Mathematical Morphology Research Articles

A Subpixel Concrete Crack Measurement Method Based on the Partial Area Effect

To improve the accuracy of concrete crack measurement with a machine vision method in structural health monitoring and in technical status evaluation, a subpixel crack measurement method based on the partial area effect was proposed. (1) First, a pixelwise crack image segmentation method was established through a multi-step process of multi-threshold fusion and morphology operation, and a novel pixel degree crack width calculation method was developed with the extraction of the middle points, the center line and its normal, and the intersection of the center line normal and crack edges. (2) Then, a subpixel algorithm based on the partial area effect was introduced to locate vertical, horizontal, and oblique cracks in subpixel crack edges, and the subpixel crack width could be calculated along the crack center line pixelwise. (3) Finally, the proposed method was verified by indoor concrete beam crack measurement tests with a digital microscope, and the results show that the maximum relative errors of the subpixel width of the horizontal, vertical, and oblique straight cracks measured by the proposed method were 3.06%, 8.97%, and 5.16%, respectively. The absolute error of the crack length was less than 0.30 mm, and the measurement accuracy could reach 0.01 pixels. The subpixel crack measurement method provides a novel possible solution for structural health monitoring.

Brake disc positioning and defect detection method based on improved Canny operator

AbstractFirstly, the Canny operator combined with circle fitting is used to position the working surface. Secondly, the median filter and the Laplacian operator are selected for preprocessing. Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection. A bidirectional connection is introduced to optimize the edge detection process of the Canny operator, and a parallel algorithm is introduced to shorten the detection time. Finally, the standard Hough transform needs to traverse the entire parameter space, and the calculation is complicated, and the probability Hough transform is selected. The experimental results show that compared with the traditional Canny algorithm, the precision, recall, and F1‐score of the improved Canny algorithm are increased by 3.2%, 13.2%, and 6.0%, respectively. After adding parallel processing, the average detection time was reduced by 45 ms. Finally, the accuracy of scratches extracted using this algorithm reached 97.96%, the leak rate, and mistake rate of this study are only 3.33% and 2.00%, which provides a more accurate and efficient theoretical support for the research on brake disc scratch detection.

Fault Diagnosis for Road Heading Bearings Based On a Multiscale Enhanced Cascaded Difference Filter

Abstract In this paper, a novel multiscale morphological filter, called multiscale enhanced cascaded difference filter (MECDF), is proposed for the fault detection of road heading bearings. Firstly, the cascaded morphological operators are established based on the cascade of the basic morphological operators with similar properties, and then the morphological difference operation is introduced to propose the cascaded difference operators. Subsequently, the enhanced cascaded difference operator (ECDO) is constructed through the convolution of cascaded difference operators. Moreover, since the scale range of structure element (SE) also determines the filtering performance of multiscale morphological filter, an improved multiscale analysis method is presented to select the optimal scale range. Finally, the bearing experimental signals are implemented to validate the effectiveness of MECDF. Experimental results testify that the scale range determined by the MECDF is better than other multiscale morphological filters. Meanwhile, the feature extraction capability of ECDO is also better than other existing morphological difference operators.

Analysis of Vision based Techniques for the Translation of Indian Sign Language

Sign language acts as a medium of communication among those of the hearing impaired and mute community. However, it cannot be easily understood by common people. Various research has been done to bridge this gap by developing Sign Language Recognition (SLR) methodologies. Studies say that 1 in every 5 deaf people is Indian.
 In this paper, a thorough review of these methodologies has been done, to compare and contrast various aspects of them. This includes an overview on different preprocessing methods used like segmentation, image morphological processing, cropping, etc, feature extraction techniques like Fourier Descriptors, Image Moments, Eigen values, Mediapipe and others. This study also covered classification models spanning from Distance metrics to Kernel based approaches and feedforward neural networks, along with Deep Learning based methods such as CNNs, LSTMs, GANs, Transformers etc.

Mathematical Morphological Operations for Quadri -Partitioned Neutrosophic Set

This research aims to introduce a novel notion in mathematical morphological operations on a quadri-partitioned neutrosophic set, which is a particular case of the neutrosophic refined set. In neutrosophic theory, the set is divided into three parts: the true set, the false set, and the indeterminancy set. The indeterminacy is studied in depth in quadri neutrosophic. The primary intention is to reduce uncertainty. The suggested study extracts the core concepts of morphological operations and explains their algebraic properties. Some features of morphological operators linked to quadri-partitioned neutrosophic sets are also derived.

One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning

Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene. In conventional models, the edge image generated is ambiguous, and the edge lines are also very thick, which typically necessitates the use of non-maximum suppression (NMS) and morphological thinning operations to generate clear and thin edge images. In this paper, we aim to propose a one-stage neural network model that can generate high-quality edge images without postprocessing. The proposed model adopts a classic encoder-decoder framework in which a pre-trained neural model is used as the encoder and a multi-feature-fusion mechanism that merges the features of each level with each other functions as a learnable decoder. Further, we propose a new loss function that addresses the pixel-level imbalance in the edge image by suppressing the false positive (FP) edge information near the true positive (TP) edge and the false negative (FN) non-edge. The results of experiments conducted on several benchmark datasets indicate that the proposed method achieves state-of-the-art results without using NMS and morphological thinning operations.

Ultra High-Speed Fault Diagnosis Scheme for DC Distribution Systems Based on Discrete Median Filter and Mathematical Morphology

Ultra High-Speed Fault Diagnosis Scheme for DC Distribution Systems Based on Discrete Median Filter and Mathematical Morphology

High-throughput assessment of cellular senescence.

Cellular senescence is a molecular process that is activated in response to a large variety of distinct stress signals. Mechanistically, cellular senescence is characterized by an arrest in cell cycle accompanied by phenotypic adaptations and physiological alterations including changes in the secretory profile of senescent cells termed the senescence-associated secretory phenotype (SASP). Here we describe a detailed, automation- compatible method for the detection of senescence-associated beta galactosidase (SA-β-gal) activity as a hallmark of cellular senescence using a conventional fluorescent microscope equipped with a transmitted light module. Moreover, we outline a protocol for the automated analysis of cellular senescence using convolutional neural networks (CNNs) and mathematical morphology. In sum, we provide a toolset for the high throughput assessment of cellular senescence based on light microscopy and automated image analysis.

A Chinese License Plate Recognition System based on OpenCV for complex environments

License plate recognition technology is a key to realizing intelligent traffic management. Nowadays, most of the license plate recognition technologies are only designed for the preferred lighting conditions. But in the natural scenes, the change of the weather and the light intensity throughout the day carry many difficulties to the location and recognition of the license plate system. To solve this problem, we proposed a license plate recognition system based on OpenCV and SVM (Support Vector Machines) in this paper. First, the Sobel Edge Detection algorithm combined with Mathematical Morphology was used to determine the approximate outline of the license plate. And Orthographic Projection was then used to segment the characters in the license plate. Finally, the segmented characters were identified by using SVM. The recognition accuracy of the proposed system is 96.52%. In order to improve the operability of the system, we have designed a Graphical User Interface (GUI) based on PyQt5, which makes the interface more beautiful and simple.

Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Feature Enhancement Method for Fuzzy Image Using Mathematical Morphology and Deep Learning

Feature Enhancement Method for Fuzzy Image Using Mathematical Morphology and Deep Learning

Detection and counting of Leishmania intracellular parasites in microscopy images.

Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania and has a high prevalence and impact on global health. Currently, the available drugs for its treatment have drawbacks, such as high toxicity, resistance of the parasite, and high cost. Therefore, the search for new, more effective, and safe drugs is a priority. The effectiveness of an anti-leishmanial drug is analyzed through in vitro studies in which a technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is slow, laborious, and prone to errors. To develop a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques. Segmentation of objects in the microscope image that might be Leishmania amastigotes was performed using the multilevel Otsu method on the saturation component of the hue, saturation, and intensity color model. In addition, morphological operations and the watershed transform combined with the weighted external distance transform were used to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, the selection of which as well as the definition of the features to be used were also part of this research. MATLAB was used for the development of the system. The results were evaluated in terms of sensitivity, precision, and the F-measure and suggested a favorable effectiveness of the proposed method. This system can help researchers by allowing large volumes of images of amastigotes to be counted using an automatic image analysis technique.

Detection of poikilocytosis cells in anemia using (ANN) & embedded systems

In the bloodstream, erythrocytes stand as vital carriers of oxygen and carbon dioxide, facilitated by the presence of hemoglobin within their structures. However, deviations in erythrocyte size can lead to the formation of Poikilocyte cells, a characteristic feature of conditions like Iron Deficiency Anemia. Variants of Poikilocytoses, such as Degmacyte, Dacrocyte, Schistocyte, and Elliptocyte, denote distinct alterations in erythrocyte morphology, often associated with diminished iron levels crucial for haemoglobin synthesis. In a recent study, the differentiation between normal RBCs and Poikilocyte cells has been addressed through the application of Artificial Neural Network (ANN) algorithms, leveraging extracted features from digital images of blood smears. This approach offers a more precise means of identifying blood disorders compared to traditional visual inspection, utilizing image analysis techniques to detect deviations in color, size, and statistical parameters. The methodology involves a series of computational steps including preprocessing, segmentation, morphological operations, feature extraction, and classification, all executed within the Matlab environment. Furthermore, to enhance diagnostic capabilities, the system integrates glucose level measurement alongside erythrocyte analysis, transmitting data to a controller which relays results via GSM signal as SMS and LCD display. This comprehensive approach not only automates cell identification and classification but also ensures efficient and accurate analysis, including the automated separation of overlapped cells.

Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation

Various opinions were made by the researchers to develop an automatic network for Optical Character Recognition (OCR). Still, character recognition in handwritten scripts is an unsolved task. In this paper, two efficient techniques are developed an effective character recognition technique for the handwritten Kannada scripts. The Kannada Character Recognition (KCR) techniques faced several challenges due to the different writing styles of people and the absence of fixed spacing among alphabets, words and lines. Another complication in the KCR model is the absence of large datasets to train the network, and it isn’t easy to write the Kannada script by combining the Kannada alphabets. Therefore, a new handwritten KCR approach is developed to identify the characters from the ancient Kannada scripts. The required Kannada script images are gathered from various online databases. The garnered images are preprocessed and segmented using morphological operation and thresholding. The relevant features from the images are achieved by the geometric feature extraction method. Finally, the characters are recognized by utilizing the Long Short Term Memory (LSTM) network, and the experimental results will be analyzed over the traditional optimization strategies and baseline works to evaluate the efficiency of the proposed network.

MRI-based brain tumor detection and types of classification using CEHJB-RI and JMST

The Brain Tumor (BT), which forms in the brain cells and spreads to the whole brain, may lead to death. Hence, early diagnosis of BT is significant. Still, the detection of BT between the skull and brain region is not concentrated, which results in misclassification outcomes. Thus, this article proposes Magnetic Resonance Imaging (MRI)-based BT detection and types’ classification utilizing Carlitz Exponential Hamilton Jacobi Bellman-based Reinforcement Learning (CEHJB-RL) and JenSorensen similarity-based Minimum Spanning Tree (JMST). Primarily, raw MRI images are taken and then pre-processed. Then, with skull and without skull regions are extracted from the pre-processed image and are subjected to the graph construction. Conversely, the edges are detected from the pre-processed image that can be patch-extracted and subjected to graph construction. By utilizing JMST and Morphological Operations (MO), the graphs are constructed. Thereafter, the features are extracted and fed to the classifier. Then, the type of BT is classified by the classifier using CEHJB-RL. Concerning the performance metrics, the outcomes illustrated that the proposed technique attained a higher accuracy (99.27%), which is better than other existing techniques.

Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction

The crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton is proposed in this paper. The main processing steps include image preprocessing, crack skeleton extraction, and inflection point recognition. In extracting the crack skeleton, the skeleton is initially extracted using morphological operations. After identifying the endpoints based on the neighborhood distribution of the skeleton points, the burrs are tracked until the branching point using the endpoints as the starting point, and the skeleton burrs are judged and removed by setting a threshold with a burr removal rate of 100%. For the identification of inflection points based on chain code calculation, the algorithm is optimized by the concentration of the inflection points and the distance and angle between the inflection points to remove false inflection points. The test data show that the algorithm has good adaptability and the accuracy is higher than 90%. After obtaining the crack skeleton image with real inflection points, the structural features of the skeleton can be calculated, including the distance ratio before and after the inflection points and the angle formed between the inflection points. This lays the foundation for future research to realize the recognition of the same crack at different time points.

Mathematical morphology based lung segmentation using multiscale dense pyramid network architecture

Mathematical morphology based lung segmentation using multiscale dense pyramid network architecture

A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power Quality Disturbance Detection and Classification

A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power Quality Disturbance Detection and Classification

A Gradient-Constrained Morphological Operation for Retrieving Subcanopy Topography Over Densely Forested Areas From ICESat-2/ATL03 Data

A Gradient-Constrained Morphological Operation for Retrieving Subcanopy Topography Over Densely Forested Areas From ICESat-2/ATL03 Data

Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.