Search Results (31)

Control charts (CCs) are one of the most important techniques in statistical process control (SPC) used to monitor the behavior of critical variables. SPC is based on the averages of the samples taken. In this way, not every measurement is observed, and errors in measurements or out-of-control behaviors that are not shown graphically can be hidden. This research proposes a novel segmented control chart (SCC) that considers each measurement of the samples, expressed in matrix form. The vision system technique is used to segment measurements by shading and segmenting into binary values based on the control limits of SPC. Once the matrix is segmented, the seven main features of the matrix are extracted using the translation-, scale-, and rotation-invariant Hu moments of the segmented matrices. Finally, a grouping is made to classify the samples in clear and simple language as excellent, good, or regular using the k-means algorithm. The results visually display the total pattern behavior of the samples and their interpretation when they are classified intelligently. The proposal can be replicated in any production sector and strengthen the control of the sampling process. Full article

This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only 2D camera images. The vision system was integrated with the Universal Robots UR5 cobot and designed for object sorting based on shape recognition. The software stack includes OpenCV for image processing, NumPy for numerical operations, and scikit-learn for multilayer perceptron (MLP) models. The paper outlines the calibration process, including lens distortion correction and camera-to-robot calibration in a hand-in-eye configuration to establish the spatial relationship between the camera and the cobot. Object localization relied on a virtual plane aligned with the robot’s workspace. Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. The evaluation focused on classification accuracy and sorting effectiveness, using sensitivity, specificity, precision, accuracy, and F1-score metrics. Results showed that neural network-based methods outperformed traditional methods in all categories, concurrently offering more straightforward implementation. Full article

In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems. Full article

Background/Objectives: The underlying mechanism of the potential involvement of inflammatory crosstalk between pericarotid fat and vascular layers in atherosclerosis pathogenesis is unclear. We investigated the association between pericarotid fat density and positive remodeling and inflammatory markers in carotid stenosis. We hypothesized that pericarotid fat density might serve as a marker of plaque inflammation in a clinical setting. Methods: We evaluated the stenosis degree and pericarotid fat density in 258 patients with carotid plaques. Plaque composition was examined, and the correlation between pericarotid fat density and expansive remodeling was investigated. Pearson’s product–moment correlation coefficient was used to examine the relationship between pericarotid fat density and the expansive remodeling ratio. We also evaluated the relationship of pericarotid fat density with plaque composition, degree of stenosis, and macrophage and microvessel counts by. The subgroup analysis compared these factors between symptomatic mild carotid stenosis. Results: The pericarotid fat density was −63.0 ± 11.1 HU. The carotid fat densities were −56.8 ± 10.4 HU in symptomatic and −69.2 ± 11.4 HU in asymptomatic lesions. The pericarotid fat density values in intraplaque hemorrhage, lipid-rich necrotic core, and fibrous plaque were −51.6 ± 10.4, −59.4 ± 12.8, and −74.2 ± 8.4 HU, respectively. Therefore, the expansive remodeling ratio was 1.64 ± 0.4. Carotid fat density and expansive remodeling ratio were correlated. Immunohistochemistry showed high macrophage and microvessel levels (143.5 ± 61.3/field and 121.2 ± 27.7/field, respectively). In symptomatic mild carotid stenosis, pericarotid fat density was correlated with other inflammatory factors. The pericarotid fat density and expansive remodeling ratio (2.08 ± 0.21) were high in mild stenosis (−50.1 ± 8.4 HU). Conclusions: Pericarotid fat and intraplaque components were well correlated. Carotid fat density may be a marker of plaque inflammation in carotid plaques. Full article

Skin cancers involve a significant concern in cancer therapy due to their association with various treatment modalities. This comprehensive review explores the increased risk of skin cancers linked to different anti-cancer treatments, including classic immunosuppressants such as methotrexate (MTX), chemotherapeutic agents such as fludarabine and hydroxyurea (HU), targeted therapies like ibrutinib and Janus Kinase inhibitors (JAKi), mitogen-activated protein kinase pathway (MAPKP) inhibitors, sonic hedgehog pathway (SHHP) inhibitors, and radiotherapy. MTX, a widely used immunosuppressant in different fields, is associated with basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and cutaneous melanoma (CM), particularly at higher dosages. Fludarabine, HU, and other chemotherapeutic agents increase the risk of non-melanoma skin cancers (NMSCs), including cSCC and BCC. Targeted therapies like ibrutinib and JAKi have been linked to an elevated incidence of NMSCs and CM. MAPKP inhibitors, particularly BRAF inhibitors like vemurafenib, are associated with the development of cSCCs and second primary melanomas (SPMs). SHHP inhibitors like vismodegib have been linked to the emergence of cSCCs following treatment for BCC. Additionally, radiotherapy carries carcinogenic risks, especially for BCCs, with increased risks, especially with younger age at the moment of exposure. Understanding these risks and implementing appropriate screening is crucial for effectively managing patients undergoing anti-cancer therapies. Full article

In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (${\mathrm{LC}}^2\mathrm{R}-\mathrm{Net}$) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that ${\mathrm{LC}}^2\mathrm{R}-\mathrm{Net}$ achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of ${\mathrm{LC}}^2\mathrm{R}-\mathrm{Net}$ in handling the task of lunar complex impact crater image retrieval. Full article

The basic information survey on homesteads requires understanding the shape of homesteads, and the shape of the homesteads based on the spatial location can reflect information such as their outline and regularity, but the current shape classification of rural homesteads at the parcel scale lacks analytical methods. In this study, we endeavor to explore a classification model suitable for characterizing homestead shapes at the parcel scale by assessing the impact of various research methods. Additionally, we aim to uncover the evolutionary patterns in homestead shapes. The study focuses on Yangdun Village, located in Deqing County, Zhejiang Province, as the research area. The data utilized comprise Google Earth satellite imagery and a vector layer representing homesteads at the parcel scale. To classify the shapes of homesteads and compare classification accuracy, we employ a combination of methods, including the fast Fourier transform (FFT), Hu invariant moments (HIM), the Boyce and Clark shape index (BCSI), and the AlexNet model. Our findings reveal the following: (1) The random forest method, when coupled with FFT, demonstrates the highest effectiveness in identifying the shape categories of homesteads, achieving an average accuracy rate of 88.6%. (2) Combining multiple methods does not enhance recognition accuracy; for instance, the accuracy of the FFT + HIM combination was 88.4%. (3) The Boyce and Clark shape index (BCSI) proves unsuitable for classifying homestead shapes, yielding an average accuracy rate of only 58%. Furthermore, there is no precise numerical correlation between the homestead category and the shape index. (4) It is noteworthy that over half of the homesteads in Yangdun Village exhibit rectangular-like shapes. Following the “homesteads reform”, square-like homesteads have experienced significant vacating, resulting in a mixed arrangement of homesteads overall. The research findings can serve as a methodological reference for the investigation of rural homestead shapes. Proficiency in homestead shape classification holds significant importance in the realms of information investigation, regular management, and layout optimization of rural land. Full article

The research focuses on the analysis of seismic data, specifically targeting the detection, edge segmentation, and classification of seismic images. These processes are fundamental in image processing and are crucial in understanding the stratigraphic structure and identifying oil and natural gas resources. However, there is a lack of sufficient resources in the field of seismic image detection, and interpreting 2D seismic image slices based on 3D seismic data sets can be challenging. In this research, image segmentation involves image preprocessing and the use of a U-net network. Preprocessing techniques, such as Gaussian filter and anisotropic diffusion, are employed to reduce blur and noise in seismic images. The U-net network, based on the Canny descriptor is used for segmentation. For image classification, the ResNet-50 and Inception-v3 models are applied to classify different types of seismic images. In image detection, Tchebichef invariants are computed using the Tchebichef polynomials’ recurrence relation. These invariants are then used in an optimized multi-class SVM network for detecting and classifying various types of seismic images. The promising results of the SVM model based on Tchebichef invariants suggest its potential to replace Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs) for seismic image detection. This approach offers a more efficient and dependable solution for seismic image analysis in the future. Full article

Sonar images have the characteristics of lower resolution and blurrier edges compared to optical images, which make the feature-matching method in underwater target tracking less robust. To solve this problem, we propose a particle filter (PF)-based underwater target-tracking method utilizing Zernike moment feature matching. Zernike moments are used to construct the feature-description vector for feature matching and contribute to the update of particle weights. In addition, the particle state transition method is optimized by using a first-order autoregressive model. In this paper, we compare Hu moments and Zernike moments, and we also compare whether to optimize the particle state transition on the tracking results or not based on the effects of each option. The experimental results based on the AUV (autonomous underwater vehicle) prove that the robustness and accuracy of this innovative method is better than the other combined methods mentioned in this paper. Full article

Geographic origins play a vital role in traditional Chinese medicinal materials. Using the geo-authentic crude drug can improve the curative effect. The main producing areas of Chinese wolfberry are Ningxia, Gansu, Qinghai, and so on. The geographic origin of Chinese wolfberry can affect its texture, shape, color, smell, nutrients, etc. However, the traditional method for identifying the geographic origin of Chinese wolfberries is still based on human eyes. To efficiently identify Chinese wolfberries from different origins, this paper presents an intelligent identification method for Chinese wolfberries based on color space transformation and texture morphological features. The first step is to prepare the Chinese wolfberry samples and collect the image data. Then the images are preprocessed, and the texture and morphology features of single wolfberry images are extracted. Finally, the random forest algorithm is employed to establish a model of the geographic origin of Chinese wolfberries. The proposed method can accurately predict the origin information of a single wolfberry image and has the advantages of low cost, fast recognition speed, high recognition accuracy, and no damage to the sample. Full article

The surface engineering of metals develops high technology to detect microscale convex, concave and flat surface patterns. It is because the manufacturing industry requires technologies to recognize microscale surface features. Thus, it is necessary to develop microscopic vision technology to recognize microscale concave, convex and flat surfaces. This study addresses microscale concave, convex and flat surface recognition via Hu moments’ patterns based on micro-laser line contouring. In this recognition, a Hu moments’ pattern is generated from a Bezier model to characterize the surface recovered through microscopic scanning. The Bezier model is accomplished by employing a genetic algorithm and surface coordinates. Thus, the flat, convex and concave surfaces are recognized based on the Hu moments’ pattern of each one. The microscope system projects a 40 μm laser line on the object and a camera acquires the object’s contour reflection to retrieve topographic coordinates. The proposed technique enhances the microscale convex, concave, flat, and surface recognition, which is performed via optical microscope systems. The contribution of microscopic shape recognition based on the Hu moments’ pattern and microscopic laser line is elucidated by a discussion based on the microscopic shape recognition performed through the optical microscopic image processing. Full article

To study the typical failure mechanism (rotor unbalance, rotor friction, and rotor crack) and vibration characteristics of the turbocharger rotor system, a rotor system dynamics simulation model was established by an improved four-node aggregate parameter method. The geometric and physical characteristics of the rotor system under three failure states and its dynamics under operation were analyzed. Thus, a typical failure dynamics simulation model of the rotor system was established. On this basis, the output failure simulation signal was extracted using the Hu invariant moment feature extraction method to analyze the system vibration characteristics under each typical failure state of the rotor system. The results show that the model in this paper can effectively reduce the computational volume and computational time, and the errors of numerical simulation were less than 3%. When an unbalance fault occurred in the rotor system, the shaft trajectory was “0” shaped and the response spectrum was dominated by 1X. When the rotor system was frictional, the shaft trajectory was a slightly concave “8” shape, and the response spectrum was dominated by 0.5X. When the rotor system was cracked, the axial trajectory was a “vortex”, and the response spectrum was dominated by 0.5X. Thus, the study of typical failure mechanism and vibration characteristics of a turbocharger rotor system by simulation calculation is effective and has good research prospects, providing an important technical reference for dynamic analysis and fault diagnosis of the rotor system. Full article

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis. Full article

Liquified natural gas (LNG) manipulator arms have been widely used in natural gas transportation. However, the automatic docking technology of LNG manipulator arms has not yet been realized. The first step of automatic docking is to identify and locate the target and estimate its pose. This work proposes a petroleum pipeline interface recognition and pose judgment method based on binocular stereo vision technology for the automatic docking of LNG manipulator arms. The proposed method has three main steps, including target detection, 3D information acquisition, and plane fitting. First, the target petroleum pipeline interface is segmented by using a color mask. Then, color space and Hu moment are used to obtain the pixel coordinates of the contour and center of the target petroleum pipeline interface. The semi-global block matching (SGBM) algorithm is used for stereo matching to obtain the depth information of an image. Finally, a plane fitting and center point estimation method based on a random sample consensus (RANSAC) algorithm is proposed. This work performs a measurement accuracy verification experiment to verify the accuracy of the proposed method. The experimental results show that the distance measurement error is not more than 1% and the angle measurement error is less than one degree. The measurement accuracy of the method meets the requirements of subsequent automatic docking, which proves the feasibility of the proposed method and provides data support for the subsequent automatic docking of manipulator arms. Full article

Differentiating between malignant and benign masses using machine learning in the recognition of breast ultrasound (BUS) images is a technique with good accuracy and precision, which helps doctors make a correct diagnosis. The method proposed in this paper integrates Hu’s moments in the analysis of the breast tumor. The extracted features feed a k-nearest neighbor (k-NN) classifier and a radial basis function neural network (RBFNN) to classify breast tumors into benign and malignant. The raw images and the tumor masks provided as ground-truth images belong to the public digital BUS images database. Certain metrics such as accuracy, sensitivity, precision, and F1-score were used to evaluate the segmentation results and to select Hu’s moments showing the best capacity to discriminate between malignant and benign breast tissues in BUS images. Regarding the selection of Hu’s moments, the k-NN classifier reached 85% accuracy for moment M1 and 80% for moment M5 whilst RBFNN reached an accuracy of 76% for M1. The proposed method might be used to assist the clinical diagnosis of breast cancer identification by providing a good combination between segmentation and Hu’s moments. Full article