Range Images
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2022 ◽  
pp. 4195-4207
Marwa Mohamed ◽  
Zahra Ezz El Din ◽  
Laila Qais

    A three-dimensional (3D) model extraction represents the best way to reflect the reality in all details. This explains the trends and tendency of many scientific disciplines towards making measurements, calculations and monitoring in various fields using such model. Although there are many ways to produce the 3D model like as images, integration techniques, and laser scanning, however, the quality of their products is not the same in terms of accuracy and detail. This article aims to assess the 3D point clouds model accuracy results from close range images and laser scan data based on Agi soft photoscan and cloud compare software to determine the compatibility of both datasets for several applications. College of Science, Departments of Mathematics and Computer in the University of Baghdad campus were exploited to create the proposed 3D model as this area location, which is one of the distinctive features of the university, allows making measurements freely from all sides. Results of this study supported by statistical analysis including 2 sample T-test and RMSE calculation in addition to visual comparison. Through this research, we note that the laser3D model provides many points in a short time, so it will reduce the field work and also its data is faster in processing to produce a reliable model of the scanned area compared with data derived from photogrammetry, then the difference were computed for all the reference points.

M. P. Pavan Kumar ◽  
B. Poornima ◽  
H. S. Nagendraswamy ◽  
C. Manjunath ◽  
B. E. Rangaswamy

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7024
Marcos Alonso ◽  
Daniel Maestro ◽  
Alberto Izaguirre ◽  
Imanol Andonegui ◽  
Manuel Graña

Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.

А.Н. Ветров ◽  
А.Ю. Потлов

Задача повышения качества результатов медицинской диагностики и удобства их интерпретации является актуальной на современном этапе развития биомедицинской инженерии. Особый интерес представляют методы визуализации, применимые при диагностике онкологических заболеваний. Повышать достоверность медицинской диагностики таких патологических состояний предлагается посредством совмещения разнодиапазонных изображений, в частности сканов в инфракрасном и видимом диапазонах длин волн. Предлагается методика, в которой два изображения конкретного биообъекта, полученные от датчиков, работающих в разных частотных диапазонах, имеющие одинаковые пространственные параметры и сформированные с общего ракурса, сводятся в общее изображение чересстрочно. Новизна предлагаемой методики заключается в том, что после совмещения изображений производится взаимная передача заданных частей каждого пикселя соседним пикселям по вертикали. В полученном изображении каждый пиксель содержит информацию оптического и инфракрасного изображений в заданных пропорциях. Показано, что предлагаемая методика обеспечивает увеличение информативности в полученном изображении в шесть раз относительно исходных изображений. Предлагаемая методика совмещения разнодиапазонных изображений может быть применена в различных прикладных областях In the medical diagnostics of diseases, it is necessary to obtain the most reliable information in order to obtain the correct diagnosis and, as a result, the correct treatment for the patient. One of the methods of diagnostic studies of oncological diseases of a near-surface nature is to obtain infrared images. It is possible to increase the reliability of information by combining images obtained from thermal imagers, as well as from television video cameras. In this paper, we propose a technique in which two images of a particular object obtained from sensors operating in different frequency ranges, having the same spatial parameters, and formed from the same angle, are interlaced into a common image. The novelty of the proposed method lies in the fact that after combining the images, the specified parts of each pixel are mutually transmitted to the neighboring pixels vertically. In the resulting image, each pixel contains information of optical and infrared images in appropriate proportions. It is shown that the proposed method provides an increase in information content in the resulting image six times relative to the original image. The proposed technique for combining multi-range images can be applied in various areas

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5526
Wojciech Grodzki ◽  
Wiera Oliferuk ◽  
Michał Doroszko ◽  
Jarosław Szusta ◽  
Leszek Urbański

The low-cycle deformation of 304L austenitic stainless steel was examined in terms of energy conversion. Specimens were subjected to cyclic loading at the frequency of 2 Hz. The loading process was carried out in a hybrid strain–stress manner. In each cycle, the increase in elongation of the gauge part of the specimen was constant. During experimental procedures, infrared and visible-range images of strain and temperature fields were recorded simultaneously using infrared thermography (IR) and digital image correlation (DIC) systems. On the basis of the obtained test results, the energy storage rate, defined as the ratio of the stored energy increment to the plastic work increment, was calculated and expressed in reference to selected sections of the specimen. It was shown that, before the specimen fracture in a specific area, the energy storage rate is equal to zero (the material loses the ability to store energy), and the energy stored during the deformation process is released and dissipated as heat. Negative and close-to-zero values of the energy storage rate can be used as a plastic instability criterion on the macroscale. Thus, the loss of energy storage ability by a deformed material can be treated as an indicator of fatigue crack initiation.

2021 ◽  
Vol 13 (18) ◽  
pp. 3640
Hao Fu ◽  
Hanzhang Xue ◽  
Xiaochang Hu ◽  
Bokai Liu

In autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. In order to enrich the LiDAR point cloud, this paper proposes a new technique that combines spatial adjacent frames and temporal adjacent frames. To eliminate the “ghost” artifacts caused by moving objects, a moving point identification algorithm is introduced that employs the comparison between range images. Experiments are performed on the publicly available Semantic KITTI dataset. Experimental results show that the proposed method outperforms most of the previous approaches. Compared with these previous works, the proposed method is the only method that can run in real-time for online usage.

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Radhika Bhagwat ◽  
Yogesh Dandawate

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.


The measurement methods using structured light have the advantage of being fast, accurate, and noncontact with the surface of the object. However, these methods have reached its limitation when measuring mechanical details with high surface gloss, due to the unpredictable reflection of incident rays after reaching to object’s surface that, consequently, leads to the simultaneous appearance of several regions with different brightness. To address this problem, we proposed a method of synthesizing extended dynamic range images based on changing the exposure time of the camera and adjusting the illumination of the projector light source so that 3D point coordinates in both bright and dark areas could be obtained through the process. The dual-camera structured light experimental model and the lightcrafter 4500 projector are synchronized through the trigger, using the gray code in combination with the line-shift projection pattern. Experimental results show that the proposed method can precisely reconstruct the 3D surface of mechanical details, while providing higher performance than the state-of-the-art methods.

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