Occlusal Area Research Articles

A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images

Currently, it is a hot research topic to retrieve the wave parameters by using X-band marine radar. However, the rainfall noise usually exists in the collected marine radar images, which seriously interferes with the extraction of the wave parameters. To reduce the influence of rainfall noise, the zero-pixel percentage (ZPP) method is widely used to detect rainfall in radar images, but the detection accuracy is limited, and the selection of the threshold needs to be further studied. Based on the ZPP method, the ratio of zero intensity to echo (RZE) method for rainfall detection is proposed in this paper. The detection threshold is determined by statistical analysis of a large amount of radar data. Additionally, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. In addition, the concept of the occlusion area is proposed. The proposed area and the wave area are used as the rainfall detection area of the radar image, respectively, for experimental research. The data obtained from the Pingtan experimental base in Fujian Province are used to verify the effectiveness of the proposed method. The experimental results show that the detection accuracy of the proposed method is 11.7% higher than that of the ZPP method, and the accuracy of rainfall intensity level retrieval is 84%.

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility.

Liposomal cytarabine and daunorubicin (CPX-351/Vyxeos)–associated distinct purpuric subtype of toxic erythema of chemotherapy: A retrospective review of 54 patients

Liposomal cytarabine and daunorubicin (CPX-351/Vyxeos)–associated distinct purpuric subtype of toxic erythema of chemotherapy: A retrospective review of 54 patients

Analysis and modeling of dust accumulation-composed spherical and cubic particles on PV module relative transmittance

PV modules are exposed to outdoor environments where dust accumulation affects optical performance. In this paper, a mathematical model is proposed to predict the impact of dust accumulation based on the shape of dust particles on the relative transmittance of PV. The model considers the light path into the dust particle. The changes of the light path are simulated through Tracepro soft. The variation of the relative transmittance with the dust-deposited density is tested and verified based on an experiment of artificial dust distribution. The results indicated that the dust of cubic particles caused a larger decrease in the relative transmittance compared with the dust of spherical particles. The relative transmittance of the spherical dust decreases from 100% to 77.64%, while that of the cubic dust decreases from 100% to 65.53%, when dust-deposited density is 10 g/m2. However, the opposite trend is seen when the light-receiving area is equal to the occlusion area of a dust particle in the simulation. The maximum relative deviation between the mathematical model and the experimental results was only 1%.

Iatrogenic allergic contact dermatitis in the (peri)anal and genital area.

Allergic contact dermatitis (ACD) from topical medication often occurs in occluded areas, for example, with wound treatment, but also in certain body locations, such as the anogenital area. To investigate the demographics and specific lesion location of patients with ACD from topical drugs applied onto the (peri)anal/genital area, and to identify the respective causal topical pharmaceutical products and ingredients involved. From January 2000 to December 10, 2018, 532 patients were tested with the baseline series, sometimes with additional series, and the topical medication used along with the ingredients. The relevant data were extracted from our electronic databases developed in-house. Forty-four patients (9%) out of 473 patients suffering from lesions in the (peri)anal/genital area had positive patch test results to topical drug preparations and/or their ingredients, sometimes in association with cosmetics for intimate hygiene. The most frequent sensitizing active principles were local anaesthetics and corticosteroids, while wool alcohols and to a minor extent benzoic acid were the most frequent culprits among the vehicle components and preservative agents, respectively. The local conditions (eg, occlusion, sweating, moist) in the anogenital area may favour skin sensitization to topical medication used to treat various skin diseases.

A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment.

Simple SummaryThe equipment in the poultry house can occlude top view images of broiler chickens and limit the efficiency of vision-based target detection. In this study, we sought to improve the efficiency of a previously developed method to detect and restore broiler chicken areas blocked by feeders and drinkers. To do this, we developed and tested linear and elliptical fitting restoration methods under different occlusion scenarios to restore occluded broiler chicken areas. The restoration method correctly restored the occluded broiler chicken area >80% of the time. This study provides a practical approach to enhancing the image quality in applying a machine vision-based method for monitoring poultry health and welfare.The presence equipment (e.g., water pipes, feed buckets, and other presence equipment, etc.) in the poultry house can occlude the areas of broiler chickens taken via top view. This can affect the analysis of chicken behaviors through a vision-based machine learning imaging method. In our previous study, we developed a machine vision-based method for monitoring the broiler chicken floor distribution, and here we processed and restored the areas of broiler chickens which were occluded by presence equipment. To verify the performance of the developed restoration method, a top-view video of broiler chickens was recorded in two research broiler houses (240 birds equally raised in 12 pens per house). First, a target detection algorithm was used to initially detect the target areas in each image, and then Hough transform and color features were used to remove the occlusion equipment in the detection result further. In poultry images, the broiler chicken occluded by equipment has either two areas (TA) or one area (OA). To reconstruct the occluded area of broiler chickens, the linear restoration method and the elliptical fitting restoration method were developed and tested. Three evaluation indices of the overlap rate (OR), false-positive rate (FPR), and false-negative rate (FNR) were used to evaluate the restoration method. From images collected on d2, d9, d16, and d23, about 100-sample images were selected for testing the proposed method. And then, around 80 high-quality broiler areas detected were further evaluated for occlusion restoration. According to the results, the average value of OR, FPR, and FNR for TA was 0.8150, 0.0032, and 0.1850, respectively. For OA, the average values of OR, FPR, and FNR were 0.8788, 0.2227, and 0.1212, respectively. The study provides a new method for restoring occluded chicken areas that can hamper the success of vision-based machine predictions.

HSPA12B Expression as an Epigenetic Signature Following Ischemic Stroke: Heat Shock Protein Mini Review

Stroke is a cardiovascular condition that caused by a clot or obstruction that prevents blood from flowing to the brain. Neural cells within the occluded area undergo cell death within minutes, and the results are often permanent, making stroke one of the leading causes of serious longterm disability. Stroke is a complex disorder that requires innovative solutions to attenuate the effects of stroke and improve clinical outcomes. Traditionally, stroke treated by relieving vascular occlusions and restoring blood flow to the infarcted tissue (necrotic tissue; core infarct) and the surrounding tissue at risk (penumbra). This technique known as thrombolysis, utilizing both chemical and mechanical modes. Chemical thrombolysis utilizes a catheter that administers medication to the clot site, which includes a multitude of treatments that include intravenous (IV) thrombolysis with recombinant tissue plasminogen activator, or rTPA, and intra-arterial (IA) therapies that aim to directly remove the occlusive clot and reestablish blood flow through recanalization.

Development of Integrative Methodologies for Effective Excavation Progress Monitoring.

Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.

Repairing 3D Models Obtained From Range Sensors

This paper presents a method to restore 3D models created from range sensors. The approach recovers the 3D information that is lost during the scanning process, usually due to inaccessible zones on the object (occluded areas and crevices). The raw and incomplete 3D data are first projected onto a grid to identify the zones to be repaired. Our algorithm calculates a suitable projection plane for each lost area defined and restores the 2D projected image. Finally, the inverse 2D to 3D transformation is carried out, and the new 3D data are merged with the initial mesh model, providing the completely restored surface. The approach has been tested on a database containing a wide variety of models, yielding excellent results. The experimental section shows that our method works for a large diversity of non-sensed and difficult-to-access zones and provides precise restorations.

Group‐Based Atrous Convolution Stereo Matching Network

Stereo matching is the key technology in stereo vision. Given a pair of rectified images, stereo matching determines correspondences between the pair images and estimate depth by obtaining disparity between corresponding pixels. The current work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task with an end‐to‐end frame based on convolutional neural networks (CNNs). However, 3D CNN puts a great burden on memory storage and computation, which further leads to the significantly increased computation time. To alleviate this issue, atrous convolution was proposed to reduce the number of convolutional operations via a relatively sparse receptive field. However, this sparse receptive field makes it difficult to find reliable corresponding points in fuzzy areas, e.g., occluded areas and untextured areas, owing to the loss of rich contextual information. To address this problem, we propose the Group‐based Atrous Convolution Spatial Pyramid Pooling (GASPP) to robustly segment objects at multiple scales with affordable computing resources. The main feature of the GASPP module is to set convolutional layers with continuous dilation rate in each group, so that it can reduce the impact of holes introduced by atrous convolution on network performance. Moreover, we introduce a tailored cascade cost volume in a pyramid form to reduce memory, so as to meet real‐time performance. The group‐based atrous convolution stereo matching network is evaluated on the street scene benchmark KITTI 2015 and Scene Flow and achieves state‐of‐the‐art performance.

A Deep Learning Approach for Real-Time Analysis of Attendees’ Engagement in Public Events

Smart city analytics requires the harnessing and analysis of emotions and sentiments conveyed by images and video footage. In recent years, facial sentiment analysis attracted significant attention for different application areas, including marketing, gaming, political analytics, healthcare, and human computer interaction. Aiming at contributing to this area, we propose a deep learning model enabling the accurate emotion analysis of crowded scenes containing complete and partially occluded faces, with different angles, various distances from the camera, and varying resolutions. Our model consists of a sophisticated convolutional neural network (CNN) that is combined with pooling, densifying, flattening, and Softmax layers to achieve accurate sentiment and emotion analysis of facial images. The proposed model was successfully tested using 3,750 images containing 22,563 faces, collected from a large consumer electronics trade show. The model was able to correctly classify the test images which contained faces with different angles, distances, occlusion areas, facial orientation and resolutions. It achieved an average accuracy of 90.6% when distinguishing between seven emotions (Happiness, smiling, laughter, neutral, sadness, anger, and surprise) in complete faces, and 86.16% accuracy in partially occluded faces. Such model can be leveraged for the automatic analysis of attendees’ engagement level in events. Furthermore, it can open the door for many useful applications in smart cities, such as measuring employees’ satisfaction and citizens’ happiness.

A Lightweight Depth Estimation Network for Wide-Baseline Light Fields.

Existing traditional and ConvNet-based methods for light field depth estimation mainly work on the narrow-baseline scenario. This paper explores the feasibility and capability of ConvNets to estimate depth in another promising scenario: wide-baseline light fields. Due to the deficiency of training samples, a large-scale and diverse synthetic wide-baseline dataset with labelled data is introduced for depth prediction tasks. Considering the practical goal for real-world applications, we design an end-to-end trained lightweight convolutional network to infer depths from light fields, called LLF-Net. The proposed LLF-Net is built by incorporating a cost volume which allows variable angular light field inputs and an attention module that enables to recover details at occlusion areas. Evaluations are made on the synthetic and real-world wide-baseline light fields, and experimental results show that the proposed network achieves the best performance when compared to recent state-of-the-art methods. We also evaluate our LLF-Net on narrow-baseline datasets, and it consequently improves the performance of previous methods.

Enhanced Spinning Parallelogram Operator Combining Color Constraint and Histogram Integration for Robust Light Field Depth Estimation

Accurate depth estimation is an essential part of most light field applications. Previous approach Spinning Parallelogram Operator (SPO) achieves robust depth estimation results in noisy and occluded scenes by fitting lines in Epipolar Plane Images (EPIs). However, only using histogram distances, SPO cannot handle complex occlusions and is sensitive to different parameters. In this letter, we propose enhanced Spinning Parallelogram Operator utilizing color constraint and histogram integration (SPO-CH) to address the above problems. The color consistency between different views is first introduced to exclude occlusions when measuring the histogram distances of occluded areas. We then design a novel Gaussian integration histogram that combines information from other adjacent bins, which improves the performances of noisy and texture-less scenes. Experimental results show that the proposed method effectively improves the depth estimation results. Comparisons with other state-of-the-art approaches show that the proposed method achieves better results for both synthetic and real-world images.

An Energy Spectrum Algorithm for Wind Direction Retrieval From X-Band Marine Radar Image Sequences

In this study, an energy spectrum (ES) algorithm is proposed to retrieve wind direction from X -band marine radar image sequences. This algorithm is based on utilizing the occlusion area zero-pixel percentage to distinguish rain-free and rain-contaminated radar data. And then the rain-contaminated images are detected and discarded. The effect of radar radial attenuation in radar image sequences is modified by the piecewise fitting technique. Wind direction is determined from rain-free and radial correction data, based on the ES of small-scale wind streaks. The ES of small-scale wind streaks is obtained by establishing an ES scale separation filter. Based on the wind streak characteristics, a 2-D fast Fourier transform is used to obtain the ES of radar images. The wind streak characteristics are derived from the distribution of the azimuth normalization radar cross section. The proposed algorithm is tested using data collected from X -band radar images and in situ anemometer data from the coast of the East China Sea. Compared with the anemometer data, after using the proposed algorithm, the root-mean-square difference for wind direction is 12.13°, which is an acceptable result for engineering application.

True Orthoimage Generation Using Airborne LiDAR Data with Generative Adversarial Network‐Based Deep Learning Model

Orthoimage, which is geometrically equivalent to a map, is one of the important geospatial products. Displacement and occlusion in optical images are caused by perspective projection, camera tilt, and object relief. A digital surface model (DSM) is essential data for generating true orthoimages to correct displacement and to recover occlusion areas. Light detection and ranging (LiDAR) data collected from an airborne laser scanner (ALS) system is a major source of DSM. The traditional methods require sophisticated procedures to produce a true orthoimage. Most methods utilize 3D coordinates of the DSM and multiview images with overlapping areas for orthorectifying displacement and detecting and recovering occlusion areas. LiDAR point cloud data provides not only 3D coordinates but also intensity information reflected from object surfaces in the georeferenced orthoprojected space. This paper proposes true orthoimage generation based on a generative adversarial network (GAN) deep learning (DL) with the Pix2Pix model using intensity and DSM of the LiDAR data. The major advantage of using LiDAR data is that the data is occlusion‐free true orthoimage in terms of projection geometry except in the case of low image quality. Intensive experiments were performed using the benchmark datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The results demonstrate that the proposed approach could have the capability of efficiently generating true orthoimages directly from LiDAR data. However, it is crucial to find appropriate preprocessing to improve the quality of the intensity of the LiDAR data to produce a higher quality of the true orthoimages.

An Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

Deep Learning-Based Masonry Wall Image Analysis

In this paper we introduce a novel machine learning-based fully automatic approach for the semantic analysis and documentation of masonry wall images, performing in parallel automatic detection and virtual completion of occluded or damaged wall regions, and brick segmentation leading to an accurate model of the wall structure. For this purpose, we propose a four-stage algorithm which comprises three interacting deep neural networks and a watershed transform-based brick outline extraction step. At the beginning, a U-Net-based sub-network performs initial wall segmentation into brick, mortar and occluded regions, which is followed by a two-stage adversarial inpainting model. The first adversarial network predicts the schematic mortar-brick pattern of the occluded areas based on the observed wall structure, providing in itself valuable structural information for archeological and architectural applications. The second adversarial network predicts the pixels’ color values yielding a realistic visual experience for the observer. Finally, using the neural network outputs as markers in a watershed-based segmentation process, we generate the accurate contours of the individual bricks, both in the originally visible and in the artificially inpainted wall regions. Note that while the first three stages implement a sequential pipeline, they interact through dependencies of their loss functions admitting the consideration of hidden feature dependencies between the different network components. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state-of-the-art is given. The experiments confirmed that the proposed method outperforms the reference techniques both in terms of wall structure estimation and regarding the visual quality of the inpainting step, moreover it can be robustly used for various different masonry wall types.

Marginal and internal fit of three-unit fixed dental prostheses fabricated from translucent multicolored zirconia: Framework versus complete contour design

Statement of problemTranslucent multicolored zirconia materials enable more esthetic complete contour zirconia fixed dental prostheses (FDPs) than conventional zirconia, which exhibits low translucency and high opacity and is monochromatic. However, how the marginal and internal fit of translucent multicolored zirconia FDPs compare with those of traditional frameworks that require veneering is unclear. PurposeThe purpose of this in vitro study was to compare the marginal and internal fit of frameworks and complete contour 3-unit FDPs fabricated from translucent multicolored zirconia. Material and methodsFrameworks with a thickness of 0.5 mm and complete contour FDPs with a thickness of 0.8 to 1.5 mm were manufactured by using a workflow similar to one from a zirconia master model (mandibular left second premolar-mandibular left second molar). Two polyvinyl siloxane replicas were made for each specimen to measure the marginal and internal fit. Measurement locations were mesial, lingual, buccal, and distal for each abutment. In these locations, the marginal opening (MO), chamfer area (CA), axial wall (AW), and occlusal area (OC) were measured. The data were analyzed with 2-way ANOVA and the Bonferroni post hoc test (α=.05). ResultsFrameworks showed significantly better mean ±standard deviation fit values than complete contour 3-unit FDPs at measurement areas MO (frameworks: 112 ±22 μm, complete contour FDPs: 144 ±37 μm) (P=.013), CA (frameworks: 89 ±12 μm, complete contour FDPs: 110 ±22 μm) (P=.006), and OC (frameworks: 182 ±36 μm, complete contour FDPs: 244 ±64 μm) (P=.008). At the measurement area AW (frameworks: 47 ±7 μm, complete contour FDPs: 50 ±9 μm of each location, no significant difference was observed between frameworks and complete contour FDPs (P=.361). ConclusionsDesign differences in 3-unit FDPs fabricated from translucent multicolored zirconia influenced the marginal and internal fit. Frameworks had smaller marginal fit than complete contour FDPs for translucent multicolored zirconia.

Patch attention network with generative adversarial model for semi-supervised binocular disparity prediction

In this paper, we address the challenging points of binocular disparity estimation: (1) unsatisfactory results in the occluded region when utilizing warping function in unsupervised learning; (2) inefficiency in running time and the number of parameters as adopting a lot of 3D convolutions in the feature matching module. To solve these drawbacks, we propose a patch attention network for semi-supervised stereo matching learning. First, we employ a channel-attention mechanism to aggregate the cost volume by selecting its different surfaces for reducing a large number of 3D convolution, called the patch attention network (PA-Net). Second, we use our proposed PA-Net as a generator and then combine it, traditional unsupervised learning loss, and the adversarial learning model to construct a semi-supervised learning framework for improving performance in the occluded areas. We have trained our PA-Net in supervised learning, semi-supervised learning, and unsupervised learning manners. Extensive experiments show that (1) our semi-supervised learning framework can overcome the drawbacks of unsupervised learning and significantly improve the performance in the ill-posed region by using only a few or inaccurate ground truths; (2) our PA-Net can outperform other state-of-the-art approaches in supervised learning and use fewer parameters.

A Study on Detection and Resolving of Occlusion Area by Street Tree Object using ResNet Algorithm

A Study on Detection and Resolving of Occlusion Area by Street Tree Object using ResNet Algorithm