images processing
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2021 ◽  
Vol 12 (3) ◽  
pp. 573-579
Author(s):  
Kalthom Adam H. Ibrahim ◽  
Mohammed Abdallah Almaleeh ◽  
Moaawia Mohamed Ahmed ◽  
Dalia Mahmoud Adam

This paper introduces the segmentation of Neisseria bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images segmentation. The images segmentation represent significant step in extracting images features and diagnoses the disease by computer software applications.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yangyang Tian ◽  
Wandeng Mao ◽  
Shaoguang Yuan ◽  
Diming Wan ◽  
Yuanhui Chen

The traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.


2021 ◽  
Vol 906 (1) ◽  
pp. 012080
Author(s):  
Mykola Yakymchuk ◽  
Ignat Korchagin ◽  
Arzu Javadova

Abstract The results of the application of mobile direct-prospecting technology of frequency-resonance processing and interpretation of satellite images and photo images at the sites hydrogen degassing in various regions are presented. Experimental reconnaissance studies were carried out to study the features of deep structure of the hydrogen degassing areas. The materials of instrumental measurements indicate that in regions of basalt volcano’s location with roots at different depths, signals at hydrogen frequencies are almost always recorded. When scanning the cross-section, responses from hydrogen are recorded from the upper edges of basaltic volcanoes to their roots. It can be assumed that basaltic volcanoes are a kind of channels through which hydrogen migrates to the upper horizons of the cross-section and further into the atmosphere. Within many basaltic volcanoes at a depth of 68 km, deep (living) water is synthesized. Hydrogen-rich water is curative and can be used for wellness purposes. All surveyed zones of longevity on Earth are located within basalt volcanoes, in which water synthesized at a depth of 68 km migrates to the surface and is used for water supply. Hydrogen deposits can be formed by basaltic volcanoes in adjacent sealed reservoirs. Within some survey areas, responses at hydrogen frequencies from limestones, dolomites and marls were recorded at shallow depths. Direct-prospecting technology can be used to study reservoirs in crystalline rocks (basalts including). Detailed studies and wells drilling in promising areas can be planned and carried out for hydrogen and living water at the same time. The result of investigation indicates the advisability of using direct-prospecting methods of frequency-resonance processing of satellite images to detect zones of hydrogen accumulation in areas of basalt volcano’s location, as well as in areas of hydrogen degassing. The use of mobile and low-cost technology will significantly speed up the exploration process for hydrogen, as well as reduce the financial costs for its implementation.


Author(s):  
Maximilian Lorenz ◽  
Matthias Menzl ◽  
Christian Donhauser ◽  
Michael Layh ◽  
Bernd R. Pinzer

AbstractPunching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.


2021 ◽  
Vol 2 (4) ◽  
pp. 5971-5981
Author(s):  
Jorge Luis Lozano Rodriguez ◽  
Juan Carlos Chang Chang Fun ◽  
Oscar Enrique Tang Cruz ◽  
Eusebio Idelmo Cisneros Tarmeño ◽  
Hernán Oscar Cortez Gutierrez ◽  
...  

La presente investigación responde a la necesidad de caracterizar la evolución temporal de los humedales mediante imágenes satelitales, su procesamiento, análisis, interpretación y discusión, pues los cambios climáticos hacen necesario tener en cuenta que son ecosistemas importantes de gran interés por las diferentes funciones que realizan. Las imágenes satelitales del satélite TERRA del sensor AQUA, fueron procesadas con el software ENVI y se determinó el parámetro del Índice de Vegetación de Diferencia Normalizada (NDVI), más el uso del Google Earth nos permitió a grandes rasgos discriminar entre zonas de vegetación, suelo desnudo, agua y la variación en el tiempo de los Humedales de la Región de Ayacucho. La teledetección nos ofrece grandes progresos en el conocimiento de la naturaleza, aunque es necesario un mayor rigor científico en la interpretación de los resultados y tener como objetivo eliminar los efectos ocasionados por la variabilidad en las condiciones de captación, distorsión provocada por la atmósfera, y la influencia de parámetros radiométricos geométricos tales como la radiancia, reflectancia, emisividad, posición del Sol, pendiente, y altitud. Por lo cual esperamos que contribuya al conocimiento de datos importantes quedando pendiente estimar otros parámetros como la humedad de suelos, evaporación, etc. a través de la cual se precisará y reorientar la tecnología propuesta.   images, processing, analysis, interpretation and discussion, as the climate changes make it necessary to consider that are important ecosystems of great interest in the different functions performed. Satellite images of the TERRA satellite AQUA sensor were processed with ENVI software and parameters Index Normalized Difference Vegetation (NDVI) was determined, plus the use of Google Earth allowed us to broadly discriminate between areas of vegetation, soil nude, water and variation in time of Wetlands in the region of Ayacucho. Remote sensing offers great progress in the knowledge of nature, although a greater scientific rigor in the interpretation of the results is necessary and aim to eliminate the effects caused by the variability in the conditions of recruitment, distortion caused by the atmosphere, and the influence of geometric radiometric parameters such as radiance, reflectance, emissivity, position of the Sun, slope, and altitude. So we hope to contribute to the knowledge of important data pending estimate other parameters such as soil moisture, evaporation, etc. through which shall specify and redirect the proposed technology.


2021 ◽  
Vol 9 (07) ◽  
pp. 928-946
Author(s):  
Wilfred Kombe Ibey ◽  
◽  
Jean-Paul Kibambe Lubamba ◽  
◽  

National stratification maps are essential to improve forest management systems. For the Democratic Republic of the Congo, the existing maps derived from remote sensing techniques do not allow an optimal representation of the diverse land cover classes constituting the national stratification scheme. This situation is inherent to the cloud persistence, the seasonality effects and the spatial resolution of the input satellite imagery used that is not always adequate for the discrimination of certain land cover classes. This paper explores a cloud-based median luminance best pixel approach to obtain a cloud-free mosaic of optimal quality. The mosaic produced has necessitated nearly 2,500 Landsat scenes and a following object-based classification enabled the generation of a stratification map for the year 2000 according to the national stratification theme. A stratified random sampling approach that required 1,141 reference samples allowed estimating the map accuracy at 79.32%. Land cover classes areas computed using standard good practices recommendations to estimate land areas indicated that the dense moist forest area was about 158,810,975 ± 7,460,671 ha representing 68.40% ± 3.21% of the country area. Thanks to the free, user-friendly and cloud-based platforms for satellite images processing, the methodology implemented is easily replicable for other tropical countries.


Author(s):  
Barbara Siemiątkowska ◽  
Krzysztof Gromada

Radar machine vision is an emerging research field in the mobile robotics. Because Synthetic ApertureRadars (SAR) are robust against weather and light condition, they provide more useful and reliable in formation than optical images. On the other hand, the data processing is more complicated and less researched than visible light images processing. The main goal of our reasarch is to build simple and efficient method of SAR image analysis. In this article we describe our research related to SAR image segmenta tion and attempts to detect elements such as the build ings, roads and forest areas. Tests were carried out for the images made available by Leonardo Airborne & Space System Company.


Author(s):  
Saudagar Punam

Tumors are complex. There are a lot of variations in sizes and location of tumor. This makes it really hard for complete understanding of tumor. Brain tumour is the abnormal growth of cells inside the brain cranium which limits the functioning of brain. Now a days, medical images processing is a most challenging and developing field. Automated detection of tumor in MRI is extremely crucial because it provides information about abnormal tissues which is important for planning treatment. The conventional method for defect detection in resonance brain images is time consuming. So, automated tumor detection methods are developed because it would save radiologist time and acquire a tested accuracy. The MRI brain tumor detection is complicated task due to complexity and variance of tumors.There are many previously implemented approaches on detecting these kinds of brain tumors. In this paper, we used and implement Convolutional Neural Network (CNN) which is one among the foremost widely used deep learning architectures for classifying a brain tumor into four types. i.e Glioma , Meningioma, Pituitary and No tumour. CNN may be used to effectively locate most cancers cells in brain via MRI. classification.


Author(s):  
Xiaoqing Tian ◽  
Yaling Li ◽  
Dingyifei Ma ◽  
Jiang Han ◽  
Lian Xia

Abstract In this paper, the control of strand width uniformity in extrusion-based additive manufacturing process based on machine vision is studied. Firstly, the images of the strand width are collected frame by frame by a CCD camera. Secondly, through a series of processes of images acquisition, images processing including images filtering, images binarization and information extraction, the useful information of strand width is obtained. Then, the theoretical relationship between the strand width and printing speed is obtained through experimental research, and a control model is obtained. Finally, by using the control model and the strand width obtained from images processing, the printing speed is adjusted to an appropriate value, which eventually led to the stabilization of the strand width. The uncontrolled and logarithmic controlled, are studied in this work. The results show that the logarithmic controlled strand width is more stable than the uncontrolled strand width. Therefore, the instability of strand width in material extrusion-based additive manufacturing process can be effectively solved by machine vision control.


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