Partial Occlusion Research Articles

Occlusion-Aware Human Mesh Model-Based Gait Recognition

Partial occlusion of the human body caused by obstacles or a limited camera field of view often occurs in surveillance videos, which affects the performance of gait recognition in practice. Existing methods for gait recognition against occlusion require a bounding box or the height of a full human body as a prerequisite, which is unobserved in occlusion scenarios. In this paper, we propose an occlusion-aware model-based gait recognition method that works directly on gait videos under occlusion without the above-mentioned prerequisite. Specifically, given a gait sequence that only contains non-occluded body parts in the images, we directly fit a skinned multi-person linear (SMPL)-based human mesh model to the input images without any pre-normalization or registration of the human body. We further use the pose and shape features extracted from the estimated SMPL model for recognition purposes, and use the extracted camera parameters in the occlusion attenuation module to reduce intra-subject variation in human model fitting caused by occlusion pattern differences. Experiments on occlusion samples simulated from the OU-MVLP dataset demonstrated the effectiveness of the proposed method, which outperformed state-of-the-art gait recognition methods by about 15% rank-1 identification rate and 2% equal error rate in the identification and verification scenarios, respectively.

Case report: Small cell lung cancer presenting as the “sunray sign” in the chest radiograph and recurrent hemoptysis

Lung cancer prognosis has not changed in the last few decades due to diagnosis at an advanced stage. Themajority of cases with early disease are asymptomatic, and whenever clinical presentations with cough, dyspnea,hemoptysis, or chest pain occur, when these cases have progressed to an advanced stage. Lung cancer topsthe list of all causes of cancer-related deaths globally and is ahead of digestive tract malignancies. Small-celllung cancer (SCLC) has the worst outcome, with survival rates in the range of weeks to months from diagnosis.SCLC is usually manifested as an extensive disease due to its delayed presentation, early metastasis, systemicnature, and poor response to available treatment options. “sunray sign” in chest radiographs is first describedin literature and constitutes a hilar mass or radiopacity with inhomogeneous linear opacities spreading towardthe periphery like sunrays, which is a marker of interstitial lymphatic involvement due to malignant spread ofdisease. The “sunray sign” is an indicator of underlying lung malignancy with central airway or main stem bronchusinvolvement and lymphatic dissemination in linear opacities. In this case report, we have reported a 51-year-oldmale who presented with cough and hemoptysis with progressive worsening of shortness of breath. Chest X-raysdocumented round opacities occupying the right hilum with linear opacities emerging toward the periphery in lungparenchyma, showing the typical “sunray sign.” Bronchoscopy was done after clinical stabilization and showedendobronchial polypoidal growth in the right main stem bronchus, causing partial occlusion of the bronchial lumen.Endobronchial needle aspiration (EBNA) cytology and forceps-guided (FB) histopathology are suggestive of “smallcell histological type” lung malignancy for the “sunray sign” in our case. A high index of suspicion is a must to ruleout underlying malignancy, and bronchoscopy is the “gold standard” test in cases with the sunray sign to confirmthe diagnosis.

Uncertainty-aware Gaze Tracking for Assisted Living Environments.

Effective assisted living environments must be able to infer how their occupants interact in a variety of scenarios. Gaze direction provides strong indications of how a person engages with the environment and its occupants. In this paper, we investigate the problem of gaze tracking in multi-camera assisted living environments. We propose a gaze tracking method based on predictions generated by a neural network regressor that relies only on the relative positions of facial keypoints to estimate gaze. For each gaze prediction, our regressor also provides an estimate of its own uncertainty, which is used to weigh the contribution of previously estimated gazes within a tracking framework based on an angular Kalman filter. Our gaze estimation neural network uses confidence gated units to alleviate keypoint prediction uncertainties in scenarios involving partial occlusions or unfavorable views of the subjects. We evaluate our method using videos from the MoDiPro dataset, which we acquired in a real assisted living facility, and on the publicly available GazeFollow and Gaze360 datasets. Experimental results show that our gaze estimation network outperforms sophisticated state-of-the-art methods, while additionally providing uncertainty predictions that are highly correlated with the actual angular error of the corresponding estimates. Finally, an analysis of the temporal integration performance of our method demonstrates that it generates accurate and temporally stable gaze predictions.

Blood Flow Restriction Following ACL Reconstruction

Background: Blood flow restriction (BFR) therapy is a technique that uses partial occlusion of arterial blood flow in tandem with low-load resistance training to promote an environment of metabolic stress within muscle tissue. It is hypothesized that such therapy can facilitate protein synthesis and muscle hypertrophy even in the setting of age, injury, or postoperative rehabilitation—conditions which are marred by muscle atrophy and progressive loss of function. Therefore, BFR may be a successful option to facilitate strength gains even in patients unable to perform traditional high-load resistance training. Indications: BFR therapy has been shown to be efficacious when used in healthy athletes, the elderly, or in postoperative patients undergoing rehabilitation after upper or lower extremity procedures. More specifically, BFR application in patients undergoing knee surgery has been shown to reduce muscle atrophy post operatively. Technique Description: BFR involves application of a tourniquet or occlusion cuff at 70% of the determined arterial occlusion pressure (commonly 150-180 mm Hg). The arterial occlusion pressure is calculated by observing the loss of Doppler ultrasonography signal at the pedal pulses with sequential inflation of a blood pressure cuff. This cuff should be applied as proximal as possible at the affected extremity. The patient subsequently performs 5 exercises, including 3 sets of 15 repetitions of each exercise, with 30 seconds of rest in between sets. The cuff remains inflated for all 5 exercises. Results: BFR in tandem with low-load resistance training has been shown to be effective in improving lower extremity muscle torque and mass of the quadriceps and hamstring muscles when used after knee surgery, specifically anterior cruciate ligament (ACL) reconstruction. The most commonly reported adverse outcomes after BFR include muscle soreness and sensory paresthesias; however, BFR is generally believed to be safe and acceptable for use in a broad spectrum of patients. Discussion/Conclusion: Muscle atrophy and loss of strength are hallmarks of aging, injured, and postoperative patients. Traditional means of high-intensity strength training is not feasible in these patient populations, and the use of BFR in tandem with lower intensity strength training shows promise in its ability to promote improvements in muscle strength and hypertrophy. However, more high-level research into the long-term effects, complications, and optimal BFR training regimen is warranted. Patient Consent Disclosure Statement: The author(s) attests that consent has been obtained from any patient(s) appearing in this publication. If the individual may be identifiable, the author(s) has included a statement of release or other written form of approval from the patient(s) with this submission for publication.

Image-Based Automatic Energy Meter Reading Using Deep Learning

We propose to perform an image-based framework for electrical energy meter reading. Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units. Combining the readings of serial numbers and energy meter units, an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup. However, such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display, orientation and scale variations caused by camera positioning, and non-uniform illumination caused by shades. To this end, our work evaluates and compares the state-of-the art deep learning algorithm You Only Look Once (YOLO ) along with traditional handcrafted features for text extraction and recognition. Our image dataset contains 10,000 images of electrical energy meters and is further expanded by data augmentation such as in-plane rotation and scaling to make the deep learning algorithms robust to these image variations. For training and evaluation, the image dataset is annotated to produce the ground truth of all the images. Consequently, YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98% for all the digits. It proves to be robust against the mentioned image variations compared with the traditional handcrafted features. Our proposed method can be highly instrumental in reducing the time and effort involved in the current meter reading, where workers visit door to door, take images of meters and manually extract readings from these images.

Food volume estimation by multi-layer superpixel.

Estimating the volume of food plays an important role in diet monitoring. However, it is difficult to perform this estimation automatically and accurately. A new method based on the multi-layer superpixel technique is proposed in this paper to avoid tedious human-computer interaction and improve estimation accuracy. Our method includes the following steps: 1) obtain a pair of food images along with the depth information using a stereo camera; 2) reconstruct the plate plane from the disparity map; 3) warp the input image and the disparity map to form a new direction of view parallel to the plate plane; 4) cut the warped image into a series of slices according to the depth information and estimate the occluded part of the food; and 5) rescale superpixels for each slice and estimate the food volume by accumulating all available slices in the segmented food region. Through a combination of image data and disparity map, the influences of noise and visual error in existing interactive food volume estimation methods are reduced, and the estimation accuracy is improved. Our experiments show that our method is effective, accurate and convenient, providing a new tool for promoting a balanced diet and maintaining health.

Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking

In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian’s entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians’ head movement tracklets, and the pedestrians’ whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole body bounding boxes are calculated, and the Hungarian algorithm is used to match the above calculation results. Finally, according to the matching results, the head bounding boxes in the head tracklets are replaced with the whole body bounding boxes, and the whole body motion tracklets of the pedestrians in the dense scene are generated. Our method can be performed online, and experiments suggested that our method effectively reduces the negative effects of false negatives and false positives on the tracker caused by severe occlusion in dense scenes.

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.

3D object detection through fog and occlusion: passive integral imaging vs active (LiDAR) sensing.

In this paper, we address the problem of object recognition in degraded environments including fog and partial occlusion. Both long wave infrared (LWIR) imaging systems and LiDAR (time of flight) imaging systems using Azure Kinect, which combine conventional visible and lidar sensing information, have been previously demonstrated for object recognition in ideal conditions. However, the object detection performance of Azure Kinect depth imaging systems may decrease significantly in adverse weather conditions such as fog, rain, and snow. The concentration of fog degrades the depth images of Azure Kinect camera, and the overall visibility of RGBD images (fused RGB and depth image), which can make object recognition tasks challenging. LWIR imaging may avoid these issues of lidar-based imaging systems. However, due to poor spatial resolution of LWIR cameras, thermal imaging provides limited textural information within a scene and hence may fail to provide adequate discriminatory information to identify between objects of similar texture, shape and size. To improve the object detection task in fog and occlusion, we use three-dimensional (3D) integral imaging (InIm) system with a visible range camera. 3D InIm provides depth information, mitigates the occlusion and fog in front of the object, and improves the object recognition capabilities. For object recognition, the YOLOv3 neural network is used for each of the tested imaging systems. Since the concentration of fog affects the images from different sensors (visible, LWIR, and Azure Kinect depth cameras) in different ways, we compared the performance of the network on these images in terms of average precision and average miss rate. For the experiments we conducted, the results indicate that in degraded environment 3D InIm using visible range cameras can provide better image reconstruction as compared to the LWIR camera and Azure Kinect RGBD camera, and therefore it may improve the detection accuracy of the network. To the best of our knowledge, this is the first report comparing the performance of object detection between passive integral imaging system vs active (LiDAR) sensing in degraded environments such as fog and partial occlusion.

Reversible Vision Loss Following Nonsurgical Filler Rhinoplasty

The increase in the frequency and popularity of aesthetic filler injections is accompanied by a high risk of complications, including ophthalmological sequalae. Of these, loss of vision is considered the most dangerous and, in most cases, irreversible. We present a case report of a patient who experienced acute vision loss due to suspected partial occlusion of the ophthalmic artery following nonsurgical rhinoplasty with hyaluronic acid filler injection. It differs from others in that treatment in the form of a combination of multiple subcutaneous hyaluronidase injections into the periocular region, a single retrobulbar injection of hyaluronidase, and hyperbaric oxygen therapy led to a significant recovery of ophthalmic symptoms with only residual visual field defects remaining.

Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm

Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits’ detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction.

Multiple object tracking with extended occlusions.

In everyday life, we often view objects through a limited aperture (e.g., soccer players on TV or cars slipping into our blind spot on a busy road), where objects often move out of view and reappear in a different place later. We modelled this situation in a series of multiple object tracking (MOT) experiments, in which we introduced a cover on the edges of the observed area and manipulated its width. This method introduced systematic occlusions, which were longer than those used in previous MOT studies. Experiment 1 (N = 50) showed that tracking under such conditions is possible, although difficult. An item-level analysis confirmed that people made more errors in targets that were covered longer and more often. In Experiment 2 (N = 50), we manipulated the tracking workload and found that the participants were less affected by the cover when the tracking load was low. In Experiment 3 (N = 50), we asked the participants to keep track of the objects' identities (multiple identity tracking [MIT]). Although MIT is subjectively more demanding, memorising identities improved performance in the most difficult cover conditions. Contrary to previous reports, we also found that even partial occlusions negatively affected tracking.

Partial vs Full Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) in a Swine Model of Raised Intracranial Pressure and Hemorrhagic Shock.

Partial resuscitative endovascular balloon occlusion of the aorta (pREBOA) is a potential method to mitigate the ischemia observed in full REBOA (fREBOA). However, the effect of pREBOA on cerebral perfusion in the setting of raised intracranial pressure (rICP) is unknown. The aim was to evaluate the effects of no REBOA (nREBOA) vs pREBOA vs fREBOA on cerebral perfusion in a swine model of rICP and hemorrhagic shock. Anesthetized swine (n = 18) underwent instrumentation. Controlled hemorrhage was performed over 30 minutes. rICP was achieved using an intracranial Fogarty catheter inflated to achieve an ICP of 20 mmHg. Animals underwent intervention for 30 minutes, followed by resuscitation. The primary outcome was cerebral perfusion measured by ICP (millimeters of mercury), cerebral perfusion pressure (CPP; millimeters of mercury), and cerebral blood flow (CBF; milliliters per minute per 100 g) derived from CT perfusion. The secondary outcomes included hemodynamics and lactate (millimoles per liter). The peak ICP of pREBOA animals (22.7 ± 2.5) was significantly lower than nREBOA and fREBOA. pREBOA CPP was significantly higher compared with nREBOA and fREBOA during resuscitation. The pREBOA CBF was greater during intervention and resuscitation compared with nREBOA (p < 0.001). Systolic blood pressure was similar between pREBOA and fREBOA, and coronary perfusion was significantly greater in pREBOA. fREBOA had significantly higher lactate during the intervention (9.3 ± 1.3) and resuscitation (8.9 ± 3.5) compared with nREBOA and pREBOA. pREBOA produced greater cerebral perfusion, as demonstrated by more favorable CPP, CBF, and ICP values. fREBOA was associated with metabolic derangement and diminished pressure during resuscitation. pREBOA is superior to fREBOA in a swine model and should be considered over fREBOA for aortic occlusion.

Primary cardiac malignant peripheral nerve sheath tumour in a dog

AbstractA 6‐year‐old, female labrador retriever was presented for acute onset abdominal distention. Clinical examination identified tachycardia, a novel grade 3/6 right basilar systolic cardiac murmur, a distended abdomen and ventral pitting oedema. Jugular pulses were not present. Echocardiography revealed an infiltrative, intramural cardiac mass, with partial occlusion of the right atrium and ventricle, resulting in isolated caudal vena cava syndrome. Postmortem examination revealed a large mass arising from the base of the right atrium and extending into the ventricular lumen. Histopathological and immunohistochemical findings were consistent with a diagnosis of malignant peripheral nerve sheath tumour, a rare primary cardiac neoplasm in dogs.

Safety and effectiveness of double microcatheter technique in the treatment of ruptured aneurysms of anterior cerebral circulation.

To evaluate the safety and effectiveness of the double microcatheter technique in the treatment of ruptured aneurysms of the anterior cerebral circulation. Between 2012 and 2019, 113 patients with ruptured aneurysms of the anterior cerebral circulation were treated using the double microcatheter technique. Clinical records, angiographic results, and procedure-related complications were reviewed. Clinical and angiographic follow-up was performed. Complete occlusion, neck remnant, and partial occlusion were, respectively, recorded in 56.6, 38.9, and 4.4% of the total cases. For all patients, the incidence of intraoperative complications was 5.3% (6/113), and the overall rate of morbidity was 10.6% (12/113). Before discharge, three patients (2.7%) died. There was no procedure-related mortality. At discharge, favorable outcomes were observed in 79.6% (90/113) of the patients. High Hunt-Hess grades and receiving a craniotomy or external ventricular drainage were risk factors for clinical outcomes at discharge. Clinical follow-up was performed in 91 patients at a mean interval of 14.07 ± 11.68 months. At follow-up, favorable outcomes were observed in 92.3% (84/91) of the patients. Angiographic follow-up was performed in 66 patients at an average of 11.53 ± 11.13 months. The recurrence rate was 37.9%. Of these patients, 13 (19.7%) received retreatment. The double microcatheter technique can be performed in ruptured aneurysms with high technical success and low morbidity/mortality. However, recurrence remains a problem, and patients should be followed up regularly.

An objective method for pedestrian occlusion level classification

Pedestrian detection is among the most safety-critical features of driver assistance systems for autonomous vehicles. One of the most complex detection challenges is that of partial occlusion, where a target object is only partially available to the sensor due to obstruction by another foreground object.A number of current pedestrian detection benchmarks provide annotation for partial occlusion to assess algorithm performance in these scenarios, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. In addition, current occlusion level annotation methods contain a high degree of subjectivity by the human annotator. This can lead to inaccurate or inconsistent reporting of an algorithm’s detection performance for partially occluded pedestrians, depending on which benchmark is used. This research presents a novel, objective method for pedestrian occlusion level classification for ground truth annotation. Occlusion level classification is achieved through the identification of visible pedestrian keypoints and through the use of a novel, effective method of 2D body surface area estimation. Experimental results demonstrate that the proposed method reflects the pixel-wise occlusion level of pedestrians in images and is effective for all forms of occlusion, including challenging edge cases such as self-occlusion, truncation and inter-occluding pedestrians.

Class-Aware Feature Aggregation Network for Video Object Detection

Recent progress in video object detection (VOD) has shown that aggregating features from other frames to capture long-range contextual information is very important to deal with the challenges in VOD, such as partial occlusion, motion blur, etc. To exploit more effective feature aggregation, we propose several improvements over previous works in this paper: (1) a class-aware pixel-level feature aggregation module, which characterizes a pixel by exploiting the context information lying in the instances from both the current frame and other frames. Different from the previous non-local operation, the proposed class-aware pixel-level feature aggregation filters out most of the noisy information from the large scope of background and objects in different classes, and only enhances representation of a foreground pixel with the same class instances with limited ambiguous information; (2) a class-aware instance-level feature aggregation module, which aggregates features for object proposals by learning two kinds of relations: the temporal dependencies among the same class object proposals from support frames sampled in a long time range or even the whole sequence, and spatial topology relation among proposals of different objects in the target frame. The homogeneity constraint in instance-level feature aggregation filters out many defective proposals, making the feature aggregation more accurate; and (3) a correlation-based feature alignment module embedded in the instance-level feature aggregation, which aligns the feature maps of the support and target proposals. Without bells or whistles, the proposed method achieves state-of-the-art performance on the ImageNet VID dataset without any post-processing methods. This project is publicly available <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LiangHann/Class-aware-Feature-Aggregation-Network-for-Video-Object-Detection</uri> .

Semi-supervised learning approach for localization and pose estimation of texture-less objects in cluttered scenes

3D object recognition and 6D pose estimation are crucial and fundamental endeavours for industrial assembly line automation such as robotic controlled pick-and-place. While the problem on textured objects is extensively studied, it is still an open research topic for texture-less industrial parts, e.g, solid cylinder and hollow tube, which are symmetric and appear similar in shapes from many viewing perspectives, causing pose ambiguity. Also, the industrial assembly line environment is usually cluttered and the captured data is noisy, which makes this task even more challenging. In this paper, we propose a novel object localization and pose estimation technique using RGB images and depth maps of industrial assembly parts. Our segmentation model is fully morphological and unsupervised for localizing the region of interest containing the target object extracted from the depth map. Our segmentation technique is effective in the presence of partial occlusion, multiple objects, and cluttered scenes. We use a model based approach for object recognition based on Stochastic Gradient Descent trained on features of Histogram of Oriented Gradients (HOG) and invariant moments of the region of interest containing the target object. We generate synthetic training images automatically from the CAD models of the industrial parts. We use a contour matching strategy based on Dynamic Time Warping (DTW) algorithm to estimate the optimal 6D pose of the object from a set of candidates. Experimental results show that our proposed approach competes and demonstrates advantages on the challenging T-LESS dataset compared to the state-of-the-art methods. • We introduce a strategy for generating multiple viewpoint synthetic images from a CAD model. • We introduce a non-supervised clustering process for object segmentation based on a depth map that does not require a training stage. • We demonstrate how different HOG feature parameters can be tuned to achieve better prediction and performance. • We present a new real-time strategy for object pose estimation using Dynamic Time Warping (DTW) between the target object contour and best prior candidates. • We show that our method achieves better time performance than other works for pose estimation.

Omega-Test: A Predictive Early-Z Culling to Improve the Graphics Pipeline Energy-Efficiency.

The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is to determine which parts of every object are visible in the final image. There are different approaches to solve the visibility problem, the Z-Test being the most common. A main factor that significantly penalizes the energy efficiency of a GPU, especially in the mobile arena, is the so-called overdraw, which happens when a portion of an object is shaded and rendered but finally occluded by another object. This useless work results in a waste of energy; however, a conventional Z-Test only avoids a fraction of it. In this article we present a novel microarchitectural technique, the Omega-Test, to drastically reduce the overdraw on a Tile-Based Rendering (TBR) architecture. Graphics applications have a great degree of inter-frame coherence, which makes the output of a frame very similar to the previous one. The proposed approach leverages the frame-to-frame coherence by using the resulting information of the Z-Test for a tile (a buffer containing all the calculated pixel depths for a tile), which is discarded by nowadays GPUs, to predict the visibility of the same tile in the next frame. As a result, the Omega-Test early identifies occluded parts of the scene and avoids the rendering of non-visible surfaces eliminating costly computations and off-chip memory accesses. Our experimental evaluation shows average EDP savings in the overall GPU/Memory system of 26.4 percent and an average speedup of 16.3 percent for the evaluated benchmarks.

Single Emotion Recognition from Multiple Faces with Fuzzy System

Emotional inference by recognizing facial expressions in an image or video has been actively conducted for several years, for example, visual emotion recognition and emotional state recognition. The limitations of illumination conditions, variations of head poses, and partial occlusion are the most challenging problems in image processing. Furthermore, recognizing and inferring insignificant objects in the background is complex. These factors degrade performance. We collected evidence from human sensing through a psychological approach rather than a numerical approach, and converted it into a language through fuzzy theory. This can not only be intuitively explained, but it is also possible to evaluate and analyze numerically. The predicted inference accuracy increased by approximately 9.7% compared with the simple average method.