scholarly journals Automatic Focusing Method of Microscopes Based on Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hongjun Zhang ◽  
Jin Yao
Keyword(s):  
Image Processing ◽  
Focal Length ◽  
Target Position ◽  
Selection Method ◽  
Local Extremum ◽  
Hill Climbing ◽  
Image Sharpness ◽  
Evaluation Algorithm ◽  
Window Selection ◽  
The Impact

Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

scholarly journals RELIABILITY OF THE GEOMETRIC CALIBRATION OF AN HYPERSPECTRAL FRAME CAMERA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1701-1707
Author(s):  
M. A. Musci ◽  
I. Aicardi ◽  
P. Dabove ◽  
A. M. Lingua
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Environmental Conditions ◽  
Precision Agriculture ◽  
Focal Length ◽  
Geometric Calibration ◽  
Fabry Perot ◽  
The Impact ◽  
Orientation Parameters

<p><strong>Abstract.</strong> One of the main tools for high resolution remote sensing and photogrammetry is the lightweight hyperspectral frame camera, that is used in several application areas such as precision agriculture, forestry, and environmental monitoring. Among these types of sensors, the Rikola (which is based on a Fabry–Perot interferometer (FPI) and produced by Senop) is one of the latest innovations. Due to its internal geometry, there are several issues to be addressed for the appropriate definition and estimation of the inner orientation parameters (IOPs). The main problems concern the possibility to change every time the sequence of the bands and to assess the reliability of the IOPs. This work focuses the attention on the assessment of the IOPs definition for each sensor, considering the impact of environmental conditions (e.g., different time, exposure, brightness) and different configurations of the FPI camera, in order to rebuild an undistorted hypercube for image processing and object estimation. The aim of this work is to understand if the IOPs are stable over the time and if and which bands can be used as reference for the calculation of the inner parameters for each sensor, considering different environmental configurations and surveys, from terrestrial to aerial applications. Preliminary performed tests showed that the focal length percentage variation among the bands of different experiments is around 1%.</p>

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

scholarly journals Innovative techniques for the improvement of industrial noise sources identification by beamforming

Noise Mapping ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 129-137
Author(s):  
Giorgio Baldinelli ◽  
Francesco Bianchi ◽  
Danilo Costarelli ◽  
Francesco D’Alessandro ◽  
Flavio Scrucca ◽  
...  
Keyword(s):  
Image Processing ◽  
Noise Source ◽  
Focal Length ◽  
Noise Sources ◽  
Geometric Information ◽  
Industrial Noise ◽  
Sources Identification ◽  
Innovative Technique ◽  
Innovative Techniques

Abstract An innovative technique based on beamforming is implemented, at the aim of detecting the distances from the observer and the relative positions among the noise sources themselves in multisource noise scenarios. By means of preliminary activities to assess the optical camera focal length and stereoscopic measurements followed by image processing, the geometric information in the source-microphone direction is retrieved, a parameter generally missed in classic beamforming applications. A corollary of the method consists of the possibility of obtaining also the distance among different noise sources which could be present in a multisource environment. A loss of precision is found when the effect of the high acoustic reflectivity ground interferes with the noise source.

The Impact Of Optical Storage Technology On Image Processing Systems

1984 ◽  
Author(s):  
Daniel T. Garges ◽  
Gerald T. Durbin
Keyword(s):  
Image Processing ◽  
Optical Storage ◽  
Storage Technology ◽  
The Impact

Investigating the Impact of Similarity Metrics in an Unsupervised-Based Feature Selection Method

2017 ◽  
Author(s):  
Carine A. Dantas ◽  
Romulo de O. Nunes ◽  
Anne M.P. Canuto ◽  
Jolao C. Xavier-Junior
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Similarity Metrics ◽  
The Impact

The Result of Covid-19 Spreads in Education World

2021 ◽  
Vol 2 (6) ◽  
pp. 46-59
Author(s):  
Nur Laila Molla
Keyword(s):  
Positive Impact ◽  
Human Life ◽  
Sample Selection ◽  
Selection Method ◽  
Analytical Tool ◽  
Test Results ◽  
Human History ◽  
The World ◽  
Life On Earth ◽  
The Impact

COVID-19 is the most devastating pandemic in human history. Every aspect of human life on earth is disrupted, with little or no education. Many countries have decided to close schools, colleges, and universities, including Indonesia. The crisis came as a shock to governments in every part of the world, including Indonesia, which had to make drastic decisions to close their schools and save lives or to reopen schools in order to save workers’ livelihoods. The purpose of this study was to analyze the impact of the spread of covid-19 in the world of education. 75 respondents used for sample. The sample selection method used was the target sample. An analytical tool used to assess product duration and determination. Test results show that the spread of Covid-19 has a positive impact on the education world. The study found that the spread of covid-19 affects the world of education.

Photogrammetric Investigation of the Flying Shape of Spinnakers in a Twisted Flow Wind Tunnel

2009 ◽  
Author(s):  
Kai Graf ◽  
Olaf Müller
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
High Resolution ◽  
Wind Tunnel ◽  
Wind Velocity ◽  
Digital Cameras ◽  
Pattern Recognition Methods ◽  
Twisted Flow ◽  
High Resolution Images ◽  
The Impact

This paper describes a method for the acquisition of the flying shape of spinnakers in a twisted flow wind tunnel. The method is based on photogrammetry. A set of digital cameras is used to obtain high resolution images of the spinnaker from different viewing angles. The images are post-processed using image-processing tools, pattern recognition methods and finally the photogrammetry algorithm. Results are shown comparing design versus flying shape of the spinnaker and the impact of wind velocity and wind twist on the flying shape. Finally some common rules for optimum spinnaker trimming are investigated and examined.

Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net

2021 ◽  
Author(s):  
Shidong Li ◽  
Jianwei Liu ◽  
Zhanjie Song
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
The Impact

Abstract Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-ofinterest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽  
2017 ◽  
pp. 382-402
Author(s):  
Petre Anghelescu
Keyword(s):  
Image Processing ◽  
Cellular Automata ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Understanding ◽  
Software Application ◽  
Detection Algorithms ◽  
Human Image ◽  
The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

Image Processing Approaches and Disaster Management

2020 ◽  
pp. 163-187
Author(s):  
Surendra Rahamatkar
Keyword(s):  
Image Processing ◽  
Disaster Management ◽  
Edge Extraction ◽  
The Impact ◽  
Areal Type

This chapter presents the relevance of picture handling to distinguish different sorts of harm. For areal-type harm, 1) edge extraction, 2) unsupervised arrangement, 3) texture examination, and 4) edge improvement are suitable to distinguish harmed zone. For liner-type harm, it is hard to improve the permeability of harm partition by picture preparing. Likewise, the impact of overlaying office information to help staff to discover harm at an extraction is described.

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