Image Difference Method Research Articles

A two-stage defect detection method for unevenly illuminated self-adhesive printed materials

The process of printing defect detection usually suffers from challenges such as inaccurate defect extraction and localization, caused by uneven illumination and complex textures. Moreover, image difference-based defect detection methods often result in numerous small-scale pseudo defects. To address these challenges, this paper proposes a comprehensive defect detection approach that integrates brightness correction and a two-stage defect detection strategy for self-adhesive printed materials. Concretely, a joint bilateral filter coupled with brightness correction corrects uneven brightness properly, meanwhile smoothing the grid-like texture in complex printed material images. Then, in the first detection stage, an image difference method based on a bright–dark difference template group is designed to effectively locate printing defects despite slight brightness fluctuations. Afterward, a discriminative method based on feature similarity is employed to filter out small-scale pseudo-defects in the second detection stage. The experimental results show that the improved difference method achieves an average precision of 99.1% in defect localization on five different printing pattern samples. Furthermore, the second stage reduces the false detection rate to under 0.5% while maintaining the low missed rate.

Characterization and depth detection of internal delamination defects in CFRP based on line laser scanning infrared thermography

In this article, the shape characterization and depth detection of delamination defects in carbon fiber reinforced polymer (CFRP) sandwich are achieved by detecting delamination defects through line laser infrared thermography. In terms of defect shape characterization, the temperature matrix difference imaging method is proposed to characterize the shape of internal defects, and the defect shape distortion is corrected by the principal component analysis algorithm to optimize the effect of defect shape characterization. In terms of defect depth detection, this study constructs a dataset of cooling curves for defects of different depths and builds an encoder–decoder convolutional neural network structure based on the attention mechanism, which is centered on a streamlined encoder–decoder structure as well as a highly integrated attention gate, which is capable of accelerating the extraction of features and reducing the number of iterations of the algorithm. The results demonstrate that we achieved an average classification accuracy of 88.9% for defects with a depth gradient difference of 0.2 mm, attaining the highest classification accuracy of 95% for a single defect. Furthermore, our approach outperformed several classical classifiers by achieving superior accuracy with fewer iterations. The experimental results demonstrate that the line laser scanning inspection method is effective in the task of detecting internal defects in CFRP materials.

Rib region detection for scanning path planning for fully automated robotic abdominal ultrasonography.

Scanning path planning is an essential technology for fully automated ultrasound (US) robotics. During biliary scanning, the subcostal boundary is critical body surface landmarks for scanning path planning but are often invisible, depending on the individual. This study developed a method of estimating the rib region for scanning path planning toward fully automated robotic US systems. We proposed a method for determining the rib region using RGB-D images and respiratory variation. We hypothesized that detecting the rib region would be possible based on changes in body surface position due to breathing. We generated a depth difference image by finding the difference between the depth image taken at the resting inspiratory position and the depth image taken at the maximum inspiratory position, which clearly shows the rib region. The boundary position of the subcostal was then determined by applying training using the YOLOv5 object detection model to this depth difference image. In the experiments with healthy subjects, the proposed method of rib detection using the depth difference image marked an intersection over union (IoU) of 0.951 and average confidence of 0.77. The average error between the ground truth and predicted positions was 16.5 mm in 3D space. The results were superior to rib detection using only the RGB image. The proposed depth difference imaging method, which measures respiratory variation, was able to accurately estimate the rib region without contact and physician intervention. It will be useful for planning the scan path during the biliary imaging.

Pre-natal diagnosis of pulmonary sequestration

Pulmonary sequestration (PS) consists of a rare lung malformation, characterized by a mass of non-functional lung tissue, which does not maintain normal communication with the tracheobronchial tree and whose blood supply is from an anomalous systemic artery. Its prenatal diagnosis is still rare, usually performed in centers of reference by trained professionals. The following is a case report of a prenatal diagnosis of pulmonary sequestration, correlating the pre and postnatal findings in different methods of diagnostic imaging.

Effects of Groundwater Depth on Vegetation Coverage in the Ulan Buh Desert in a Recent 20-Year Period

As a typical desert in the Inner Mongolia Autonomous Region, the Ulan Buh Desert has a dry climate and scarce precipitation all year round. Groundwater has become the main factor limiting the growth of vegetation in this region. It is of great significance to study the influence of groundwater depth on the spatial distribution pattern of vegetation in this region. Based on the PIE-Engine platform and using long-term time-series Landsat data, this paper analyzed the spatial–temporal distribution characteristics and trends in vegetation coverage in the Ulan Buh Desert in the last 20 years using a pixel dichotomy model and the image difference method. The Kriging interpolation method was used to interpolate the groundwater depth data from 106 monitoring wells in the Ulan Buh Desert over the past 20 years, and the spatial distribution characteristics of groundwater depth in the Ulan Buh Desert were analyzed. Finally, the correlation coefficient between changes in vegetation coverage and changes in groundwater depth was calculated. The results showed the following: (1) The vegetation coverage in the Ulan Buh Desert was higher in the periphery and lower in the center of the desert. The overall vegetation level showed an increasing trend year by year; the growth rate was 4.73%/10 years, and the overall vegetation cover showed an improving trend. (2) The overall groundwater depth in the Ulan Buh Desert was deep in the southwest and shallow in the northeast. In the past 20 years, the groundwater depth in the Ulan Buh area has become shallower, and the ecological condition has gradually improved. (3) On the whole, the vegetation coverage varied with the groundwater depth, and the shallower the groundwater depth, the greater the vegetation coverage. When the groundwater depth increased to more than 4 m, the change in the groundwater depth had a significant effect on the vegetation coverage. However, when the groundwater depth was greater than 6 m, the change in the groundwater depth had no significant effect on the change in vegetation coverage.

Beyond the Limits of Light: An Application of Super-Resolution Confocal Microscopy (sCLSM) to Investigate Eocene Amber Microfossils.

Amber is known as one of the best sources of fossil organisms preserved with exceptional fidelity. Historically, different methods of imaging have been applied to amber, including optical microscopy and microtomography. These methods are sufficient to resolve millimeter-scaled fossils. However, microfossils, such as microarthropods, require another resolution. Here, we describe a non-destructive method of super resolution confocal microscopy (sCLSM) to study amber-preserved microfossils, using a novel astigmatid mite species (genus Histiogaster, Acaridae) from Eocene Rovno amber as a model. We show that the resolution obtained with sCLSM is comparable to that of scanning electron microscopy (SEM) routinely used to study modern mites. We compare sCLSM imaging to other methods that are used to study amber inclusions and emphasize its advantages in examination of unique fossil specimens. Furthermore, we show that the deterioration of amber, which manifests in its darkening, positively correlates with its increased fluorescence. Our results demonstrate a great potential of the sCLSM method for imaging of the tiniest organisms preserved in amber.

Computed tomography with adjusted dose for body mass index may be superior to whole-body radiography especially in elderly patients with multiple myeloma.

Whole-body skeletal radiography has traditionally been used in the management of multiple myeloma for defining treatment strategies. For several reasons, radiography has been replaced by computed tomography (CT) covering the same regions. To evaluate the body mass index (BMI) adjusted effective radiation dose from two different methods of whole-body radiologic imaging for multiple myeloma assessment. The current investigation analyses the dose to patients resulting from the two methods, conventional radiography supplemented with tomosynthesis (203 examinations) and CT (264 examinations). All patients subject to myeloma staging for 4.5 years were included in the study. Exposure parameters were collected from the PACS and conversion factors were calculated using the software packages PCXMC and VirtualDose enabling the calculation of the effective dose to each patient based on BMI. The Mann-Whitney U test was used for comparisons between groups. Patients were subject to a median effective dose of 2.5 mSv for conventional radiography and 5.1 mSv for CT, a statistically significant difference. The effective dose for whole-body CT in assessing multiple myeloma is twice as high as for whole-body skeletal survey with modern digital radiography, but at a low level and considerably less than the levels quoted in the earlier studies of ∼30 mSv when the technique was first explored.

An Enhancement Process for Multi-focus Images Resulted from Image Fusion using qshiftN DTCWT and MPCA in Multiresolution Domain

Images having different focus values are combined using image fusion methodologies. The fused image shows superiority in the quality and highly informative than compared o any other multifocus images. The fused image would show its suitability for visual perception object detection due to its high quality than compared to multifocus images. Among the various quality parameters of the fused image, directional invariance and shift invariance significantly influence the fused image quality. The traditional image fusion methods using wavelets severely suffers with poor invariance and lack of directionality. An efficient image fusion method is developed using DTCWT with qshiftN and MPCA algorithms in the multiresolution (MR) domain. Multifocus input images are decomposed into high and low frequency components using MR algorithm. The decomposed frequency components of the input images are fused using DTCWT with qshiftN algorithm. The proposed fusion algorithm preserves both sift invariance and directional properties of the multifocus source image. Lastly, the fused image is processed using MPCA algorithm to enhance the features. The proposed methodology is assessed using various multifocus images and the evaluated metrics are contrasted with technologically advanced algorithms reported recently. The statistical metrics evaluated for the proposed method for different multifocus images shows superior properties than compared to the recently reported multifocus image fusion algorithms.

Research on polarization characteristics of background light by modified polarization difference imaging method

<sec>The random scattering event of light by water medium is the primary reason for the degradation in underwater imaging. Underwater polarization imaging technology can enhance the signal-to-noise ratio of imaging effectively by utilizing the polarization information difference between background scattered light and target light. However, as scattering events increase in the water body, it is difficult to maintain the polarization characteristics of light, which reduces the effect of removing scattering based on polarization characteristics. In addition, the polarization rule of background scattered light in water is unclear, and there is a lack of quantitative description of the polarization characteristics of scattered light. Therefore, the study of polarization transmission characteristics of underwater scattered light is of great significance in reducing the scattering light of underwater polarization imaging.</sec><sec>In order to clarify the polarization characteristics of underwater background scattered light, especially the polarization angle information, this paper proposes a method for ascertaining polarization angle of background light based on modified polarization difference imaging method. In this method, the coupling relationship between optimal weight coefficient and enhancement measure evaluation (EME) value of the Stokes vector difference result is analyzed, and the background light polarization angle is calculated based on the optimal weight coefficient. Combined with the experimental results, the EME distribution trend of the optimal weight coefficient and the modified polarization difference imaging method results in different turbidity water bodies are determined, the scattering suppression limit is explored, and the trend of background scattered light polarization direction with turbidity of water is analyzed. The results show that the proposed method can obtain the exact polarization angle of background scattered light in different water environments, revealing a trend that the polarization direction of background scattered light becomes orthogonal to the incident light direction as the turbidity of the water increases. This research provides a methodological basis for determining the polarization direction of the background scattered light in underwater imaging.</sec>

Visualization and Characterization of Cumulative Arc Erosion Behavior Using 2-D Microscopic Images

The electrical arc erosion behavior extremely limits the lifespan of electromechanical switching devices. Observation of the gradual evolution of arc erosion is highly beneficial for to understand the arc ignition and the contact degradation. In this article, the erosion of contact materials is simulated and visualized in situ with the recorded 2-D microscopic images. A novel method for erosion area identification is proposed with the combination of difference image method and mathematical morphology operations. The variation in erosion area as a function of switching current cycles is demonstrated, and the center of erosion zone that migrates continuously during the endurance experiment is determined. Finally, not only three types of arc erosion regimes (centralized arc erosion, separated arc erosion, and commutated arc erosion) are revealed, but also the associated occurrence probabilities during 1000 cycles are provided.

Investigating the Sensitivity of New Formulation MAGAT and NIPAM Polymer Gels in the Radiation Therapy Dosimetry.

Background: Normoxic polymer gels have been used as a three dimensional (3D) dosimeter in radiation therapy, recently. The sensitivity of these gels is important in dosimetry and their improvement can be also useful.Objective: In this study, different modalities of gel reading were used and the structure of gel changed due to the best improvement of sensitivity. The sensitivities of the new formulation of Methacrylic acid gel (MAGAT) and N-isopropyl acrylamide (NIPAM) polymer gel dosimeters were studied using two different reading methods of magnetic resonance imaging (MRI) and X-ray computed tomography (X-ray CT).Material and Methods: In this experimental study, in addition to making the NIPAM polymer gel dosimeter, a new formulation of normoxic polymer gel dosimeter, which named MAGAT gel, was investigated. The gels were irradiated with 6 MV in low doses, including1, 1.5, 1.75, 2 and 2.5 Gy. MRI and X-ray CT did the reading of gel dosimeters a day after irradiation using an elevated protocol.Results: The dose sensitivities of 0.92 HGy-1 and 0.47 HGy-1 were obtained for new MAGAT and NIPAM polymer gel dosimeters, respectively, based on the X-ray CT reading modality. The use of MRI reading modality and the dose sensitivities were 0.74 S-1Gy-1 and 0.27 S-1Gy-1 for new MAGAT and NIPAM polymer gel dosimeters, respectively.Conclusion: The new formulation of MAGAT polymer gel with a suitable protocol of gel reading has a better response.

643. PRACTICAL IMAGING REVIEW OF EXPECTED FINDINGS AND POSSIBLE COMPLICATIONS RELATED TO MULTIMODAL TREATMENT OF ESOPHAGEAL CANCER

Abstract Esophageal cancer has been a major health issue, representing the eighth most common cancer worldwide. In the last decades, esophageal cancer treatment has evolved rapidly and a multimodality approach has been increasingly used. Main treatment options for locally advanced tumors include surgery resection, neoadjuvant or definitive chemoradiotherapy and palliative endoscopic dilation/stenting. In that context, radiology plays an important role in the early identification and management of treatment related complications. Retrospective analysis of the radiological exams of esophageal cancer patients in the ICESP (Cancer Institute of Sao Paulo) followed by selection of several cases (more than 300 cases) illustrating the expected findings and the main complications related to each treatment modality (surgical, endoscopic and radiotherapy-related). Five main different complications were selected and the radiological findings were classified using the HARMS mnemonic: Hiatal hernia; Anastomotic fistula, Radiation-induced injury, Mediastinal and pleural-pulmonary abscess and stenting-related injuries. For each classification, several images were selected using the different methods of esophageal imaging - mainly upper gastrointestinal series and computed tomography. After categorization it is possible to recognize and familiarize with post-treatment complications using the “HARMS” mnemonic. Therefore, we present a review for the radiologists describing what is important to report as well as a compilation of possible image findings that oncologists, surgeons and endoscopists must be aware of.

Change Detection Based on Fusion Difference Image and Multi-Scale Morphological Reconstruction for SAR Images

Synthetic aperture radar (SAR) image-change detection is widely used in various fields, such as environmental monitoring and ecological monitoring. There is too much noise and insufficient information utilization, which make the results of change detection inaccurate. Thus, we propose an SAR image-change-detection method based on multiplicative fusion difference image (DI), saliency detection (SD), multi-scale morphological reconstruction (MSMR), and fuzzy c-means (FCM) clustering. Firstly, a new fusion DI method is proposed by multiplying the ratio (R) method based on the ratio of the image before and after the change and the mean ratio (MR) method based on the ratio of the image neighborhood mean value. The new DI operator ratio–mean ratio (RMR) enlarges the characteristics of unchanged areas and changed areas. Secondly, saliency detection is used in DI, which is conducive to the subsequent sub-area processing. Thirdly, we propose an improved FCM clustering-change-detection method based on MSMR. The proposed method has high computational efficiency, and the neighborhood information obtained by morphological reconstruction is fully used. Six real SAR data sets are used in different experiments to demonstrate the effectiveness of the proposed saliency ratio–mean ratio with multi-scale morphological reconstruction fuzzy c-means (SRMR-MSMRFCM). Finally, four classical noise-sensitive methods are used to detect our DI method and demonstrate the strong denoising and detail-preserving ability.

Characterization of the SKA1-Low prototype station Aperture Array Verification System 2

The low frequency component of the Square Kilometre Array (SKA1-Low) will be an aperture phased array located at the Murchison Radio-astronomy Observatory (MRO) site in Western Australia. It will be composed of 512 stations, each consisting of 256 log-periodic dual-polarized antennas, and will operate in the low frequency range (50 to 350 MHz) of the SKA bandwidth. The Aperture Array Verification System 2 (AAVS2), operational since late 2019, is the last full-size engineering prototype station deployed at the MRO site before the start of the SKA1-Low construction phase. The aim of this paper is to characterize the station performance through commissioning observations at six different frequencies (55, 70, 110, 160, 230, and 320 MHz) collected during its first year of activities. We describe the calibration procedure, present the resulting all-sky images and their analysis, and discuss the station calibratability and system stability. Using the difference imaging method, we also derive estimates of the SKA1-Low sensitivity for the same frequencies and compare them with those obtained through electromagnetic simulations across the entire telescope bandwidth, finding good agreement (within 13%). Moreover, our estimates exceed the SKA1-Low requirements at all considered frequencies by up to a factor of ∼2.3. Our results are very promising and allow for an initial validation of the AAVS2 prototype station performance, which is an important step toward the coming SKA1-Low telescope construction and science.

DIFFERENTIAL DIAGNOSIS OF SOFT TISSUE TUMORS.

The aim of the study is to identify clinical signs of neurilemmoma of the upper limb, its main ultrasound and X-ray distinctions from other benign tumors of soft tissues. To make a preliminary diagnosis, it is advisable to use different methods of imaging, such as: ultrasound, MRI, radiography. Radiological diagnosis of soft tissue tumors should be based on the integrated use of radiological and ultrasound methods with mandatory consideration of clinical and anamnestic data such as patient age, topographic location of the tumor and its growth rate. Although ultrasound and MRI do not allow to completely differentiate the tumor, but they help to choose the right tactics of the patient. Treatment is to remove the tumor and includes cesium dissection and removal from the nerve bundle, using magnification.

Forest Disaster Detection Method Based on Ensemble Spatial–Spectral Genetic Algorithm

Remote sensing images change detection is the key technology for monitoring forest windfall damages. Genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional genetic algorithms remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize contextual, and hierarchical information of image objects in addition to solely using spectral information. Additionally, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with spatial-based GA offers a promising method (E-nGA) for automating the process of monitoring forest loss. The research in this paper is presented in four parts. First, block-matching and 3D filtering is performed to suppress noises while enhancing valuable information. The difference image is then generated using the image difference method. Afterward, context-based saliency detection and Fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently-used classification methods, as well as the simple genetic algorithm, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

Retracted] Design of Sports Image Contour Feature Acquisition System Based on the Background Subtraction Method

Moving target detection (MTD) is one of the emphases and difficulties in the field of computer vision and image processing. It is the basis of moving target tracking and behavior recognition. We propose two methods are improved and fused, respectively, and the fusion algorithm is applied to the complex scene for MTD, so as to improve the accuracy of MTD in complex and hybrid scenes. Using the main idea of the three‐frame difference image method, the background difference method and the interframe difference method are combined to make their advantages complementary to overcome each other’s weaknesses. The experimental results show that the method can be well adapted to the situation of periodic motion interference in the background, and it can adapt to the situation of sudden background changes.

Real-time detection of particleboard surface defects based on improved YOLOV5 target detection

Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, mAP@.5, mAP@.5:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.

Optimization of Edge Extraction Algorithm for Objects in Complex Background

In the field of artificial intelligence, machine vision is expected because of its low cost and easy popularization. Feature extraction is one of the core steps of machine vision. When we use Canny algorithm, which extracts edges by gray level change, we cannot directly separate the object from the background. The edge information of the object will also be lost when the Gaussian filter smoothes the image. The research proposes an improved edge extraction algorithm based on Canny algorithm. We use the image difference method and object envelopment method to separate the object from the background, so the data to be processed is greatly reduced and the speed and accuracy of the algorithm are improved. Then we use bilateral filtering instead of Gaussian filtering, which increases the weight of gray difference on the basis of Gaussian filtering, so we can to retain more edge information. Finally, we improve the gradient operator, by determining the gradient operator coefficient according to the inverse ratio of the Euclidean distance between the pixel and the center pixel. The average performance of the new gradient operator in the center of the picture is better than that of Sobel operator. Experimental simulation shows that the algorithm has good detection accuracy. Compared with traditional algorithms, the improved algorithm is not affected by complex backgrounds, and reduce the influence of light. The improved algorithm has clear edges in the center area of the image.

Features of implementation of the method for fusion different images by location equipment of on-board systems

The paper highlights the features of the implementation of on-board systems for monitoring the earth’s surface, based on small aircraft. The problems of fusion of location information from on-board monitoring systems of various types of functioning are considered. The motivation of integrating optical and radar images of the earth’s surface is shown. The result of the work is the synthesis of optical and radar images of the earth’s surface into a single information field.