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Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. Goal: In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. Methods: First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85%. Results: Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5% accuracy, 93.8% recall, 90.3% precision, and 92.0% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75% gain in performance compared to the baseline CNN-based framework. Conclusions: Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring.

Examining and potentially adjusting one's cognitive processes in response to dissatisfaction with one's performance is a fundamental aspect of intelligence. Remarkably, such sophisticated abstract concepts necessary for achieving Artificial General Intelligence can be effectively incorporated into basic Machine Learning algorithms. In this study, we introduce a method for replicating self-awareness through a supervisory Artificial Neural Network (ANN), which monitors patterns in the activation functions of an underlying ANN to identify signs of substantial uncertainty within the underlying ANN and, consequently, the reliability of its predictions. The underlying ANN in this context is a Convolutional Neural Network (CNN) ensemble primarily utilized for tasks related to facial recognition and facial expression analysis. We evaluate the performance of the supervisory ANNs using various activation functions as they learn to gauge the dependability of predictions made by the Inception v3 CNN ensemble. To conduct computational experiments, we employ a facial data set that incorporates makeup and occlusion factors. These experiments are designed to mimic real-world conditions where the training data set exclusively consists of images without makeup or occlusion, while the test data set comprises images featuring makeup and occlusion. This partitioning ensures the model is tested under challenging out-of-training data distribution scenarios. [ABSTRACT FROM AUTHOR]

Abstract Increasingly, automatic face recognition algorithms have become necessary with the development and extensive use of face recognition technology, particularly in the era of machine learning and artificial intelligence. However, the presence of unconstrained environmental conditions degrades the quality of acquired face images and may deteriorate the performance of many classical face recognition algorithms. Due to this backdrop, many researchers have given considerable attention to image restoration and enhancement mechanisms, but with minimal focus on occlusion-related and multiple-constrained problems. Although occlusion robust face recognition modules, via sparse representation have been explored, they require a large number of features to achieve correct computations and to maximize robustness to occlusions. Therefore, such an approach may become deficient in the presence of random occlusions of relatively moderate magnitude. This study assesses the robustness of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transformation for preprocessing and city block distance for classification (DWT-PCA/SVD-L1) face recognition module to image degradations due to random occlusions of varying magnitudes (10% and 20%) in test images acquired with varying expressions. Numerical evaluation of the performance of the DWT-PCA/SVD-L1 face recognition module showed that the use of the de-occluded faces for recognition enhanced significantly the performance of the study recognition module at each level (10% and 20%) of occlusion. The algorithm attained the highest recognition rate of 85.94% and 78.65% at 10% and 20% occlusions respectively, when the MICE de-occluded face images were used for recognition. With the exception of Entropy where MICE de-occluded face images attained the highest average value, the MICE and RegEM result in images of similar quality as measured by their Absolute mean brightness error (AMBE) and peak signal to noise ratio (PSNR). The study therefore recommends MICE as a suitable imputation mechanism for de-occlusion of face images acquired under varying expressions.

Tracking trajectories of the unconnected vehicles contributes to the improvement of traffic efficiency and safety. However, the effects of occlusions on the accuracy and reliability of the tracking results are nonnegligible. To address this issue, a modified multiple objects tracking algorithm was proposed to reduce the loss of trajectories caused by occlusions. The proposed algorithm was based on the multiple objects detection results, in which the motion states of the detected objects were determined. Further, the Kalman filter was employed to predict the trajectories, and each trajectory was uniquely matched to the detected object labeled with a tracking identity (ID) using the Hungarian algorithm. Afterward, a flexible life cycle strategy was proposed in terms of the speeds and accelerations of the detected objects, which controls the life cycle of the labeled trajectories when the occlusion occurred and guarantees the continuity of trajectories. The proposed tracking algorithm was tested on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data set, and the classification of events, activities, and relationships workshops, multiple object tracking (CLEAR-MOT) metrics was introduced to evaluate the performance of the proposed algorithm. The results indicated that the proposed algorithm with appropriate life cycle apparently increased the precision and accuracy and reduced the influence of occlusions on multiple objects tracking. Future work will concentrate on field implementations of the algorithm, and various scenarios and weather conditions will be taken into account. [ABSTRACT FROM AUTHOR]

Using 10 years of satellite‐borne radar and lidar observations coupled with a novel method for automated occlusion identification, composite transects of cloud and precipitation across occluded thermal ridges of extratropical cyclones are, for the first time, constructed. These composites confirm that occluded sectors are characterized by the most extensive cloud cover and heaviest precipitation in any of the frontal regions of the cyclone. Hydrometeor frequency in occluded sectors is sensitive to the cyclone's ascent strength but not to the mean precipitable water in the cyclone's environment. This result is in contrast to the strong relationships between hydrometeor frequency and both precipitable water and ascent strength as previously reported in warm frontal regions. In both hemispheres, cloud and precipitation increase with the maximum value of the equivalent potential temperature at 700 hPa within the occluded thermal ridge, until a threshold is reached. For very large values of maximum equivalent potential temperature, hydrometeors become less frequent while precipitation rates increase. It is suggested that this conjunction is a by‐product of an increase in the frequency of convection in those instances. While in the Northern Hemisphere occluded sectors exhibit deeper and wider cloud structures than their Southern Hemisphere counterparts, their hydrometeor occurrence frequencies are less. The differences in maximum equivalent potential temperature of the thermal ridges in both hemispheres does not appear to explain the more frequent hydrometeors in the Southern Hemisphere. These relationships offer new perspectives on the interplay between cloud processes and cyclone evolution, as well as new observational constraints for process evaluation of Earth system models. [ABSTRACT FROM AUTHOR]

Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades. While some detectors are able to handle partially occluded checkerboards, they fail when a large occlusion completely divides the checkerboard. We propose a new checkerboard detection pipeline for occluded checkerboards that has a robust performance under varying levels of noise, blurring, and distortion, and for a variety of imaging modalities. This pipeline consists of a checkerboard detector and checkerboard enhancement with Gaussian processes (GP). By learning a mapping from local board coordinates to image pixel coordinates via a Gaussian process, we can fill in occluded corners, expand the board beyond the image borders, allocate detected corners that do not fit an initial grid, and remove noise on the detected corner locations. We show that our method can improve the performance of other publicly available state-of-the-art checkerboard detectors, both in terms of accuracy and the number of corners detected. Our code and datasets are made publicly available. The checkerboard detector pipeline is contained within our Python checkerboard detection library, called PyCBD. The pipeline itself is modular and easy to adapt to different use cases. [ABSTRACT FROM AUTHOR]

Recognition of degraded frontal face images acquired under occlusion constraints remain challenging despite the plethora of reconstruction mechanisms. Though recent works have leveraged on some imputation mechanisms in this regard, their robustness in multiple constrained environments may not be guaranteed and may be affected by the choice of pre-processing mechanism. This paper proposes enhancement mechanisms that augment or complement the use of three (3) multiple imputation mechanisms for facial reconstruction in the presence of multiple constraints (10% and 20% occlusions and varying facial expressions). Specifically, we propose the use of a Discrete Cosine Transform-based (DCT) denoising or a Discrete Wavelet-based denoising following Histogram Equalization (HE-DWT) of the reconstructed face images prior to recognition. Experimental results showed that the proposed augmented enhancements improved significantly the recognition rates (90.63% & 91.15% and 86.98% & 85.94% for DCT and HE-DWT at 10% and 20% occlusion levels respectively for Missforest de-occluded face images) as compared with DWT in recognizing degraded frontal face images under moderately low levels of occlusions and varying expressions.

Ego lane detection is one of the key techniques in Ego Lane Analysis System (ELAS) implemented in smart autonomous driving cars for lane detection in roads. This technique has been extensively studied in recent years because of its accurate and robust detection of shape and location of lanes. The conventional methods are less robust and computationally expensive since they have several challenges in localization of lanes due to presence of occlusions on roads. So to avoid these issues, this paper uses a novel 2-stage lane detection method using deep convolutional neural network to detect the lanes and its key-points by optimally fit a curve to the lane to compensate on above mentioned issues. The proposed methodology for lane detection predicts the key-points accurately and it robust under various weather conditions and highway driving scenarios. In terms of performance, this technique is fast and robust with low computational cost and has high performance when deployed on autonomous vehicle-based systems.

Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades. While some detectors are able to handle partially occluded checkerboards, they fail when a large occlusion completely divides the checkerboard. We propose a new checkerboard detection pipeline for occluded checkerboards that has a robust performance under varying levels of noise, blurring, and distortion, and for a variety of imaging modalities. This pipeline consists of a checkerboard detector and checkerboard enhancement with Gaussian processes (GP). By learning a mapping from local board coordinates to image pixel coordinates via a Gaussian process, we can fill in occluded corners, expand the board beyond the image borders, allocate detected corners that do not fit an initial grid, and remove noise on the detected corner locations. We show that our method can improve the performance of other publicly available state-of-the-art checkerboard detectors, both in terms of accuracy and the number of corners detected. Our code and datasets are made publicly available. The checkerboard detector pipeline is contained within our Python checkerboard detection library, called PyCBD. The pipeline itself is modular and easy to adapt to different use cases.

Vascular occlusions in patients with coronavirus diseases 2019 (COVID-19) have been frequently reported in severe outcomes mainly due to a dysregulation of neutrophils mediating neutrophil extracellular trap (NET) formation. Lung specimens from patients with COVID-19 have previously shown a dynamic morphology, categorized into three types of pleomorphic occurrence based on histological findings in this study. These vascular occlusions in lung specimens were also detected using native endogenous fluorescence or NEF in a label-free method. The three types of vascular occlusions exhibit morphology of DNA rich neutrophil elastase (NE) poor (type I), NE rich DNA poor (type II), and DNA and NE rich (type III) cohort of eleven patients with six males and five females. Age and gender have been presented in this study as influencing variables linking the occurrence of several occlusions with pleomorphic contents within a patient specimen and amongst them. This study reports the categorization of pleomorphic occlusions in patients with COVID-19 and the detection of these occlusions in a label-free method utilizing NEF. [ABSTRACT FROM AUTHOR]

Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster. [ABSTRACT FROM AUTHOR]

Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intensive Care Units (PICUs) face the challenge of occlusions hindering accurate image analysis and interpretation. Goal: In this study, we propose a hybrid approach to effectively segment common occlusions encountered in remote monitoring applications within PICUs. Our approach centers on creating a deep-learning pipeline for limited training data scenarios. Methods: First, a combination of the well-established Google DeepLabV3+ segmentation model with the transformer-based Segment Anything Model (SAM) is devised for occlusion segmentation mask proposal and refinement. We then train and validate this pipeline using a small dataset acquired from real-world PICU settings with a Microsoft Kinect camera, achieving an Intersection-over-Union (IoU) metric of 85%. Results: Both quantitative and qualitative analyses underscore the effectiveness of our proposed method. The proposed framework yields an overall classification performance with 92.5% accuracy, 93.8% recall, 90.3% precision, and 92.0% F1-score. Consequently, the proposed method consistently improves the predictions across all metrics, with an average of 2.75% gain in performance compared to the baseline CNN-based framework. Conclusions: Our proposed hybrid approach significantly enhances the segmentation of occlusions in remote patient monitoring within PICU settings. This advancement contributes to improving the quality of care for pediatric patients, addressing a critical need in clinical practice by ensuring more accurate and reliable remote monitoring., (© 2024 The Authors.)

The classical treatment option for amblyopia in Latvia is occlusions of the non-amblyopic eye. The newest methods involve specialized computer games that involve both eyes in visual processing during the treatment as well as stimulate binocularity. To assess the efficiency of specialized computer games in the treatment of amblyopia in school-age children in Latvia, 237 children (7â18 years old) participated in the experiment, of whom only 22 completed it: 11 participants had occlusion therapy, 11 participants played the specialized dichoptic computer game Tetris®. The visual acuity of the amblyopic eye as well as stereopsis were evaluated at near and far distances before the treatment, 2 months and 4 months after the beginning of the treatment. The results show statistically significant improvement in visual acuity in both treatment groups after four months of therapy. The extent of improvement is similar in both groups. Specialized computer games for amblyopia treatment may be recommended to patients from an age when they are able to use a computer along with a keyboard and computer mouse, who want to improve their visual acuity in the amblyopic eye but are not willing to use occlusions. The only requirement for using specialized games is that the patients need to have binocular single vision. In conclusion, the use of specialized computer games is an alternative type of amblyopia treatment compared to occlusion therapy.

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The depth ordering of two surfaces, one occluding the other, can in principle be determined from the correlation between the occlusion border's blur and the blur of the two surfaces. If the border is blurred, the blurrier surface is nearer; if the border is sharp, the sharper surface is nearer. Previous research has found that observers do not use this informative cue. We reexamined this finding. Using a multiplane display, we confirmed the previous finding: Our observers did not accurately judge depth order when the blur was rendered and the stimulus presented on one plane. We then presented the same simulated scenes on multiple planes, each at a different focal distance, so the blur was created by the optics of the eye. Performance was now much better, which shows that depth order can be reliably determined from blur information but only when the optical effects are similar to those in natural viewing. We asked what the critical differences were in the single- and multiplane cases. We found that chromatic aberration provides useful information but accommodative microfluctuations do not. In addition, we examined how image formation is affected by occlusions and observed some interesting phenomena that allow the eye to see around and through occluding objects and may allow observers to estimate depth in da Vinci stereopsis, where one eye's view is blocked. Finally, we evaluated how accurately different rendering and displaying techniques reproduce the retinal images that occur in real occlusions. We discuss implications for computer graphics.

The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a highly probable scenario behind the leaves exploiting learned patterns from images with non-occluded berries. Our experiments show that the estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries. Furthermore, we show that our approach can identify which areas in the image should be changed by adding new berries without explicitly requiring information about hidden areas.

Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster.

The classical treatment option for amblyopia in Latvia is occlusions of the nonamblyopic eye. The newest methods involve specialized computer games that involve both eyes in visual processing during the treatment as well as stimulate binocularity. To assess the efficiency of specialized computer games in the treatment of amblyopia in schoolage children in Latvia, 237 children (7-18 years old) participated in the experiment, of whom only 22 completed it: 11 participants had occlusion therapy, 11 participants played the specialized dichoptic computer game Tetris®. The visual acuity of the amblyopic eye as well as stereopsis were evaluated at near and far distances before the treatment, 2 months and 4 months after the beginning of the treatment. The results show statistically significant improvement in visual acuity in both treatment groups after four months of therapy. The extent of improvement is similar in both groups. Specialized computer games for amblyopia treatment may be recommended to patients from an age when they are able to use a computer along with a keyboard and computer mouse, who want to improve their visual acuity in the amblyopic eye but are not willing to use occlusions. The only requirement for using specialized games is that the patients need to have binocular single vision. In conclusion, the use of specialized computer games is an alternative type of amblyopia treatment compared to occlusion therapy. [ABSTRACT FROM AUTHOR]

• The framework enables enhanced UAV data acquisition for fruit detection purposes in vineyards considering the biophysical environment. • The optimization algorithm provides paths up to 24% shorter compared to conventional mission planners. • The proposed framework is designed for both single and multiple UAVs flying synchronously over the field. • The algorithm can be applied to woody crops other than vineyards trained in vertical trellis since they share similar biophysical characteristics and crop structures. Typically, commercial orchards and vineyards consist of large fields that encounter similar development phases at once. Thus, it becomes necessary to efficiently fly over all fields to detect fruit and identify their status in a very limited timeframe. For this purpose, Unmanned Aerial Vehicles (UAVs) path planning plays a pivotal role in agriculture as it enables optimal coverage of agricultural fields, leading to enhanced data acquisition and improved precision agriculture practices, for instance, disease assessment and pesticide application. In addition, deep learning techniques offer precise image analysis. On the one hand, object detection has been applied to agricultural fields to carry out a wide range of operations, such as detecting apples and predicting yield in vineyards, with higher detection accuracy when the fruits are fully visible. On the other hand, when crops present leaf-occlusion, the algorithms face difficulties and are unable to adapt to the specific characteristics of the field. Therefore, this study seeks to address this issue by developing a novel framework to enhance UAV path planning for data collection in vineyards, considering the current biophysical environment. To this end, the proposed framework requires two flights: i) a first flight (survey) to acquire insights on the crop structure and environment, and ii) a second flight using the Ant Colony Optimization Max-Min Ant System (ACO-MMAS) algorithm to enhance image acquisition by considering multiple angles to overcome partial leaf-occlusion. Further, the optimisation algorithm can potentially boost the acquisition of datasets for fruit detection by considering single and multiple UAVs flying synchronously while ensuring a safe distance between platforms and efficient coverage. The method was tested in two vineyards with different environmental characteristics, increasing levels of difficulty and acquired during two different growing seasons. It improved the length of the computed paths by up to 24%, compared to a base algorithm that considers only the closest point without any optimisation, improving the decision-making processes and resource allocation in crop management. [ABSTRACT FROM AUTHOR]

Interest in agricultural automation has increased considerably in recent decades due to benefits such as improving productivity or reducing the labor force. However, there are some current problems associated with unstructured environments make developing a robotic harvester a challenge. This article presents a dual-arm aubergine harvesting robot consisting of two robotic arms configured in an anthropomorphic manner to optimize the dual workspace. To detect and locate the aubergines automatically, we implemented an algorithm based on a support vector machine (SVM) classifier and designed a planning algorithm for scheduling efficient fruit harvesting that coordinates the two arms throughout the harvesting process. Finally, we propose a novel algorithm for dealing with occlusions using the capabilities of the dual-arm robot for coordinate work. Therefore, the main contribution of this study is the implementation and validation of a dual-arm harvesting robot with planning and control algorithms, which, depending on the locations of the fruits and the configuration of the arms, enables the following: (i) the simultaneous harvesting of two aubergines; (ii) the harvesting of a single aubergine with a single arm; or (iii) a collaborative behavior between the arms to solve occlusions. This cooperative operation mimics complex human harvesting motions such as using one arm to push leaves aside while the other arm picks the fruit. The performance of the proposed harvester is evaluated through laboratory tests that simulate the most common real-world scenarios. The results show that the robotic harvester has a success rate of 91.67% and an average cycle time of 26 s/fruit.

Any $3D$ 3 D tracking algorithm has to deal with occlusions: multiple targets get so close to each other that the loss of their identities becomes likely; hence, potentially affecting the very quality of the data with interrupted trajectories and identity switches. Here, we present a novel tracking method that addresses the problem of occlusions within large groups of featureless objects by means of three steps: i) it represents each target as a cloud of points in $3D$ 3 D ; ii) once a $3D$ 3 D cluster corresponding to an occlusion occurs, it defines a partitioning problem by introducing a cost function that uses both attractive and repulsive spatio-temporal proximity links; and iii) it minimizes the cost function through a semi-definite optimization technique specifically designed to cope with the presence of multi-minima landscapes. The algorithm is designed to work on $3D$ 3 D data regardless of the experimental method used: multicamera systems, lidars, radars, and RGB-D systems. By performing tests on public data-sets, we show that the new algorithm produces a significant improvement over the state-of-the-art tracking methods, both by reducing the number of identity switches and by increasing the accuracy of the estimated positions of the targets in real space. [ABSTRACT FROM AUTHOR]

Vehicle detection and tracking plays a major role in military and civilian applications. The accurate detection of multiple vehicles in a complicated traffic environment is too difficult. This process is made more difficult, if there are occlusions between vehicles. The optimal feature selection of a vehicle is also a challenging task. These issues are focused in the existing work namely convolution neural network (CNN) based vehicle detection. However in this work, feature selection is not concentrated and with the lesser feature information classification accuracy is reduced considerably. This is focused and resolved in the proposed research by introducing the method namely Enhanced Convolution neural network with Support Vector Machine (ECNN-SVM) based vehicle detection. In this work initially feature extraction is done. Haar like features are used for this purpose. This has compact representation, capture information from multiple scales, encoded edge and structural information and can be efficient computation. Haar like feature pool is large scale in nature. Enhanced bat optimization is used to select features from this pool. This selection is done by combining sample's class label and feature value. Confidence level between two thresholds of tracking rectangles is used for classification. Support vector machine (SVM) classifier is combined with local binary pattern to perform this classification. The interference areas between moving objects and vehicles are removed by Enhanced Convolutional Neural Network (ECNN) classifier. The efficiency of the proposed method is computed by using experimental and theoretical analysis. [ABSTRACT FROM AUTHOR]

Peripheral artery disease (PAD) is the buildup of calcium and fatty deposits in the arterial walls (atherosclerosis). This is an important clinical issue, specifically in cases with multilevel lesions. A patient underwent sequential angioplasty treatment for major PAD, which was characterized by multilevel lesions affecting both the infrapopliteal arteries. The proximal vessels and infrapopliteal vessels are mostly observed to be affected by PAD, thus the patient likely has PAD localized to the lower leg. In the femoropopliteal segment, lower extremity artery or aortic atherosclerotic occlusive disease can lead to significant outcomes. Severe claudication and pain during rest in both legs were observed in a patient with a history of hypertension and diabetes mellitus. With an angiography, the superficial femoral, popliteal, and tibial arteries have been shown to have major stenoses and occlusions. A progressive treatment was used because of the complexity of the lesions initiating with endovascular revascularization of the superficial femoral artery. The popliteal and tibial arteries were then repaired with angioplasty and stent placement. After the treatment, the patient's symptoms significantly improved, including elimination of their rest discomfort and claudication. Measurements of the ankle-brachial index (ABI) indicated that the affected limbs' perfusion was refined. Six months later, a follow-up angiography revealed intact vessels with no restenosis. This case report shows the successful outcome of recurrent angioplasty in curing complicated multilevel PAD, giving symptomatic relief and maintaining limb perfusion. This research is required to assess the long-term outcomes and longevity of this kind of treatment in patient populations that are comparable to others., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Patil et al.)

The double pendulum is a system that manifests fascinating non-linear behavior. This made it a popular tool in academic settings for illustrating the intricate response of a seemingly simple physical apparatus, or to validate tools for studying nonlinear phenomena. In addition, the double pendulum is also widely used in several modeling applications including robotics and human locomotion analysis. However, surprisingly, there is a lack of a thoroughly documented hardware that enables designing, building, and reliably tracking and collecting data from a double pendulum.This paper provides comprehensive documentation of a research quality bench top double pendulum. The contributions of our work include (1) providing detailed CAD drawings, part lists, and assembly instructions for building a low friction double pendulum. (2) A new tracking algorithm written in Python for tracking the position of both links of the double pendulum. This algorithm measures the angles of the links by examining each frame, and computes uncertainties in the measured angles by following several trackers on each link. Additionally, our tracking algorithm bypasses the data transmission difficulties caused by instrumenting the bottom link with physical sensors. (3) A derivation of the equations of motion of the actual physical system. (4) A description of the process (with provided Python code) for extracting the model parameters—e.g., damping—with error bounds from physical measurements.

Introduction Endovascular management for patients with chronic outflow lesions has been considered as an optimal alternative option for symptom control, as conservative compression therapy may fail later. The study aimed to determine the clinical outcomes and to evaluate the quality of life (QoL) improvement after recanalization and deep venous stenting in patients with postthrombotic syndrome and nonthrombotic iliac vein lesion in whom conventional treatment had failed. Patients and methods This study was conducted prospectively on 40 patients with chronic deep venous diseases (nonthrombotic iliac vein lesion or postthrombotic syndrome) and who presented to Vascular Surgery Department, Faculty of Medicine, Assiut, Egypt from July 2016 to September 2019. Results A total of 40 patients with 50 limbs (21 males; median age, 45.2 years, with range, 18–75 years) were included, and all had successful recanalization and stent deployment. A complete history and physical examination were performed for all included patients as well as assessment of daily activity improvements after endovenous procedures during postoperative follow-up visits at 2 weeks, 1.5, 3, 6, and 12 months. Deep venous recanalization with deployment of stents was medically successful in all 50 limbs with overall high patency (primary, assisted secondary, and secondary patency rates of 76, 88, and 92%, respectively). A statistically significant improvement in QoL scores (VEINESQOL/Sym scores) was observed in all patients as compared with the baseline at 2, 6, and 12 months after stenting (64.4, 69.2, and 73.4, respectively, P<0.05) along with a significant decrease in both Villalta and Venous Clinical Severity Score scores from baseline compared with 2, 6, and 12 months after the treatment (7.3, 6.4, and 6.3, respectively, P<0.0001 and 7.0, 6.4, and 6.2, respectively, P<0.0001). Conclusions Our results confirm the significant effect of stenting on QoL of patients with chronic symptomatic iliofemoral venous obstructive lesions with high cumulative patency rate, making it a viable treatment option. [ABSTRACT FROM AUTHOR]

SUMMARY: Navigation tasks are often subject to several constraints that can be related to the sensors (visibility) or come from the environment (obstacles). In this paper, we propose a framework for autonomous omnidirectional wheeled robots that takes into account both collision and occlusion risk, during sensor-based navigation. The task consists in driving the robot towards a visual target in the presence of static and moving obstacles. The target is acquired by fixed – limited field of view – on-board cameras, while the surrounding obstacles are detected by lidar scanners. To perform the task, the robot has not only to keep the target in view while avoiding the obstacles, but also to predict its location in the case of occlusion. The effectiveness of our approach is validated through several experiments. [ABSTRACT FROM AUTHOR]

State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we shift the operating point in this field of conflicts towards universality and speed. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader. [ABSTRACT FROM AUTHOR]

• Santiago Urban Dataset SUD was acquired with MLS and HMLS equipment. • Occlusions are reduced by the integration of MLS and HMLS data. • Point clouds are labelled by heuristic and Deep Learning methods in eight classes. • SUD is valid for comparing semantic segmentation works. • SUD is valid for urban 3D mobility studies. Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles , and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research. [ABSTRACT FROM AUTHOR]

The enlightenment of the formation of neutrophil extracellular traps (NETs) as a part of the innate immune system shed new insights into the pathologies of various diseases. The initial idea that NETs are a pivotal defense structure was gradually amended due to several deleterious effects in consecutive investigations. NETs formation is now considered a double-edged sword. The harmful effects are not limited to the induction of inflammation by NETs remnants but also include occlusions caused by aggregated NETs (aggNETs). The latter carries the risk of occluding tubular structures like vessels or ducts and appear to be associated with the pathologies of various diseases. In addition to life-threatening vascular clogging, other occlusions include painful stone formation in the biliary system, the kidneys, the prostate, and the appendix. AggNETs are also prone to occlude the ductal system of exocrine glands, as seen in ocular glands, salivary glands, and others. Last, but not least, they also clog the pancreatic ducts in a murine model of neutrophilia. In this regard, elucidating the mechanism of NETs-dependent occlusions is of crucial importance for the development of new therapeutic approaches. Therefore, the purpose of this review is to address the putative mechanisms of NETs-associated occlusions in the pathogenesis of disease, as well as prospective treatment modalities.

Nuclei segmentation plays a crucial role in disease understanding and diagnosis. In whole slide images, cell nuclei often appear overlapping and densely packed with ambiguous boundaries due to the underlying 3D structure of histopathology samples. Instance segmentation via deep neural networks with object clustering is able to detect individual segments in crowded nuclei but suffers from a limited field of view, and does not support amodal segmentation. In this work, we introduce a dense feature pyramid network with a feature mixing module to increase the field of view of the segmentation model while keeping pixel-level details. We also improve the model output quality by adding a multi-scale self-attention guided refinement module that sequentially adjusts predictions as resolution increases. Finally, we enable clusters to share pixels by separating the instance clustering objective function from other pixel-related tasks, and introduce supervision to occluded areas to guide the learning process. For evaluation of amodal nuclear segmentation, we also update prior metrics used in common modal segmentation to allow the evaluation of overlapping masks and mitigate over-penalization issues via a novel unique matching algorithm. Our experiments demonstrate consistent performance across multiple datasets with significantly improved segmentation quality., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

Target tracking is a popular but challenging problem in computer vision field. Due to many aggravating factors such as position transformation, illumination, occlusion, it is difficult to achieve robust target tracking. According to the above constraints, an improved target tracking algorithm based on spatio-temporal context (STC) under occlusions is proposed. On the basis of STC, the proposed method introduces a novel mechanism for dealing with occlusion, the scale update, and the learning rate update to reduce the error update of the model and restrain error accumulation. As a consequence, the tracking performance can be improved efficiently. Extensive experimental results show that our algorithm outperforms the original STC algorithm and some other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world. Highlights • We propose a novel Adversarial Network that solves occlusions in pedestrian images. • Our reconstruction keeps both the appearance and the background coherent. • We condition the occluded body part restoration on pedestrian attributes. • We devise a new way for synthetically generating occlusion and visible pairs. • We provide a huge CG dataset for pedestrian attribute recognition in crowded areas. [ABSTRACT FROM AUTHOR]

Abstract Archaeozoology or zooarchaeology is the scientific field that aims to rebuild natural and cultural relations between human and animals. To reach that objective, archaeozoologists study faunal remains, such as bones. In this regard, a 3D representation is put into place. This would allow a better understanding of the initial spatial positions of the animals in the pits, and would make it possible to examine in a more rigorous manner the explanatory hypotheses by manipulating the 3D view of the pit and bones. This manipulation invokes the use of computer vision techniques, including 3D object recognition methods. In this work we present a novel approach to recognize 3D bones in occluded and cluttered scenes inspiring from a stat of the art method called spin images. We combine this method with saliency concept to improve the the robustness to occlusions and clutters significantly and also to decrease the complexity of the algorithm. [ABSTRACT FROM AUTHOR]

Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous surface for many types of analyses. Ultimately, the relative proportion of occlusions in a TLS survey is an indicator of the survey quality. Recognizing that regions of a scanned scene occluded from one scan position are likely visible from another point of view, a prevalence of occlusions can indicate an insufficient number of scans and/or poor scanner placement. Conversely, a prevalence of dropouts is ordinarily not indicative of survey quality, as a scanner operator cannot usually control the presence of specular reflective or absorbent surfaces in a scanned scene. To this end, this manuscript presents a novel methodology to determine data completeness by properly classifying and quantifying the proportion of the site that consists of point returns and the two types of data gaps. Knowledge of the data gap origin can not only facilitate the judgement of TLS survey quality, but it can also identify pooled water when water reflections are the main source of dropouts in a scene, which is important for ecological research, such as habitat modeling. The proposed data gap classification methodology was successfully applied to DEMs for two study sites: (1) A controlled test site established by the authors for the proof of concept of classification of occlusions and dropouts and (2) a rocky intertidal environment (Rabbit Rock) presenting immense challenges to develop a topographic model due to significant tidal fluctuations, pooled water bodies, and rugged terrain generating many occlusions.