Contour Detection Research Articles

Automatic Outer Contour Detection and Quantification of Vehicles Using Monocular Vision

Oversized vehicles have the potential to collide with walls or ceilings when passing through tunnels and bridges, posing a serious threat to the health of transportation infrastructure and public safety. Hence, it is crucial to accurately and immediately detect vehicle dimensions, including length, width, and height, to avoid such accidents. Using computer vision and view geometry, this study presents a framework for automatically detecting and quantifying the outer contours of vehicles through monocular vision. First, traffic scene images are captured which are then used to create a transformation matrix of the ground surface. Second, a modified Mask region-based convolutional neural network (Mask R-CNN) is constructed to detect and segment the vehicle instances from the video frames. Finally, a view geometry-based algorithm was developed to detect the outer contours of passing vehicles and quantify their dimensions. In the field test, the accuracy of the vehicle segmentation and the identified vehicle dimensions was validated. In addition, the proposed method’s superiority was confirmed by comparing it with two other existing approaches. The comparison results show that the proposed method has better accuracy and is more convenient to use since it does not require a premeasured reference. In addition, the developed method can accurately identify not only the dimensions of vehicles parallel to the road but also vehicles that are changing lanes or making a U-turn.

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by “burr” signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.

Algorithms for contour detection in agricultural images

Contour detection requires a more extensive use of them in agriculture. However, processing by experts on the basis of such images, including subjective visual observation of the field area, takes a lot of time and energy. One of the important parts of the image processing process is the problem of determining the contour of the object in the image, through which the objects in the image are extracted, that is, segmented. This research work is devoted to the comparative analysis of contour detection methods, and the presented methods were first tested on the basis of the original image and images whose contours were separated by experts. The test contour was performed based on the contour images extracted by the expert and the contour images generated by using the methods, and the comparison of the results was performed by the pixel comparison method. Based on the obtained results, an approach of applying the appropriate method depending on the quality of the image is proposed.

A Hands-on Guide to AmoePy - a Python-Based Software Package to Analyze Cell Migration Data.

Amoeboid cell motility is fundamental for a multitude of biological processes such as embryogenesis, immune responses, wound healing, and cancer metastasis. It is characterized by specific cell shape changes: the extension and retraction of membrane protrusions, known as pseudopodia. A common approach to investigate the mechanisms underlying this type of cell motility is to study phenotypic differences in the locomotion of mutant cell lines. To characterize such differences, methods are required to quantify the contour dynamics of migrating cells. AmoePy is a Python-based software package that provides tools for cell segmentation, contour detection as well as analyzing and simulating contour dynamics. First, a digital representation of the cell contour as a chain of nodes is extracted from each frame of a time-lapse microscopy recording of a moving cell. Then, the dynamics of these nodes-referred to as virtual markers-are tracked as the cell contour evolves over time. From these data, various quantities can be calculated that characterize the contour dynamics, such as the displacement of the virtual markers or the local stretching rate of the marker chain. Their dynamics is typically visualized in space-time plots, the so-called kymographs, where the temporal evolution is displayed for the different locations along the cell contour. Using AmoePy, you can straightforwardly create kymograph plots and videos from stacks of experimental bright-field or fluorescent images of motile cells. A hands-on guide on how to install and use AmoePy is provided in this chapter.

Automated Classification and Size Estimation of Fetal Ventriculomegaly from MRI Images: A Comparative Study of Deep Learning Segmentation Approaches

Fetal ventriculomegaly is one of the major risks in prenatal diagnosis, which is an enlargement of the ventricles of the developing fetus's brain. Timely prediction of these brain disorders helps patients and healthcare providers make informed decisions and treatment plans. This paper investigates the classification of ventriculomegaly from MRI images using a deep learning approach. Segmentation of ventricles is performed using DeeplabV3+ and U-Net architectures and the accuracy, dice coefficient, and loss of these two approaches are compared. Additionally, a novel approach for size estimation was developed, utilizing contour detection to delineate ventricle boundaries and compute their areas by enclosing them within rectangles. A threshold-based classification method was then applied to differentiate between abnormal ventriculomegaly and normal cases. This research contributes to the assessment and comparison of the segmentation algorithms for aiding in the identification and classification of ventriculomegaly cases, offering potential implications for clinical diagnosis and treatment planning.

Application of 3D Printing Technology in Customized Garment Production

Abstract In this paper, human body images are acquired by DSLR camera shooting, and the photos are grayscaled based on the HIS model. Then, the different operations weaken the similar parts between the images, and the other parts of the photos are highlighted. The image is separated from the background by image binarization. After the contour detection of the image, the pixel coordinates of the contour points in the image are deposited into a dynamic array by the size judgment of the pixel values. The subsequent size calculation is called to make a pad. According to the human body, clothing size automation measurement has been derived from the human body characteristics of the size, the use of three-dimensional printing technology to produce clothing to meet consumer demand, and three-dimensional printing technology to support the production of customized clothing under the experimental analysis. It is verified that the data difference between the actual size of the subject’s ready-made clothes and the size of the clothes produced based on 3D printing technology is kept within ±0.48cm. The virtual fitting pressure is less than the comfort threshold of women’s upper body pressure (2.75kpa), which indicates that the clothes produced by this research will not cause physiological discomfort to the human body and, at the same time, meet the consumer’s personalized clothing customization needs.

MRU-Net: A remote sensing image segmentation network for enhanced edge contour Detection

MRU-Net: A remote sensing image segmentation network for enhanced edge contour Detection

Gesture-Based Control of Multimedia Player Using Python and OpenCV

The increasing significance of computers in our daily lives, coupled with the rise of ubiquitous computing, has necessitated effective human-computer interaction. Hand gesture recognition systems have emerged as a real-time video-based solution for detecting and interpreting hand gestures, offering intelligent and natural human-computer interaction (HCI) methods. This project focuses on leveraging human hands as input devices for computer operation. Developed using Python and the OpenCV library, the program utilizes a computer webcam to capture and analyze hand shapes and patterns. The program provides real-time feedback by displaying recognized hand gestures on the live video stream. The ultimate outcome of this project is an application that enhances user experiences in contactless systems. The project recognizes and detects human hand motions using the Python computer language through a process flow that includes background subtraction, hand ROI segmentation, contour detection, and finger recognition. Techniques for processing images are used, including hand gesture detection, pattern recognition, thresholding, and contour detection. The processing of incoming photos and the creation of related keystrokes are made possible by OpenCV, a rich set of image processing tools

A CNN-based strategy to automate contour detection of the hip and proximal femur using DXA hip images from longitudinal databases (CLSA and CaMos)

ABSTRACT Hip fractures contribute significantly to mortality in older adults. New methods to identify those at risk use dual-energy X-ray absorptiometry (DXA) images and advanced image processing. However, DXA images have an overlapping femur and pelvis and may contain boundary lines, making automation challenging. Herein, a 5-layer U-net convolutional neural network (CNN) was developed to segment the femur from hip DXA images. Images were used from the Canadian Longitudinal Study on Aging (CLSA, N=104) and Canadian Multicentre Osteoporosis Study (CaMos, N=105) databases for training, with manual contour drawing defining the ‘true output’ of each image. An algorithm was then developed to mask each hip image, and trained to predict subsequent masks. The CNN was tested with 44 additional CLSA images and 42 CaMos images. This proposed approach had an accuracy and intersection over union (IoU) of 97% and 0.57, and 93% and 0.51, for CaMos and CLSA scans, respectively. Furthermore, a series of augmentation techniques was applied to increase the data size, with accuracies of 96% and 94%, and IoU of 0.53 and 0.50. Overall, our strategy automatically determined the contour of the proximal femur using various clinical DXA images, a key step to automate fracture risk assessment in clinical practice.

Applying Advanced Lightweight Architecture DSGSE-Yolov5 to Rapid Chip Contour Detection

Chip contour detection aims to detect damaged chips in chip slots during IC packaging and testing using vision facilities. However, the operation speed of the new chip transportation machine is too fast, and the current chip contour detection models, such as Yolov5, M3-Yolov5, FGHSE-Yolov5, and GSEH-Yolov5, running on the embedded platform, Jetson Nano, cannot detect chip contours in a timely manner. Therefore, there must be a rapid response for chip contour detection. This paper introduces the DSGSE-Yolov5s algorithm, which can accelerate object detection and image recognition to resolve this problem. Additionally, this study makes a performance comparison between the different models. Compared with the traditional model Yolov5, the proposed DSGSE-Yolov5s algorithm can significantly promote the speed of object detection by 132.17% and slightly increase the precision by 0.85%. As a result, the proposed approach can outperform the other methods.

Vehicle Tracking and Counting through Image Processing

Vehicle Tracking and Counting through Image Processing is an efficient system for real-time vehicle detection, classification, and counting using the powerful combination of YOLO and OpenCV. The System uses the application of machine learning in the domain of image processing for vehicle tracking and counting. OpenCV in this system is used to process the video feed or images and provide real-time visualization of the vehicles for detection. The system uses advanced computer vision techniques such as contour detection, image segmentation, and feature extraction to accurately locate and recognize individual vehicles in a scene. The detected vehicles are classified into various categories, including cars, trucks, motorcycles, and bicycles (Two & four wheelers) using the trained YOLO model. In addition to detection and classification the count of vehicles is also noted down. The primary objective of this system is to track the vehicles, distinguish and count them. This system can be used in – Traffic Signal Management and Parking Lot Monitoring. Keywords: - YOLO, OpenCV, Python, CVAT

Effect of papillary muscle and trabeculae on left ventricular function analysis via computed tomography: A cross-sectional study.

Deciding whether to include or exclude the papillary muscles and trabeculae to blood pool is essential, because quantifications of left ventricular (LV) functional parameters and myocardial mass are significantly affected. As a result, such inclusion or exclusion might produce different indices for diagnosis and therapy. Using cardiac computed tomography (CT), we obtained standard values of the portion of papillary muscle and trabeculae in normal adults, and to find out how the inclusion or exclusion of papillary muscle and trabeculae affect LV functional parameters depending on the patient group. Excluding the papillary muscles from the LV mass results in easier automated contour detection using CT. The percentage portions of papillary muscle and trabeculae to LV end-diastolic volume (EDV) and LV mass (LVM) were 11.9 ± 5.6% and 20.2 ± 4.3%, respectively, significantly affecting disease diagnosis. Imaging should be consistent at follow-up and include or exclude the papillary muscles and trabeculae to avoid introducing significant differences between measurements.

Printed circuit board solder joint quality inspection based on lightweight classification network

AbstractSolder joint quality inspection is a crucial step in the qualification inspection of printed circuit board (PCB) components, and efficient and accurate inspection methods will greatly improve its production efficiency. In this paper, we propose a PCB solder joint quality detection algorithm based on a lightweight classification network. First, the Select Joint segmentation method was used to obtain the solder joint information, and colour space conversion was used to locate the solder joint. The mask method, contour detection, and box line method were combined to complete the extraction of solder joint information. Then, by combining the respective characteristics of convolutional neural network and Transformer and introducing Cross‐covariance attention to reduce the computational complexity and resource consumption of the model and evenly distribute the global view mutual information in the whole training process, a new lightweight network model MobileXT is proposed to complete defect classification. Only 16.4% of the Vision Transformer computing resources used in this model can achieve an average accuracy improvement of 31%. Additionally, the network is trained and validated using a dataset of 1804 solder joint images constructed from 93 PCB images and two external datasets to evaluate MobileXT performance. The proposed method achieves more efficient localization of the solder joint information and more accurate classification of weld joint defects, and the lightweight model design is more appropriate for industrial edge device deployments.

Abstract 17867: Inward Displacement: A Novel CMR Technique for Quantification of Myocardial Wall Motion in Patients With Myocardial Infarction

Introduction: Inward displacement (InD) is a novel parameter which quantifies myocardial wall motion (WM) by measuring the distance travelled by endocardial tracked points towards their corresponding center of the left ventricle in systole, calculated from 3 LAX views obtained during CMR. Hypothesis: InD using CMR is capable of obtaining global quantifiable measurement of WM abnormalities in patients with myocardial infarction (MI) as defined by Late Gadolinium Enhancement (LGE). Methods: We compared patients with history of MI documented by LGE CMR.Studies were acquired in a 1.5 T Siemens Aera (Siemens, Erlangen Germany) scanner with an 18-channel body array coil; Multi-planar ECG-gated cine CMR was obtained using a balanced steady-state free precession readout and LGE images were acquired using an IR prepped sequence 10 min after Gadolinium contrast injection. Offline analysis using automated endocardial LV contour detection was conducted for each of the LAX series and manual corrections were performed as required. InD was measured in mm and expressed as a percentage. Values were compared between MI patients and normal subjects, and analyzed using a Medis Suite workstation (Medis Medical Imaging BV, Leiden, The Netherlands). Results: We analyzed 646 segments in 38 patients of which 9 had past history of myocardial infarction. Global InD for healthy subjects was 28% vs 19% SEM 0.97, as compared to patients with MI (p:< 0.00001). Conclusions: InD is capable of quantification of abnormal motility in subjects with myocardial infarction, therefore producing an objective measure of LV segmental WM abnormalities, further studies are warranted.

An Efficient Libed and GBLRU-Based Solar Panel Hotspot Detection System Using Thermal Images

In the Photovoltaic (PV) system, monitoring, assessing, and detecting the occurred faults is essential. Autonomous diagnostic models are required to examine the solar plants and to detect the anomalies within these PV panels since the prevailing hotspot detection models were unable to detect the faults rapidly and consistently. A novel Log Inverse Bilateral Edge Detector (LIBED) and Gated Bernoulli Logmax Recurrent Unit (GBLRU)-centered Solar Panel (SP) hotspot detection scheme is proposed in this research that analyzed the operating PV module’s thermal images. Images are applied for the image processing steps prior to hotspot detection. By utilizing the Contrast Limited Adaptive Histogram Equalization (CLAHE) model, the image’s contrast has been augmented in the image processing step. The alpha (α) Modified Histogram Blending (αMHB) method is utilized to eliminate the outlier data available in the image. Subsequently, an effective LIBED contour detection method was utilized to detect the SP. Several features are extracted by utilizing the detected panels. Then, optimal features are chosen as of the extracted features by utilizing the Barnacles Mating Optimizer (BMO) algorithm. The GBLRU was utilized to predict the defective panels. The defective panels’ hotspots were isolated by utilizing the Haversine Self-Organizing Map (HSOM) model. The experimental evaluation of the proposed system’s performance is analyzed with the prevailing classifiers. The state-of-art methods were outperformed by the proposed GBLRU-based Hotspot detection system. The efficiency 94.34%, accuracy 97.23%, hot-spot detection rate 91.23% had been attained which were improved outcomes compared to existed models.

Association of Chondrolabral Lesions with Ultrasound-Guided Detection of Pathological Head-Neck Contour.

This study aimed to investigate whether the asphericity of the neck-head junction of the femur confirmed via ultrasound is associated with further pathology due to femoro-acetabular impingement (FAI). After a clinical examination with positive FAI tests, an ultrasound examination of the hip was performed. In the case of asphericity, a quantitative ultrasound-assisted assessment of the hip was performed, followed by contrast-enhanced arthro-MRI with the question of cartilage or labral damage. We included 51 patients with a mean age of 35.25. According to the examination algorithm, asphericity was present in all patients via ultrasonography. The average anterior alpha angle (AAA) determined in ultrasonography was 43.49°. The average AAA on the arthro-MRI was 44.19°. The mean anterior head neck offset (AHNO) in ultrasound was 5.27 mm, and in arthro-MRI, it was 5.36 mm. Arthro-MRI confirmed a bump in 47 patients and a talization disorder in 4 patients. In 49 patients, a labral lesion was found, with one being a re-rupture. Furthermore, in one patient, labral degeneration was identified. Cartilage damage to the hip joint was found in 25 patients. Two patients had neither labral nor cartilage damage in the arthro-MRI. In our study, sonographically confirmed asphericity of the head-neck junction was found in 49 cases, which was associated with further pathology and, according to the current doctrine, was attributable to the FAI and required surgical intervention. This study shows that the detection of a pathologic head and neck contour via ultrasound in combination with positive clinical signs, as present in FAI, is associated with chondrolabral lesions detected via arthro-MRI in 96.1% of cases.

Measurement of Molten Steel Level Using a Single Camera in Top Side-Pouring Twin-Roll Casting

The molten steel level in twin-roll strip casting (TRC) has a significant impact on the heat transfer process between the molten steel and the rolls, as well as the subsequent solidification process of the steel. Therefore, ensuring a specific and stable molten steel level is crucial for the quality of as-casting strips. To achieve this, a precise and real-time molten steel level detection system is required. This paper utilizes machine vision technology to measure the molten steel level. A general mathematical model for the molten steel level in the TRC process is established. An image processing method for measuring the molten steel level using a single camera is proposed, including image segmentation, edge detection, and multiple coordinate transformations of the molten pool contour. The adverse effect of the inlet or nozzle is taken into account. Experimental measurements were conducted, and the results indicate that a single camera can accurately measure the molten steel level. Potential sources of error or limitations that may impact the accuracy of the proposed method is discussed.

Real‐time detection of Angelica dahurica tablet using YOLOX_am

AbstractIn the production of Angelica dahurica tablet (ADT), the manual sorting approach often leads to inefficiency, inconsistent standards, and subjective grading results. The traditional machine vision‐based sorting method, while helping to reduce the demand for labor in factories, suffers from problems such as incomplete contour detection and poor classification of dahurica tablets. To address the above problems, this paper proposes YOLOX_am, a novel deep learning‐based network that combines the fast detection ability of YOLOX and the feature weighting ability of the attention mechanism. In addition, a real‐time sorting system for dahurica tablets is also built and YOLOX_am is deployed in it. The experimental results show that the mAP of ADT's detection using the proposed model reaches 83.53%, which outperforms the original network YOLOX by 4.88%. Moreover, the detection speed of YOLOX_am reaches 1390 ms per image, which meets the requirement of real‐time sorting. Therefore, the combination of YOLOX and the attention mechanism is feasible and effective. YOLOX_am is both fast and accurate for ADT's detection and can be deployed in the sorting system to meet actual production needs.Practical applicationsThe traditional classification of defects in herbal tablets mainly relies on manual detection. This paper introduces a deep learning method into the sorting of herbal tablets. To solve the problem of subtle differentiation of tablets' defects, a neural network structure that can quickly and accurately detect small defective samples is proposed. This network has been applied to practical production and achieved high effectiveness.

تجربة عن الاستشهادات

We demonstrate a method to extract the heart outlines from noisy echocardiograph data in this paper. In order to eliminate significant noise and obtain improved image quality, our method is based on enhancing the image before applying contour detection. To accomplish that, we combine a variety of pre-processing techniques (filtering, morphological operations, and contrast adjustment) to enhance low contrast in echocardiograph images and avoid unclear edges. After applying these techniques, we are able to detect the boundaries of the heart and the movement of the valves using conventional edge detection techniques.

Advances on the detection and measurement of bubble contours during subcooled boiling in microgravity

A method for the detection of bubble contours in images of subcooled boiling in microgravity is proposed. The method refines an initial, rough contour approximation obtained from conventional background subtraction. This refinement entails the warping of an elliptical ring image region into a polar image, where bubble contour detection becomes simpler. The proposed method exhibits robustness to a wide variety of visual phenomena that typically hinder the detection of bubble contours. This is achieved by the grouping of continuous image edges. A ground truth annotated data set is provided that enables the quantitative and comparative evaluation of the proposed methods.