In aeronautics, engineering, medicine, robotics and other industries, optical methods are widely used to measure the geometry and surface deformation of various objects from their images. These methods are based on di...
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A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials...
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ISBN:
(纸本)9781510667877;9781510667884
A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.
Context. Binary stars are invaluable tools that can be used to precisely measure the fundamental properties of stars, to test stellar models, and further our understanding of stellar evolution. Stellar binarity may al...
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Context. Binary stars are invaluable tools that can be used to precisely measure the fundamental properties of stars, to test stellar models, and further our understanding of stellar evolution. Stellar binarity may also play an important role in the formation and evolution of exoplanetary systems. Aims. We provide a technique for resolving intermediate-separation binaries stars with medium-sized telescopes (i.e. diameter less than or equal to 2.5 metres) at wavelengths around 825 nm in the super-resolution range (i.e. below the limit defined by the Rayleigh criterion). Methods. We combined two well-known algorithms that have been applied to reduce the halo in lucky imaging observations: COvariancE of Lucky images and the Lucky Imaging Speckle Suppression Algorithm. We reviewed the fundamentals of both algorithms and describe a new technique called Lucky Imaging Super resolution Technique (LIST), which is optimized for peak highlighting within the first ring of the Airy pattern. To validate the technique, we carried out several observing campaigns of well-known binary stars with the FastCam instrument (FC) on the 1.52 m Carlos S & aacute;nchez Telescope (TCS) and 2.56 m Nordic Optical Telescope (NOT), both located at the Observatorios de Canarias (OCAN). Results. The projected angular separation between objects was resolved by applying LIST to FC data taken with TCS and NOT, with a result below 0.15 ''. It can go down to approximately 0.05 '', given the limitations of the detector plate scale. This is, to our knowledge, the first time that binary companions with such small angular separations have been detected using only lucky imaging at optical wavelengths. The average accuracy achieved for the angular separation measurement is 16 +/- 2 mas with NOT and is 20 +/- 1 mas with TCS. The average accuracy obtained for the position angle measurement is 9.5 degrees +/- 0.3 degrees for NOT and 11 degrees +/- 2 degrees for TCS. We also made an attempt to measure the relative
Unlike diseases of the human body, plant diseases don't camouflage themselves within the body of the crop. The leaves reflect the infection with a change in color, shape, texture or a combination of the three. Hen...
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Thermal imaging has long been utilized across industries to maintain electrical equipment and detect faults in machines, ensuring their reliable operation. Infrared thermography, or thermal imaging, has emerged as a p...
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In country such as India due to monsoon and frequent climatic changes leads to natural disasters such as floods, drought, landslides, cyclones, forest fire, earthquake and so on. The post disaster badly impacts on hum...
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Multispectral data is obtained using special sensors that measure reflected or emitted energy in certain spectral ranges. This data represents information about various properties of objects, such as color, texture, s...
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In stereoscope-based Minimally Invasive Surgeries (MIS), dense stereo matching plays an indispensable role in 3D shape recovery, AR, vR, and navigation tasks. Although numerous Deep Neural Network (DNN) approaches are...
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In stereoscope-based Minimally Invasive Surgeries (MIS), dense stereo matching plays an indispensable role in 3D shape recovery, AR, vR, and navigation tasks. Although numerous Deep Neural Network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack of open-source annotated data set and the limitation of the task-specific pre-trained DNNs. Among the prior-free stereo matching algorithms, there is no successful real-time algorithm in none GPU environment for MIS. This paper proposes the first CPU-level real-time prior-free stereo matching algorithm for general MIS tasks. We achieve an average 17 Hz on 640*480 images with a single-core CPU (i5-9400) for surgical images. Meanwhile, it achieves slightly better accuracy than the popular ELAS. The patch-based fast disparity searching algorithm is adopted for the rectified stereo images. A coarse-to-fine Bayesian probability and a spatial Gaussian mixed model were proposed to evaluate the patch probability at different scales. An optional probability density function estimation algorithm was adopted to quantify the prediction variance. Extensive experiments demonstrated the proposed method's capability to handle ambiguities introduced by the textureless surfaces and the photometric inconsistency from the non-Lambertian reflectance and dark illumination. The estimated probability managed to balance the confidences of the patches for stereo images at different scales. It has similar or higher accuracy and fewer outliers than the baseline ELAS in MIS, while it is 4-5 times faster. The code and the synthetic data sets are available at https://***/JingweiSong/BDIS-v2.
The quality of computer vision systems to detect abnormalities in various medical imaging processes, such as dual-energy X-ray absorptiometry, magnetic resonance imaging (MRI), ultrasonography, and computed tomography...
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The quality of computer vision systems to detect abnormalities in various medical imaging processes, such as dual-energy X-ray absorptiometry, magnetic resonance imaging (MRI), ultrasonography, and computed tomography, has significantly improved as a result of recent developments in the field of deep learning. There is discussion of current techniques and algorithms for identifying, categorizing, and detecting DFU. On the small datasets, a variety of techniques based on traditional machine learning and imageprocessing are utilized to find the DFU. These literary works have kept their datasets and algorithms private. Therefore, the need for end-to-end automated systems that can identify DFU of all grades and stages is critical. The study's goals were to create new CNN-based automatic segmentation techniques to separate surrounding skin from DFU on full foot images because surrounding skin serves as a critical visual cue for evaluating the progression of DFU as well as to create reliable and portable deep learning techniques for localizing DFU that can be applied to mobile devices for remote monitoring. The second goal was to examine the various diabetic foot diseases in accordance with well-known medical categorization schemes. According to a computer vision viewpoint, the authors looked at the various DFU circumstances including site, infection, neuropathy, bacterial infection, area, and depth. Machine learning techniques have been utilized in this study to identify key DFU situations as ischemia and bacterial infection.
Medical image analysis is an invaluable tool in medicine. Different imaging modalities provide an effective means for mapping images that can feed machine and deep learning models which can significantly contribute to...
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