detection.

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.

Enhancing microscopy images of minerals through morphological center operator‐based feature extraction

To well enhance the mineral image and image details obtained by microscopes, an effective mineral image enhancement algorithm through feature extraction using the morphological center operator is proposed in this work. First, mineral image feature extraction based on the morphological center operator is proposed and discussed. Second, the multiscale extension of the mineral image feature extraction is given by using the multiscale structuring elements. Third, the important mineral image features at multiscales of image are extracted and used to construct the final mineral features for mineral image enhancement. Finally, the original mineral image is well enhanced through importing the extracted final mineral image features into the original mineral image. Experimental results on different types of microscopy images of minerals verified the effective performance of the proposed algorithm for microscopy mineral image enhancement.

Weighted image fusion based on multi-scale top-hat transform: Algorithms and a comparison study

Image fusion is an important technique in image processing. The key of image fusion is importing all the possible useful image features into the fusion result image. Top-hat transform could extract bright and dim image features. Multi-scale top-hat transform could extract all the image features at different image scales. Therefore, the multi-scale top-hat transform could be used for image fusion. In this paper, the weighted image fusion algorithms based on the multi-scale top-hat transform are discussed. The multi-scale top-hat transform using multi-scale structuring elements with the same shape and increasing sizes is used to extract the useful bright and dim image features for image fusion. To appropriately import the extracted useful bright and dim image features into the fusion result image, three weighted image fusion strategies are discussed, which gives three effective algorithms for image fusion. Moreover, the visual and quantitative comparisons of these algorithms with some widely used algorithms are also discussed. And, the experimental results show that, the weighted image fusion algorithms based on the multi-scale top-hat transform could achieve satisfying results in all the visual and quantitative comparisons. And, the results suggested that, because the mean value weighted image fusion algorithm obtains clearer result images with more image details than other algorithms and achieves the best overall performance on all the measures, the mean value weighted image fusion algorithm could achieve a better performance. All of the experimental results indicate that, the weighted image fusion algorithms based on multi-scale top-hat transform may be widely used in different applications, such as target detection, object recognition and security surveillance, for image fusion.

Measure of image clarity using image features extracted by the multiscale top-hat transform

To correctly quantify the clarity of an image and achieve good discrimination, an effective measure of image clarity using the image features extracted by the multiscale top-hat transform is presented in this paper. Firstly, multiscale image features are extracted by the top-hat transform using multiscale structuring elements. Then, the final extracted image features for constructing the measure of image clarity are obtained from the extracted multiscale image features. Finally, the mean value of the final extracted image features is calculated as the proposed measure of image clarity. Comparison experiments and application to image clarity enhancement of mineral images show that the proposed measure is effective for measuring image clarity and has good discrimination ability compared with some other measures. Therefore, the proposed image clarity measure may be used for different applications such as automatic focusing in microscopy, image enhancement, image fusion and so on.

Application of Multi-Scale Morphology Gradient in the Foreign Fibers Recognition

The extraction of edge feature is a key technology in the foreign fibers recognition. The traditional algorithm of edge detection operator is more sensitive to noise, which is unable to extract complete edge information. Based on the morphological edge detection, we can get the ideal foreign fibers edge characteristics under the condition of noise for the multi-scale structure elements, combining with inflation and corrosion operator to structure morphology gradient operator, the adjustment of morphology structure elements dimensions. Experiment results show that it has a certain feasibility and effectiveness compared to classic edge detection algorithm in the respect of restraining the noise and improving the testing precision.

Edge preserved image fusion based on multiscale toggle contrast operator

An edge preserved image fusion algorithm based on multiscale toggle contrast operator is proposed in this paper. First, the multiscale toggle contrast operator using multiscale structuring elements with the same shape and increasing sizes is discussed. Then, the multiscale dilation and erosion features which represent the edge information of the original images are extracted by using the multiscale toggle contrast operator. After the final dilation and erosion fusion features are constructed from the extracted multiscale dilation and erosion features, the final fusion image is formed by combining the final dilation and erosion fusion features into a base image calculated from the original image. Because the multiscale dilation and erosion features which represent the edge information are effectively extracted and combined into the final fusion image, clear and well preserved edge features of the final fusion image are obtained. Experimental results show that, the proposed image fusion algorithm is efficient for edge preserving and performs well.

Method for building recognition from FLIR images

Herein, a method for building recognition is presented in forward looking infrared (FLIR) images with clutter background, which is composed of the several sub-procedures. In the first phase, a three-dimensional (3-D) target model is generated and the model features are predicted based on the sensor's perspective relative to the 3-D target model. The second phase of the process, multi-scale structuring elements are generated pertaining to the 3-D target model and flight trajectory. Structuring elements for infrared image is selected by a look-up-table approach based on the parameters of sensor's view, and the use of morphology-based filters can respond to the size and shape of target to suppress the clutter background. In the following process, iterative segmentation for the result image of background suppression is used to obtain regions of interest (ROIs), and features extraction of ROIs and matching retain the ROIs that are closest to predicted features. Lastly, the target is identified by fusing the line features and multi-frame integration. Experiment results show the proposed algorithm can precisely recognize the target from FLIR images with a complicated background.

Application of support vector machine based on pattern spectrum entropy in fault diagnostics of rolling element bearings

This paper presents a novel pattern classification approach for the fault diagnostics of rolling element bearings, which combines the morphological multi-scale analysis and the ‘one to others’ support vector machine (SVM) classifiers. The morphological pattern spectrum describes the shape characteristics of the inspected signal based on the morphological opening operation with multi-scale structuring elements. The pattern spectrum entropy and the barycenter scale location of the spectrum curve are extracted as the feature vectors presenting different faults of the bearing, which are more effective and representative than the kurtosis and the enveloping demodulation spectrum. The ‘one to others’ SVM algorithm is adopted to distinguish six kinds of fault signals which were measured in the experimental test rig under eight different working conditions. The recognition results of the SVM are ideal and more precise than those of the artificial neural network even though the training samples are few. The combination of the morphological pattern spectrum parameters and the ‘one to others’ multi-class SVM algorithm is suitable for the on-line automated fault diagnosis of the rolling element bearings. This application is promising and worth well exploiting.

Multiscale toggle contrast operator-based mineral image enhancement

Mineral image is one type of crucial data for mineral research and analysis. However, some mineral images are not clear in some cases. To efficiently enhance mineral images and therefore benefit the applications of these images, a multiscale toggle contrast operator based algorithm is proposed in this paper. First, the toggle contrast operator is discussed. Secondly, the multiscale toggle contrast operator using structuring elements with different sizes is given. Thirdly, the multiscale toggle contrast operator is used to enhance the original image at different scales. Finally, the final enhanced image is constructed from the multiscale enhanced results. Because multiscale structuring elements are used, the algorithm performs efficiently and produces few noises. Experimental results show that the proposed algorithm is efficient for mineral image enhancement. More importantly, the proposed algorithm could be also used in other types of images, such as visual image, medical image and so on, for image enhancement.

Detection of Retinal Blood Vessels Based on Morphological Analysis with Multiscale Structure Elements and SVM Classification

Detection of Retinal Blood Vessels Based on Morphological Analysis with Multiscale Structure Elements and SVM Classification