Fabric Pilling Hairiness Extraction From Depth Images Based on the Predicted Fabric Surface Plane

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

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The study of needle type influence on woven fabric surface area during the sewing process

Textile Research Journal ◽

10.1177/00405175211019133 ◽

2021 ◽

pp. 004051752110191

Author(s):

Beti Rogina-Car ◽

Stana Kovačević

Keyword(s):

Computer Program ◽

Tensile Properties ◽

Surface Area ◽

Cotton Fabrics ◽

Woven Fabric ◽

Microscopic Images ◽

Needle Type ◽

Fabric Surface ◽

Needle Point

The aim of this study was to investigate the damage to cotton fabrics (ticking and damask) caused by stitching with three types of needle point shapes (R, SES and SUK) and four needle sizes (70, 80, 90 and 100 Nm). Damage to the yarn and the surface area of the hole were investigated. Based on the results, it can be concluded that two types of damage occur during sewing: the needle passes through the warp/weft (it displaces the yarn) and the needle damages the warp/weft. An analysis and comparison of the surface area of the holes was carried out, obtained by a computer program based on microscopic images. The results show greater damage to the yarn at the needle piercing point in the ticking due to higher density, friction and low yarn migration. The largest surface area of the holes was produced when sewing with SUK-designated needles on ticking and damask. When sewing damask, R-designated needles cause the least damage to the piercing point, whereas SES-designated needles give the best results when sewing the ticking. Thread damage was further confirmed by testing the tensile properties of the yarn at the needle piercing points.

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Two Independent Light Dilution Corrections for the SO2 Camera Retrieve Comparable Emission Rates at Masaya Volcano, Nicaragua

Remote Sensing ◽

10.3390/rs13050935 ◽

2021 ◽

Vol 13 (5) ◽

pp. 935

Author(s):

Matthew Varnam ◽

Mike Burton ◽

Ben Esse ◽

Giuseppe Salerno ◽

Ryunosuke Kazahaya ◽

...

Keyword(s):

Light Scattering ◽

Optical Depth ◽

Camera Calibration ◽

Wind Direction ◽

Emission Rate ◽

Emission Rates ◽

Depth Images ◽

Masaya Volcano ◽

Rapid Changes ◽

Volcanic Emission

SO2 cameras are able to measure rapid changes in volcanic emission rate but require accurate calibrations and corrections to convert optical depth images into slant column densities. We conducted a test at Masaya volcano of two SO2 camera calibration approaches, calibration cells and co-located spectrometer, and corrected both calibrations for light dilution, a process caused by light scattering between the plume and camera. We demonstrate an advancement on the image-based correction that allows the retrieval of the scattering efficiency across a 2D area of an SO2 camera image. When appropriately corrected for the dilution, we show that our two calibration approaches produce final calculated emission rates that agree with simultaneously measured traverse flux data and each other but highlight that the observed distribution of gas within the image is different. We demonstrate that traverses and SO2 camera techniques, when used together, generate better plume speed estimates for traverses and improved knowledge of wind direction for the camera, producing more reliable emission rates. We suggest combining traverses and the SO2 camera should be adopted where possible.

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RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽

10.3390/s21041299 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1299

Author(s):

Honglin Yuan ◽

Tim Hoogenkamp ◽

Remco C. Veltkamp

Keyword(s):

Pose Estimation ◽

Ground Truth ◽

3D Models ◽

Depth Image ◽

Great Success ◽

Estimation Algorithms ◽

Depth Images ◽

Object Pose Estimation ◽

Image Pairs ◽

Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

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Tea moisture content detection with multispectral and depth images

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2021.106082 ◽

2021 ◽

Vol 183 ◽

pp. 106082

Author(s):

Yuzhen Wei ◽

Yong He ◽

Xiaoli Li

Keyword(s):

Moisture Content ◽

Depth Images

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Thermal and breathability management of microencapsulated phase change material (MPCM) incorporated jute fabric

Journal of Engineered Fibers and Fabrics ◽

10.1177/15589250211029564 ◽

2021 ◽

Vol 16 ◽

pp. 155892502110295

Author(s):

Abdus Shahid ◽

Solaiman Miah ◽

Abdur Rahim

Keyword(s):

Phase Change ◽

Phase Change Material ◽

Thermal Fluctuation ◽

Water Repellency ◽

Air Permeability ◽

Jute Fabric ◽

Moisture Management ◽

Microencapsulated Phase Change Material ◽

Fabric Surface ◽

Change Material

Jute bags are widely used to carry food grains and other materials that may be prone to quality deterioration due to thermal fluctuation. Thermal and moisture properties play a significant role in the packaging materials in the form of a container. This study deals with the effect of microencapsulated phase change material (MPCM) with hydrophobic binder on thermal and moisture management properties of jute fabric. Jute fabric was treated with MPCM by pad-dry-cure method. The treated sample was characterized by thermogravimetric analysis (TGA), differential scanning colorimeter (DSC), scanning electron microscope (SEM), moisture management tester (MMT), and air permeability tester. The results revealed that MPCM treated jute fabric shows greater thermal stability and heat absorption ability of 10.58 J/g while changing from solid to liquid phase. The SEM image ensures even distribution of MPCMs on fabric surface and surface roughness was also observed using image processing software. The air permeability was found to decrease whereas the water repellency enhanced in the developed sample.

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HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints

Sensors ◽

10.3390/s21041356 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1356

Author(s):

Linda Christin Büker ◽

Finnja Zuber ◽

Andreas Hein ◽

Sebastian Fudickar

Keyword(s):

Color Images ◽

Depth Image ◽

Accuracy Evaluation ◽

Timed Up And Go ◽

Position Errors ◽

Depth Images ◽

Upper And Lower Extremities ◽

Rgb Images ◽

Human Joints ◽

Body Joints

With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).

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