Traditional Mask Research Articles

Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.

The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at an altitude of 15 m, followed by image stitching and plot boundary recognition via a mask region-based convolutional neural network (Mask R-CNN). The images were then segmented into plot-scale subgraphs, which were categorized into 3 growth stages. The panicle vision transformer (Panicle-ViT), which integrates a multipath vision transformer and replaces the Mask R-CNN backbone, accurately detects panicles. Additionally, the Res2Net50 architecture classified panicle types with 4 angles of 0°, 15°, 45°, and 90°. The results confirm that the performance of Plot-Seg is comparable to that of manual segmentation. Panicle-ViT outperforms the traditional Mask R-CNN across all the datasets, with the average precision at 50% intersection over union (AP50) improved by 3.5% to 20.5%. The PNpA quantification for the full dataset achieved superior performance, with a coefficient of determination (R 2) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.

Pipeline multi-type high consequence area identification based on mask R-CNN with fused attention mechanisms

Identification of high consequence areas is an important task in pipeline integrity management. However, traditional identification methods are generally characterized by low efficiency, high cost and low accuracy. For this reason, this paper proposes a recognition method based on the improved algorithm Mask Region-based Convolutional Neural Network. Coordinate attention mechanism module is introduced into the traditional Mask R-CNN algorithm to improve the recognition accuracy and reduce the training time. For the identification results, GIS tools are utilized to establish high consequence zones along both sides of the pipeline, and the grade and scope of the high consequence zones are determined according to relevant specifications.In this paper, this method is used to identify the high-consequence area of a pipeline section in Guangdong Province, the results show that: 1, the improved algorithm in the identification of densely populated, geologic hazards, flammable and explosive high consequence zones of the average accuracy of the identification of 1.7%, 3.4%, 3.9%. 2, The method in this paper identifies 8 more building elements and 0.311 more kilometers of pipeline mileage compared to traditional identification methods. The method of this paper can provide a reference for the early identification of high consequence areas of pipelines.

An Enhanced Mask R-CNN Approach for Pulmonary Embolism Detection and Segmentation.

Pulmonary embolism (PE) refers to the occlusion of pulmonary arteries by blood clots, posing a mortality risk of approximately 30%. The detection of pulmonary embolism within segmental arteries presents greater challenges compared with larger arteries and is frequently overlooked. In this study, we developed a computational method to automatically identify pulmonary embolism within segmental arteries using computed tomography (CT) images. The system architecture incorporates an enhanced Mask R-CNN deep neural network trained on PE-containing images. This network accurately localizes pulmonary embolisms in CT images and effectively delineates their boundaries. This study involved creating a local data set and evaluating the model predictions against pulmonary embolisms manually identified by expert radiologists. The sensitivity, specificity, accuracy, Dice coefficient, and Jaccard index values were obtained as 96.2%, 93.4%, 96.%, 0.95, and 0.89, respectively. The enhanced Mask R-CNN model outperformed the traditional Mask R-CNN and U-Net models. This study underscores the influence of Mask R-CNN's loss function on model performance, providing a basis for the potential improvement of Mask R-CNN models for object detection and segmentation tasks in CT images.

A framework to identify guano on photovoltaic modules in offshore floating photovoltaic power plants

Guano are an important factor affecting the cleanliness of photovoltaic modules on floating solar power plants at sea. It can lead to a decrease in photoelectric conversion efficiency, power loss, and even the occurrence of “hot spots”, thereby causing damage to the components. Therefore, the segmentation and detection of guano are crucial for visual automation in the cleaning and inspection processes. However, the composition, density, and thickness of guano naturally vary, leading to inconsistent levels of transparency and color. The uneven intensity of guano images greatly reduces the accuracy of segmentation and detection. Addressing this issue, this study proposes a segmentation algorithm based on combining different color channels to segment guano on the surface of photovoltaic modules. The mean shift method is used for adaptive segmentation to facilitate the detection of guano. Furthermore, the segmentation results obtained by this method are introduced into the input space to improve the traditional Mask RCNN. In addition, this study successfully produced a dataset of guano on the surface of photovoltaic modules using on-site data collection and with the help of a platform built in-house in the laboratory. The experimental results on the self-constructed dataset demonstrate that the enhanced Mask R-CNN model has shown an approximate increase of 5.9% and 6.0% in mAP values for object recognition and segmentation compared to the traditional Mask R-CNN model. This indicates the effectiveness of the methodology proposed in this study.

Exploring Local Wisdom: Analysis of Non-Verbal Communication Skills in Cirebon Traditional Mask Dance Audiences

This research delves into the intriguing realm of mask dance and its role as a nonverbal communication medium for audiences in Cirebon. By examining dance skills such as movement, rhythm, expression, level, and floor patterns, the study aims to unravel the profound impact of this art form. A qualitative approach and descriptive research design are adopted to achieve this, allowing for a comprehensive exploration of the performance form and the far-reaching effects of mask dance. The research findings promise to provide profound insights into the significance of dance in preserving local culture. By shedding light on the intricate nuances of mask dance, this study contributes to a deeper understanding of the art's value and cultural implications. Not only does it enhance our understanding of the art form itself, but it also holds practical implications for researchers and stakeholders in the field. The knowledge gleaned from this research equips researchers and stakeholders with a more profound appreciation and recognition of the richness embedded within the Cirebon mask dance. It serves as a testament to the power of dance as a nonverbal form of communication, capable of transcending language barriers and conveying messages that resonate with audiences on a profound level. This research serves as a valuable resource, providing a foundation upon which future studies and initiatives can build, further enhancing the preservation and promotion of local art and culture.

The Effect of Using a Traditional Mask of Moringa Leaves for Dry Facial Skin Care

Dry facial skin can be caused by several factors including age, sun exposure, weather, genetics, skin structure, diet, environmental factors, and skin diseases. The purpose of this study was to determine the effect of using traditional Moringa leaf masks for dry facial skin care. This type of research is experimental research with quasi-experimental methods. The research sample consisted of 6 samples using the purposive sampling method. The type of data used is primary data. Data analysis used the normality test, homogeneity test, and hypothesis testing (ANOVA), followed by the Duncan test. Analysis of normality and homogeneity of the data is used to test prerequisite analysis before testing the hypothesis. The results showed that in the control group (X0) without the use of traditional moringa leaf masks, there was no change in the level of skin moisture and the level of smoothness of facial skin texture. In the experimental group X1 and X2 using a traditional leaf mask showed a significant change in the level of moisture and smoothness of facial skin texture. There are differences in the effect of treating dry faces on women aged 35-45 years using traditional masks from Moringa leaves in three different treatments for indicators of moisture and facial skin texture with a significance of 0.016 <0.050.

Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM

• A method for individual identification of cows in unconstrained barn was developed. • The extracted and selected features have strong adaptability to pattern deformation. • A method involving fusion of Mask R-CNN and SVM was used to identify cows. • To reduce the running time and number of model parameters, Mask R-CNN was modified. The identification of individual dairy cows is an important prerequisite for dairy cow behaviour analysis and disease detection. Computer vision-based cow recognition is a noncontact and stress-free approach. In a free environment in a barn, due to changes in camera position and angle, recorded cow patterns are often deformed, making individual cow identification difficult. For cows in an unconstrained barn environment, this paper proposes a method for individual cow identification. First, a top-view image of a cow is obtained, and an improved Mask R-CNN is used to segment this image and extract the shape features of the cow’s back. Then, a Fisher approach is used to select the best feature subset, and a support vector machine (SVM) classifier is applied to identify individual cows. To verify the effectiveness of the target detection algorithm, the proposed method is compared with the traditional Mask R-CNN model, and the precision, recall, F1 score, average run time per image and average precision of the improved Mask R-CNN model are 98.21%, 96.48%, 97.34%, 1.02 s, and 97.39%, respectively. An SVM classifier trained based on the obtained shape features is used for individual cow identification. The proposed method achieves a 98.67% cow identification accuracy based on a dataset containing top-view images of 48 cows. The results demonstrate the effectiveness of the proposed cow identification method and its significant potential for use in precision dairy cow management.

Formulation of Traditional Mask Powder Containing the Mixture of Coffea robusta, Angelica keiskei and Oryzae sativa, and its Activity as Tyrosinase Enzyme Inhibitor

Formulation of Traditional Mask Powder Containing the Mixture of Coffea robusta, Angelica keiskei and Oryzae sativa, and its Activity as Tyrosinase Enzyme Inhibitor

Moving Car Recognition and Removal for 3D Urban Modelling Using Oblique Images

With the progress of photogrammetry and computer vision technology, three-dimensional (3D) reconstruction using aerial oblique images has been widely applied in urban modelling and smart city applications. However, state-of-the-art image-based automatic 3D reconstruction methods cannot effectively handle the unavoidable geometric deformation and incorrect texture mapping problems caused by moving cars in a city. This paper proposes a method to address this situation and prevent the influence of moving cars on 3D modelling by recognizing moving cars and combining the recognition results with a photogrammetric 3D modelling procedure. Through car detection using a deep learning method and multiview geometry constraints, we can analyse the state of a car’s movement and apply a proper preprocessing method to the geometrically model generation and texture mapping steps of 3D reconstruction pipelines. First, we apply the traditional Mask R-CNN object detection method to detect cars from oblique images. Then, a detected car and its corresponding image patch calculated by the geometry constraints in the other view images are used to identify the moving state of the car. Finally, the geometry and texture information corresponding to the moving car will be processed according to its moving state. Experiments on three different urban datasets demonstrate that the proposed method is effective in recognizing and removing moving cars and can repair the geometric deformation and error texture mapping problems caused by moving cars. In addition, the methods proposed in this paper can be applied to eliminate other moving objects in 3D modelling applications.

Mask R-CNN-based Cat Class Recognition and Segmentation

Aiming at the low accuracy of the traditional Mask R-CNN applied to the image segmentation of different cats, an improved Mask R-CNN recognition and segmentation algorithm was proposed. The third channel of the FPN feature extraction path is added to obtain more comprehensive feature information, improve the accuracy of the segmentation mask and reduce the training time. The experimental results show that the method achieves 87.54% segmentation accuracy on the Kaggle dog and cat classification detection dataset, which is 13.57% better than the accuracy of the traditional Mask R-CNN algorithm on the same dataset, and has better detection and segmentation performance, providing a new method for the study of instance segmentation.

427 Comparison of PAP Interface Pressure on the Nasal Bridge: Soft Cloth vs. Traditional Masks

Abstract Introduction Pressure between a CPAP mask and the skin is a significant contributor to irritation and pressure ulcers, an area of localized soft tissue ischemic necrosis caused by prolonged pressure over bony prominences that exceeds supra capillary pressure (70 mmHg). We hypothesized that cloth masks (CM) would exert a lower nasal bridge pressure than traditional mask (TM) products constructed of silicone and plastic. Methods We evaluated the pressure exerted by seven types of nasal masks in three trials onto the nasal bridge of two healthy adult volunteers, one female, one male, while they received 10 cm H₂O of CPAP. Five commercially available CMs (SleepWeaver® 3D, SleepWeaver® Advance Pediatric, SleepWeaver® Élan, and SleepWeaver® Prevent, Circadiance®, LLC) were tested as were three TMs constructed primarily of silicone and plastic (DreamWisp™, Philips Respironics, Inc.; Mirage™, ResMed; Zest™, Fisher & Paykel Healthcare). Pressure was detected using a texsens®-g low pressure sensor force measuring device. Pressure data from each 30 second trial were summarized as the median value after confirming that pressure did not vary by time (one-way ANOVA, p = 0.7393). Median values were then compared across trials, subjects, and masks using one-way ANOVAs and student’s t-tests. Results After confirming that pressure did not vary by trial (one-way ANOVA, p=0.4585) or subject (t-test, p=0.0938), pressure data were summarized by mask. On average, CMs exerted 37.0 (17.7) mmHg of nasal bridge pressure, although there was significant variation across masks (one-way ANOVA, p < 0.0001). Conversely, TMs averaged 112 (38.5) mmHg of nasal bridge pressure without significant variation across masks (one-way ANOVA, p=0.1291). CMs averaged 75.26 mmHg less pressure than TMs (p < 0.0001), a difference of 67 percent. Conclusion The data supports the hypothesis that pressure from CMs on the bridge of the nose are significantly lower than a sample of TMs with similar shape and style, and the null hypothesis was rejected. Furthermore, the average CM was below the threshold for capillary closing, in contrast to the average TM. Therefore, for CPAP users with predicted or existing skin sensitivity, comfort and/or compliance concerns, CM should be considered as a first choice in mask selection. Support (if any):

A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

Multi-modal semantic image segmentation

We propose a modality invariant method to obtain high quality semantic object segmentation of human body parts, for four imaging modalities which consist of visible images, X-ray images, thermal images (heatmaps) and infrared radiation (IR) images. We first consider two modalities (i.e. visible and X-ray images) to develop an architecture suitable for multi-modal semantic segmentation. Due to the intrinsic difference between images from the two modalities, state-of-the-art approaches such as Mask R-CNN do not perform satisfactorily. Insights from analysing how the intermediate layers within Mask R-CNN work on both visible and X-ray modalities have led us to propose a new and efficient network architecture which yields highly accurate semantic segmentation results across both visible and X-ray domains. We design multi-task losses to train the network across different modalities. By conducting multiple experiments across visible and X-ray images of the human upper extremity, we validate the proposed approach, which outperforms the traditional Mask R-CNN method through better exploiting the output features of CNNs. Based on the insights gained on these images from visible and X-ray domains, we extend the proposed multi-modal semantic segmentation method to two additional modalities; (viz. heatmap and IR images). Experiments conducted on these two modalities, further confirm our architecture’s capacity to improve the segmentation by exploiting the complementary information in the different modalities of the images. Our method can also be applied to include other modalities and can be effectively utilized for several tasks including medical image analysis tasks such as image registration and 3D reconstruction across modalities.

History of Inheritance of Wayang Topeng Malangan (Malang Traditional Mask Puppet) in Pakisaji and Tumpang

This study aims to explain the history and process of inheriting mask puppet art in two representative hermitages (padepokan) (in Malang, namely Padepokan Asmoro Bangun in Pakisaji sub-district and Mangun Dharma in Tumpang sub-district). This research uses a qualitative approach. Data collection techniques are done through interviews, observation, and study documents. The process of inheritance includes art practices such as dancing, puppeteering, playing musical instruments, and making masks and transferring knowledge about Wayang Topeng Malangan. Inheritance is generally done since offsprings (children/grandchildren) are still at an early age. The history of the inheritance of the traditional mask puppet art in Padepokan Asmoro Bangun has occurred for six generations of this family since before 1930 starting from the Serun period. The sixth Padepokan Asmoro Bangun Heirs are two daughters named Winnie Padnecwara (15 years old) and Dita Andhika Raja Padmi (12 years old) who learned this art since the age of four. Padepokan Mangun Dharma is the home of mask puppet art, as well as music, traditional dance, and theatre, macapat, puppetry, and shadow puppets. The history of the inheritance of this hermitage began with the great-grandfather of the puppeteer Soleh namely Kek Sainten (Grandpa Sainten). The current heir of Padepokan Mangun Dharma is the son of the puppeteer Soleh, Supriyono Hadi Prasetyo, who also learned puppetry both autodidact and formally through college. Although inheritance occurs vertically, which is hereditary in each hermitage, horizontal inheritance also occurs between the two. This indicates that the inheritance process of Padepokan Asmoro Bangun and Mangun Dharma are related to each other.

Research on abnormal object detection in specific region based on Mask R-CNN

As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

Development of Traditional Mask Mold as Teaching Tool in Learning of Arts, Culture, and Craftsmanship

Based on preliminary observation at SDN Kedungane 02 Semarang, learning media in art, culture, and craft lesson subject in the lesson of making a mask was still limited. It was necessary to develop a learning media which supporting the practice of making a mask.The type of research was Research and Development. The steps consist of potential and problem, data collection, product designing, design validation, design revision, small group product testing, product revision, large group product testing, product revision, and final product making. The main subject of this research was the students of grade V SDN Kedungpane 02 consists of 39 students. Technical collecting data used both test and non-test techniques. Data analysis technique used percentage descriptive statistic, validity test, reliability test, and N-gain. The results of the research showed: (1) the mask mould product design which was being developed was a traditional mask from Java Island and being adjusted to the level of student development, (2) the result of product feasibility assessment from material expert was 96,4% (very feasible) and media expert was 94,2% (very feasible), (3) the effectiveness of the media was being seen from the responses of students and teachers with the result belong to very good criteria, and there was an moderated improvement in pre test and post test result. Showed by the result of gain test value which was 0,52 and the average of performance assessment value was 88,36. The conclusion of this research was the traditional mask-mould media in the lesson of making a mask at class V of elementary school was feasible to be used and effective in improving the result of student art work.

Making the Traditional Mask a Thing of the Past

The first reaction to seeing the DreamPort is always something special. Right up front, you notice it doesn’t have headgear, and the mind races a bit with the possibilities. Then you hold it and realize how light it is resting in your hand. Aside from the tube, it doesn’t conjure the phrase “CPAP mask” on your tongue at all. First off, it’s not a traditional shape you’ve seen before.

The Structure of Laughter in Korean Traditional Mask Play <Yangjubyulsandaenori>

The Structure of Laughter in Korean Traditional Mask Play <Yangjubyulsandaenori>

Use of Traditional Mask Images in Design Development for Fashion-Cultural Products

This paper developed motifs through the use of images of masks with which the Korean innate consciousness of aesthetics is expressed. The motifs were applied to fashion items. This paper investigated the concept, origin and types of traditional masks through the existing literature. Using the computer graphic programs of Illustrator and Photoshop CS2, three basic motifs were set from the images of the nobleman, Bune of Hahoi Tal and Byeongsan Tal. Each motif was extended into two motifs by changing shape and color. For the basic motif design, this study sought to express contemporary images, suitably for each fashion item, while maintaining the basic shape of the masks and their traditional images. In addition, this study set the concept of the design so that could be accepted by various age groups. For the design of handkerchiefs, a rotating array, involving enlargement, reduction, repetition, and the gradation of motifs, as well as a diagonal symmetric array, and all-over patterns were developed. The T-shirt design created here was divided into a half-sleeve box type, a round neckline sleeveless type, a V-neckline close-fitting sleeveless type, and a V-neckline close-fitting cap sleeve type. For the design of necklaces, OLED or LED, which are considered as a future display type, was used. Additionally, the production of an entertainment styled new atmosphere is proposed.