An Automatic Microalgal Cells Counting Method

2014 ◽  
Vol 1010-1012 ◽  
pp. 178-181
Author(s):  
Yun Zhao ◽  
De Jian Zheng ◽  
Ying Shen
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Binary Image ◽  
Morphological Operations ◽  
Microscopic Image ◽  
Median Filtering ◽  
Counting Method ◽  
Automatic Counting

An automatic counting method for microalgal cells were proposed using digital image processing with characteristics of microalgae microscopic image considered. Firstly, the microalgae image was pretreated with graying, and was then applied the median filtering to remove the noise. Secondly, the image was applied Bot-hat conversion to enhance the contrast. Thirdly, the image was segmented with Otsu algorithm, and was then applied morphological operations. Finally, the binary image segments representing microalgal cells were labeled and counted. The results of experiments showed that this method was simple, efficient and accurate in counting microalgal cells in the microscopic image.

Realization of Car’s Frontal Projected Area Based on OpenCV

2013 ◽  
Vol 300-301 ◽  
pp. 1673-1676 ◽  
Author(s):  
Chun Bo Dong ◽  
Xing Jun Hu ◽  
Yan Wang ◽  
Li Min Fu
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Accuracy ◽  
Median Filtering ◽  
Contour Extraction ◽  
Projected Area ◽  
Automotive Body ◽  
Body Development ◽  
Image Processing Algorithms

Digital image processing technology applied to the field of automotive body development, especially the aerodynamic development of the external shape of automobile. This method and its corresponding software improve the efficiency compared with other traditional fluid software tools in processing the car’s front area, reduce artificial error, and have high accuracy. Digital image processing algorithms used in this article includes Image Gray, Median Filtering, Image Segmentation, Contour Extraction and so on.

Applied-Information Technology with Method of Digital Image Noise Processing

2014 ◽  
Vol 951 ◽  
pp. 253-256 ◽  
Author(s):  
Guo Qing Yu ◽  
Chen Meng Sui ◽  
Bing Dong Sui
Keyword(s):  
Image Processing ◽  
Information Technology ◽  
Digital Image Processing ◽  
Digital Image ◽  
Average Method ◽  
Image Noise ◽  
Computer Algorithm ◽  
Basic Knowledge ◽  
Median Filtering ◽  
Low Pass

Digital image processing is a kind of technology which employs a certain algorithm to realize image processing through computer algorithm. The powerful capacity of computing and graphics displaying function of Matlab makes image processing becomes more simple and intuitive. This paper introduces the basic knowledge of dealing with noise in digital image, expounds the neighborhood average method, median filtering in detail, and low pass filtering and other typical of eliminating noise method , and at the same time analyzes and compares the characteristics of several typical methods by use of the software Matlab.

Computer Processing of an Image

2021 ◽  
pp. 1-22
Author(s):  
Chandra Prabha R. ◽  
Shilpa Hiremath
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Color Image ◽  
Image Representation ◽  
Computer Processing ◽  
Binary Image ◽  
Grayscale Image ◽  
Under Sampling

In this chapter, the authors have briefed about images, digital images, how the digital images can be processed. Image types like binary image, grayscale image, color image, and indexed image and various image formats are explained. It highlights the various fields where digital image processing can be used. This chapter introduces a variety of concepts related to digital image formation in a human eye. The mechanism of the human visual system is discussed. The authors illustrate the steps of image processing. Explanation on different elements of digital image processing systems like image acquisition, and others are also provided. The components required for capturing and processing the image are discussed. Concepts of image sampling, quantization, image representation are discussed. It portrays the operations of the image during sampling and quantization and the two operations of sampling which is oversampling and under-sampling. Readers can appreciate the key difference between oversampling and under-sampling applied to digital images.

scholarly journals Analysis and automatic segmentation of images for lungs regions extraction in X-ray chest

2020 ◽  
pp. 18-27
Author(s):  
Joel Quintanilla-Domínguez ◽  
Juan Israel Yañez-Vargas ◽  
Miriam Butanda-Serrano ◽  
Enrique Sánchez-Torrecitas
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Morphological Operations ◽  
X Ray ◽  
Image Processing Techniques ◽  
Segmentation Of Images ◽  
Processing Techniques

One of the main disease caused by the COVID-19 in the humans is the pneumonia. This disease mainly attacks the lungs and one of the effective methods for diagnosis is through X-ray chest analysis. Due this in this work a methodology that allow the segmentation and analysis of regions that belong to the lungs in images of X-ray chest is presented. This methodology is based mainly in the implementation of some digital image processing techniques such as: contrast enhancement, segmentation, binarization and the application of morphological operations as the erosion and dilatation.

Potential application of digital image-processing method and fitted logistic model to the control of oriental fruit moths (Grapholita molesta Busck)

2016 ◽  
Vol 106 (4) ◽  
pp. 457-463 ◽  
Author(s):  
Z.G. Zhao ◽  
E.H. Rong ◽  
S.C. Li ◽  
L.J. Zhang ◽  
Z.W. Zhang ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Logistic Model ◽  
Control Period ◽  
Processing Method ◽  
Grapholita Molesta ◽  
Image Processing Method ◽  
Automatic Counting ◽  
Sex Pheromone Traps

AbstractMonitoring of oriental fruit moths (Grapholita molesta Busck) is a prerequisite for its control. This study introduced a digital image-processing method and logistic model for the control of oriental fruit moths. First, five triangular sex pheromone traps were installed separately within each area of 667 m2 in a peach orchard to monitor oriental fruit moths consecutively for 3 years. Next, full view images of oriental fruit moths were collected via a digital camera and then subjected to graying, separation and morphological analysis for automatic counting using MATLAB software. Afterwards, the results of automatic counting were used for fitting a logistic model to forecast the control threshold and key control period. There was a high consistency between automatic counting and manual counting (0.99, P < 0.05). According to the logistic model, oriental fruit moths had four occurrence peaks during a year, with a time-lag of 15–18 days between adult occurrence peak and the larval damage peak. Additionally, the key control period was from 28 June to 3 July each year, when the wormy fruit rate reached up to 5% and the trapping volume was approximately 10.2 per day per trap. Additionally, the key control period for the overwintering generation was 25 April. This study provides an automatic counting method and fitted logistic model with a great potential for application to the control of oriental fruit moths.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

A method to measure pores and fissures in geologic materials under S.E.M. by digital image processing

1978 ◽  
Vol 36 (1) ◽  
pp. 212-213
Author(s):  
L. Montoto ◽  
M. Montoto ◽  
A. Bel-Lan
Keyword(s):  
Image Processing ◽  
Optical Microscopy ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Processing ◽  
Free Surfaces ◽  
Geologic Materials ◽  
Automatic Information ◽  
Detector Output ◽  
Rock Specimens

INTRODUCTION.- The physical properties of rock masses are greatly influenced by their internal discontinuities, like pores and fissures. So, these need to be measured as a basis for interpretation. To avoid the basic difficulties of measurement under optical microscopy and analogic image systems, the authors use S.E.M. and multiband digital image processing. In S.E.M., analog signal processing has been used to further image enhancement (1), but automatic information extraction can be achieved by simple digital processing of S.E.M. images (2). The use of multiband image would overcome difficulties such as artifacts introduced by the relative positions of sample and detector or the typicals encountered in optical microscopy.DIGITAL IMAGE PROCESSING.- The studied rock specimens were in the form of flat deformation-free surfaces observed under a Phillips SEM model 500. The SEM detector output signal was recorded in picture form in b&w negatives and digitized using a Perkin Elmer 1010 MP flat microdensitometer.

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